Add tasks/version/logging_utils/beam_search to basic_correctness deps

Following the deeper audit pattern from #42219:

- `vllm/tasks.py`: imported by `vllm/model_executor/models/registry.py`
  and other model-side code transitively reached by `LLM(...)`.
- `vllm/version.py`: imported by `vllm/v1/engine/core.py` and
  `vllm/engine/arg_utils.py`.
- `vllm/logging_utils/`: imported by `vllm/model_executor/models/registry.py`
  (`logtime`) and `vllm/v1/engine/core.py`.
- `vllm/beam_search.py`: imported by `vllm/entrypoints/llm.py` (the
  `LLM` class is the entry point under test).

These are first-order imports of the engine/LLM path that
`test_basic_correctness.py`, `test_cpu_offload.py`, and
`test_cumem.py` exercise.

Signed-off-by: Kevin H. Luu <kevin@anyscale.com>

Signed-off-by: khluu <khluu000@gmail.com>

.buildkite/ci_config.yaml

@@ -8,8 +8,8 @@ run_all_patterns:
   - "CMakeLists.txt"
   - "requirements/common.txt"
   - "requirements/cuda.txt"
-  - "requirements/build.txt"
-  - "requirements/test.txt"
+  - "requirements/build/cuda.txt"
+  - "requirements/test/cuda.txt"
   - "setup.py"
   - "csrc/"
   - "cmake/"

.buildkite/ci_config_intel.yaml

@@ -0,0 +1,23 @@
+name: vllm_intel_ci
+job_dirs:
+  - ".buildkite/intel_jobs"
+run_all_patterns:
+  - "docker/Dockerfile"
+  - "CMakeLists.txt"
+  - "requirements/common.txt"
+  - "requirements/xpu.txt"
+  - "requirements/build/cuda.txt"
+  - "requirements/test/cuda.txt"
+  - "setup.py"
+  - "csrc/"
+  - "cmake/"
+run_all_exclude_patterns:
+  - "docker/Dockerfile."
+  - "csrc/cpu/"
+  - "csrc/rocm/"
+  - "cmake/hipify.py"
+  - "cmake/cpu_extension.cmake"
+registries: public.ecr.aws/q9t5s3a7
+repositories:
+  main: "vllm-ci-test-repo"
+  premerge: "vllm-ci-test-repo"

.buildkite/hardware_tests/amd.yaml

@@ -10,7 +10,7 @@ steps:
       docker build
       --build-arg max_jobs=16
       --build-arg REMOTE_VLLM=1
-      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx90a;gfx942'
+      --build-arg ARG_PYTORCH_ROCM_ARCH='gfx90a;gfx942;gfx950'
       --build-arg VLLM_BRANCH=$BUILDKITE_COMMIT
       --tag "rocm/vllm-ci:${BUILDKITE_COMMIT}"
       -f docker/Dockerfile.rocm
@@ -20,11 +20,3 @@ steps:
     - docker push "rocm/vllm-ci:${BUILDKITE_COMMIT}"
     env:
       DOCKER_BUILDKIT: "1"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 1
-        - exit_status: -10  # Agent was lost
-          limit: 1
-        - exit_status: 1  # Machine occasionally fail
-          limit: 1

.buildkite/hardware_tests/cpu.yaml

@@ -3,7 +3,6 @@ depends_on: []
 steps:
 - label: CPU-Kernel Tests
   depends_on: []
-  soft_fail: true
   device: intel_cpu
   no_plugin: true
   source_file_dependencies:
@@ -13,17 +12,35 @@ steps:
   - vllm/_custom_ops.py
   - tests/kernels/attention/test_cpu_attn.py
   - tests/kernels/moe/test_cpu_fused_moe.py
+  - tests/kernels/moe/test_cpu_fp8_fused_moe.py
   - tests/kernels/test_onednn.py
+  - tests/kernels/test_awq_int4_to_int8.py
+  - tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+      pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
+      pytest -x -v -s tests/kernels/moe/test_cpu_fp8_fused_moe.py
+      pytest -x -v -s tests/kernels/test_onednn.py
+      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py
+      pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py"
+
+- label: CPU-Compatibility Tests
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  - cmake/cpu_extension.cmake
+  - setup.py
+  - vllm/platforms/cpu.py
   commands:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
-      pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-      pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
-      pytest -x -v -s tests/kernels/test_onednn.py"
+      bash .buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh"
 
 - label: CPU-Language Generation and Pooling Model Tests
   depends_on: []
-  soft_fail: true
   device: intel_cpu
   no_plugin: true
   source_file_dependencies:
@@ -33,13 +50,12 @@ steps:
   - tests/models/language/pooling/
   commands:
     - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
       pytest -x -v -s tests/models/language/generation -m cpu_model
       pytest -x -v -s tests/models/language/pooling -m cpu_model"
 
 - label: CPU-Quantization Model Tests
   depends_on: []
-  soft_fail: true
   device: intel_cpu
   no_plugin: true
   source_file_dependencies:
@@ -49,6 +65,7 @@ steps:
   - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
   - vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py
   - vllm/model_executor/layers/quantization/kernels/mixed_precision/cpu.py
+  - vllm/model_executor/layers/fused_moe/experts/cpu_moe.py
   - tests/quantization/test_compressed_tensors.py
   - tests/quantization/test_cpu_wna16.py
   commands:
@@ -57,12 +74,11 @@ steps:
       pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs
       pytest -x -v -s tests/quantization/test_cpu_wna16.py"
       
-- label: CPU-Distributed Tests
+- label: CPU-Distributed Tests (PP+TP)
   depends_on: []
-  soft_fail: true
   device: intel_cpu
   no_plugin: true
-  source_file_dependencies:
+  source_file_dependencies: &cpu_distributed_deps
   - csrc/cpu/shm.cpp
   - vllm/v1/worker/cpu_worker.py
   - vllm/v1/worker/gpu_worker.py
@@ -71,14 +87,24 @@ steps:
   - vllm/platforms/cpu.py
   - vllm/distributed/parallel_state.py
   - vllm/distributed/device_communicators/cpu_communicator.py
+  - .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
   commands:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh"
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh tp_pp"
+
+- label: CPU-Distributed Tests (DP+TP)
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies: *cpu_distributed_deps
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh dp_tp"
 
 - label: CPU-Multi-Modal Model Tests %N
   depends_on: []
-  soft_fail: true
   device: intel_cpu
   no_plugin: true
   source_file_dependencies:
@@ -89,11 +115,11 @@ steps:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
       pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
-  parallelism: 2
+  parallelism: 3
 
 - label: "Arm CPU Test"
   depends_on: []
-  soft_fail: true
+  soft_fail: false
   device: arm_cpu
   no_plugin: true
   commands: 

.buildkite/image_build/image_build.sh

@@ -92,8 +92,8 @@ check_and_skip_if_image_exists() {
 }
 
 ecr_login() {
-    aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
-    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+    aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
+    aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
 }
 
 prepare_cache_tags() {
@@ -192,6 +192,7 @@ export BUILDKITE_COMMIT
 export PARENT_COMMIT
 export IMAGE_TAG
 export IMAGE_TAG_LATEST
+export COMMIT="${COMMIT:-${BUILDKITE_COMMIT}}"
 export CACHE_FROM
 export CACHE_FROM_BASE_BRANCH
 export CACHE_FROM_MAIN

.buildkite/image_build/image_build_cpu.sh

@@ -11,7 +11,7 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
 
 # skip build if image already exists
 if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
@@ -25,9 +25,7 @@ fi
 docker build --file docker/Dockerfile.cpu \
   --build-arg max_jobs=16 \
   --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --build-arg VLLM_CPU_AVX512BF16=true \
-  --build-arg VLLM_CPU_AVX512VNNI=true \
-  --build-arg VLLM_CPU_AMXBF16=true \
+  --build-arg VLLM_CPU_X86=true \
   --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
   --target vllm-test \
   --progress plain .

.buildkite/image_build/image_build_cpu_arm64.sh

@@ -11,10 +11,10 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
 
 # skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu) ]]; then
+if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu) ]]; then
   echo "Image not found, proceeding with build..."
 else
   echo "Image found"
@@ -25,9 +25,9 @@ fi
 docker build --file docker/Dockerfile.cpu \
   --build-arg max_jobs=16 \
   --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
   --target vllm-test \
   --progress plain .
 
 # push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu

.buildkite/image_build/image_build_torch_nightly.sh

@@ -0,0 +1,68 @@
+#!/bin/bash
+set -euo pipefail
+
+# Build a vLLM test image with PyTorch nightly installed.
+# Called by the pipeline generator's "vLLM Against PyTorch Nightly" group.
+
+if [[ $# -lt 5 ]]; then
+  echo "Usage: $0 <registry> <repo> <commit> <branch> <image_tag>"
+  exit 1
+fi
+
+REGISTRY=$1
+REPO=$2
+BUILDKITE_COMMIT=$3
+BRANCH=$4
+IMAGE_TAG=$5
+
+# --- Arguments ---
+echo "--- :mag: Arguments"
+echo "REGISTRY: ${REGISTRY}"
+echo "REPO: ${REPO}"
+echo "BUILDKITE_COMMIT: ${BUILDKITE_COMMIT}"
+echo "BRANCH: ${BRANCH}"
+echo "IMAGE_TAG: ${IMAGE_TAG}"
+
+# --- ECR login ---
+echo "--- :key: ECR login"
+aws ecr-public get-login-password --region us-east-1 \
+  | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr get-login-password --region us-east-1 \
+  | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+
+# --- Set up buildx ---
+echo "--- :docker: Setting up buildx"
+docker buildx create --name vllm-builder --driver docker-container --use || true
+docker buildx inspect --bootstrap
+docker buildx ls
+
+# --- Skip if image already exists ---
+echo "--- :mag: Checking if image already exists"
+if docker manifest inspect "$IMAGE_TAG" >/dev/null 2>&1; then
+  echo "Image found: $IMAGE_TAG — skipping build"
+  exit 0
+fi
+echo "Image not found, proceeding with build..."
+
+# --- CUDA 13.0 for nightly builds ---
+# Nightly CI uses CUDA 13.0 while regular CI stays on CUDA 12.9
+NIGHTLY_CUDA_VERSION="13.0.2"
+NIGHTLY_BUILD_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-devel-ubuntu22.04"
+NIGHTLY_FINAL_BASE_IMAGE="nvidia/cuda:${NIGHTLY_CUDA_VERSION}-base-ubuntu22.04"
+
+echo "--- :docker: Building torch nightly image (CUDA ${NIGHTLY_CUDA_VERSION})"
+docker buildx build --file docker/Dockerfile \
+  --build-arg max_jobs=16 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --build-arg USE_SCCACHE=1 \
+  --build-arg PYTORCH_NIGHTLY=1 \
+  --build-arg CUDA_VERSION="${NIGHTLY_CUDA_VERSION}" \
+  --build-arg BUILD_BASE_IMAGE="${NIGHTLY_BUILD_BASE_IMAGE}" \
+  --build-arg FINAL_BASE_IMAGE="${NIGHTLY_FINAL_BASE_IMAGE}" \
+  --build-arg torch_cuda_arch_list="8.0 8.9 9.0 10.0 12.0" \
+  --tag "$IMAGE_TAG" \
+  --push \
+  --target test \
+  --progress plain .
+
+echo "--- :white_check_mark: Torch nightly image build complete: $IMAGE_TAG"

.buildkite/image_build/image_build_xpu.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+set -e
+
+if [[ $# -lt 3 ]]; then
+  echo "Usage: $0 <registry> <repo> <commit>"
+  exit 1
+fi
+
+REGISTRY=$1
+REPO=$2
+BUILDKITE_COMMIT=$3
+
+# authenticate with AWS ECR
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+
+# skip build if image already exists
+if ! docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then
+  echo "Image not found, proceeding with build..."
+else
+  echo "Image found"
+  exit 0
+fi
+
+# build
+docker build \
+  --file docker/Dockerfile.xpu \
+  --build-arg max_jobs=16 \
+  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu \
+  --progress plain .
+
+# push
+docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu

.buildkite/intel_jobs/engine_intel.yaml

@@ -0,0 +1,21 @@
+group: Engine Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: Engine (1 GPU)
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/v1/engine/
+    - tests/v1/engine/
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py'

.buildkite/intel_jobs/kernels_intel.yaml

@@ -0,0 +1,21 @@
+group: Kernels Intel
+depends_on: 
+  - image-build-xpu
+steps:
+- label: vLLM IR Tests
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/ir
+    - vllm/kernels
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s kernels/ir'

.buildkite/intel_jobs/lora_intel.yaml

@@ -0,0 +1,135 @@
+group: LoRA Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: LoRA Runtime + Utils
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_layers.py &&
+      pytest -v -s lora/test_lora_checkpoints.py &&
+      pytest -v -s lora/test_lora_functions.py &&
+      pytest -v -s lora/test_lora_huggingface.py &&
+      pytest -v -s lora/test_lora_manager.py &&
+      pytest -v -s lora/test_lora_utils.py &&
+      pytest -v -s lora/test_peft_helper.py &&
+      pytest -v -s lora/test_resolver.py &&
+      pytest -v -s lora/test_utils.py &&
+      pytest -v -s lora/test_add_lora.py  &&
+      pytest -v -s lora/test_worker.py'
+
+- label: LoRA Fused/MoE Kernels
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_fused_moe_lora_kernel.py && 
+      pytest -v -s lora/test_moe_lora_align_sum.py'
+
+- label: LoRA Punica Kernels
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      set -o pipefail &&
+      pytest -v -s lora/test_punica_ops.py --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-3-43264-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype1-1-2049-64-128-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-1-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-1-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-8-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype0-3-2049-128-8-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-8-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype1-1-2049-256-128-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-3-64256-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-2-29696-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-3-49408-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-2-16384-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-2-51328-32-4-4]"'
+
+- label: LoRA Punica FP8/XPU Ops
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_punica_ops_fp8.py &&
+      pytest -v -s lora/test_punica_xpu_ops.py'
+
+- label: LoRA Models
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      (pytest -v -s lora/test_mixtral.py --deselect="tests/lora/test_mixtral.py::test_mixtral_lora[4]" || true) &&
+      pytest -v -s lora/test_quant_model.py --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model0]" --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model1]" --deselect="tests/lora/test_quant_model.py::test_quant_model_tp_equality[model0]" &&
+      pytest -v -s lora/test_transformers_model.py &&
+      pytest -v -s lora/test_chatglm3_tp.py &&
+      pytest -s -v lora/test_minicpmv_tp.py'
+
+- label: LoRA Multimodal
+  timeout_in_minutes: 45
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/lora
+    - tests/lora
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      pytest -v -s lora/test_default_mm_loras.py && 
+      pytest -v -s lora/test_whisper.py'

.buildkite/intel_jobs/misc_intel.yaml

@@ -0,0 +1,55 @@
+group: Miscellaneous Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: V1 Core + KV + Metrics
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/
+    - tests/v1/core
+    - tests/v1/executor
+    - tests/v1/kv_offload
+    - tests/v1/worker
+    - tests/v1/kv_connector/unit
+    - tests/v1/metrics
+    - tests/entrypoints/openai/correctness/test_lmeval.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'pip install -r requirements/kv_connectors.txt &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      cd tests &&
+      pytest -v -s v1/executor'
+
+- label: V1 Sample + Logits
+  timeout_in_minutes: 30
+  device: intel_gpu
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/
+    - tests/v1/sample
+    - tests/v1/logits_processors
+    - tests/v1/test_oracle.py
+    - tests/v1/test_request.py
+    - tests/v1/test_outputs.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      cd tests &&
+      pytest -v -s v1/logits_processors --ignore=v1/logits_processors/test_custom_online.py --ignore=v1/logits_processors/test_custom_offline.py &&
+      pytest -v -s v1/test_oracle.py &&
+      pytest -v -s v1/test_request.py &&
+      pytest -v -s v1/test_outputs.py'

.buildkite/intel_jobs/test-intel.yaml

@@ -0,0 +1,65 @@
+group: Intel
+steps:
+  - label: ":docker: Build XPU image"
+    soft_fail: true
+    depends_on: []
+    key: image-build-xpu
+    commands:
+      - bash -lc '.buildkite/image_build/image_build_xpu.sh "public.ecr.aws/q9t5s3a7" "vllm-ci-test-repo" "$BUILDKITE_COMMIT"'
+    env:
+      DOCKER_BUILDKIT: "1"
+    retry:
+      automatic:
+        - exit_status: -1  # Agent was lost
+          limit: 2
+        - exit_status: -10  # Agent was lost
+          limit: 2
+  - label: "XPU example Test"
+    depends_on:
+      - image-build-xpu
+    timeout_in_minutes: 30
+    device: intel_gpu
+    no_plugin: true
+    env:
+      REGISTRY: "public.ecr.aws/q9t5s3a7"
+      REPO: "vllm-ci-test-repo"
+    source_file_dependencies:
+      - vllm/
+      - .buildkite/intel_jobs/test-intel.yaml 
+    commands:
+      - >-
+        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+        'pip install tblib==3.1.0 &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8 &&
+        python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --kv-cache-dtype fp8 &&
+        python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --block-size 64 --enforce-eager --max-model-len 8192 &&
+        python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 &&
+        python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel'
+  - label: "XPU V1 test"
+    depends_on:
+      - image-build-xpu
+    timeout_in_minutes: 30
+    device: intel_gpu
+    no_plugin: true
+    env:
+      REGISTRY: "public.ecr.aws/q9t5s3a7"
+      REPO: "vllm-ci-test-repo"
+    source_file_dependencies:
+      - vllm/
+      - .buildkite/intel_jobs/test-intel.yaml 
+    commands:
+      - >-
+        bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+        'cd tests &&
+        pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py --ignore=v1/core/test_scheduler_e2e.py &&
+        pytest -v -s v1/engine --ignore=v1/engine/test_output_processor.py &&
+        pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py -k "not test_topk_only and not test_topp_only and not test_topk_and_topp" &&
+        pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py &&
+        pytest -v -s v1/structured_output &&
+        pytest -v -s v1/test_serial_utils.py &&
+        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
+        pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py'

.buildkite/lm-eval-harness/configs/Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

@@ -1,6 +1,9 @@
 # For hf script, without -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
 model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
+required_gpu_arch:
+  - gfx942
+  - gfx950
 tasks:
 - name: "mmlu_pro"
   metrics:

.buildkite/lm-eval-harness/configs/Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml

@@ -1,6 +1,9 @@
 # For vllm script, with -t option (tensor parallel size)
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -l 1319 -t 1
 model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
+required_gpu_arch:
+  - gfx942
+  - gfx950
 tasks:
 - name: "gsm8k"
   metrics:

.buildkite/lm-eval-harness/configs/Qwen3-235B-A22B-Instruct-2507-FP8.yaml

@@ -1,4 +1,7 @@
 model_name: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
+required_gpu_arch:
+  - gfx942
+  - gfx950
 tasks:
   - name: "mmlu_pro"
     metrics:

.buildkite/lm-eval-harness/configs/SparseLlama3.1_2of4_fp8_compressed.yaml

@@ -1,12 +0,0 @@
-# For vllm script, with -t option (tensor parallel size).
-# bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
-model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.6353
-  - name: "exact_match,flexible-extract"
-    value: 0.637
-limit: null
-num_fewshot: null 

.buildkite/lm-eval-harness/configs/models-large-rocm-fp8.txt

@@ -0,0 +1 @@
+Qwen3-235B-A22B-Instruct-2507-FP8.yaml

.buildkite/lm-eval-harness/configs/models-small-rocm.txt

@@ -1,5 +1,6 @@
 Qwen2.5-1.5B-Instruct.yaml
 Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
+Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
 Qwen1.5-MoE-W4A16-compressed-tensors.yaml

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -13,9 +13,11 @@ import os
 from contextlib import contextmanager
 
 import lm_eval
-import numpy as np
+import pytest
 import yaml
 
+from vllm.platforms import current_platform
+
 DEFAULT_RTOL = 0.08
 
 
@@ -63,6 +65,9 @@ def launch_lm_eval(eval_config, tp_size):
         "allow_deprecated_quantization=True,"
     )
 
+    if current_platform.is_rocm() and "Nemotron-3" in eval_config["model_name"]:
+        model_args += "attention_backend=TRITON_ATTN"
+
     env_vars = eval_config.get("env_vars", None)
     with scoped_env_vars(env_vars):
         results = lm_eval.simple_evaluate(
@@ -85,9 +90,40 @@ def launch_lm_eval(eval_config, tp_size):
     return results
 
 
+def _check_rocm_gpu_arch_requirement(eval_config):
+    """Skip the test if the model requires a ROCm GPU arch not present.
+
+    Model YAML configs can specify::
+
+        required_gpu_arch:
+          - gfx942
+          - gfx950
+
+    The check only applies on ROCm.  On other platforms (e.g. CUDA) the
+    field is ignored so that shared config files work for both NVIDIA and
+    AMD CI pipelines.
+    """
+    required_archs = eval_config.get("required_gpu_arch")
+    if not required_archs:
+        return
+
+    if not current_platform.is_rocm():
+        return
+
+    from vllm.platforms.rocm import _GCN_ARCH  # noqa: E402
+
+    if not any(arch in _GCN_ARCH for arch in required_archs):
+        pytest.skip(
+            f"Model requires GPU arch {required_archs}, "
+            f"but detected arch is '{_GCN_ARCH}'"
+        )
+
+
 def test_lm_eval_correctness_param(config_filename, tp_size):
     eval_config = yaml.safe_load(config_filename.read_text(encoding="utf-8"))
 
+    _check_rocm_gpu_arch_requirement(eval_config)
+
     results = launch_lm_eval(eval_config, tp_size)
 
     rtol = eval_config.get("rtol", DEFAULT_RTOL)
@@ -102,6 +138,8 @@ def test_lm_eval_correctness_param(config_filename, tp_size):
                 f"ground_truth={ground_truth:.3f} | "
                 f"measured={measured_value:.3f} | rtol={rtol}"
             )
-            success = success and np.isclose(ground_truth, measured_value, rtol=rtol)
+
+            min_acceptable = ground_truth * (1 - rtol)
+            success = success and measured_value >= min_acceptable
 
     assert success

.buildkite/performance-benchmarks/README.md

@@ -83,7 +83,6 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3-8B",
             "tensor_parallel_size": 1,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },

.buildkite/performance-benchmarks/scripts/compare-json-results.py

@@ -7,12 +7,12 @@ import argparse
 import html as _html
 import json
 import os
+from contextlib import nullcontext
 from dataclasses import dataclass
 from importlib import util
 from pathlib import Path
 
 import pandas as pd
-import regex as re
 
 pd.options.display.float_format = "{:.2f}".format
 plotly_found = util.find_spec("plotly.express") is not None
@@ -33,6 +33,45 @@ pd.set_option("display.precision", 2)
 pd.set_option("display.float_format", lambda x: f"{x:.2f}")
 
 
+# -----------------------------
+# Concurrency normalization (NEW, small)
+# -----------------------------
+def _find_concurrency_col(df: pd.DataFrame) -> str:
+    for c in [
+        "# of max concurrency.",
+        "# of max concurrency",
+        "Max Concurrency",
+        "max_concurrency",
+        "Concurrency",
+    ]:
+        if c in df.columns:
+            return c
+
+    for c in df.columns:
+        if "concurr" in str(c).lower():
+            s = df[c]
+            if s.dtype.kind in "iu" and s.nunique() > 1 and s.min() >= 1:
+                return c
+
+    raise ValueError(
+        "Cannot infer concurrency column. "
+        "Please rename the column to one of the known names "
+        "or add an explicit override (e.g., --concurrency-col)."
+    )
+
+
+def _normalize_concurrency_in_df(
+    df: pd.DataFrame, canonical: str = "# of max concurrency."
+) -> pd.DataFrame:
+    if canonical in df.columns:
+        return df
+    detected = _find_concurrency_col(df)
+    if detected in df.columns and detected != canonical:
+        return df.rename(columns={detected: canonical})
+    df[canonical] = pd.NA
+    return df
+
+
 # -----------------------------
 # Core data compare
 # -----------------------------
@@ -52,19 +91,25 @@ def compare_data_columns(
     - Concat along axis=1 (indexes align), then reset_index so callers can
       group by columns.
     - If --debug, add a <file_label>_name column per file.
+
+    Minimal fix to support different max_concurrency lists across files:
+      - normalize concurrency column naming to "# of max concurrency."
+      - align on UNION of keys (missing points become NaN)
+      - BUGFIX: don't drop throughput rows based on P99/Median presence
     """
     print("\ncompare_data_column:", data_column)
 
     frames = []
     raw_data_cols: list[str] = []
-    compare_frames = []
 
+    # Determine key cols after normalizing concurrency
     cols_per_file: list[set] = []
     for f in files:
         try:
             df_tmp = pd.read_json(f, orient="records")
         except Exception as err:
             raise ValueError(f"Failed to read {f}") from err
+        df_tmp = _normalize_concurrency_in_df(df_tmp, canonical="# of max concurrency.")
         cols_per_file.append(set(df_tmp.columns))
 
     key_cols = [c for c in info_cols if all(c in cset for cset in cols_per_file)]
@@ -75,12 +120,25 @@ def compare_data_columns(
             "No common key columns found from info_cols across the input files."
         )
 
-    meta_added = False
+    union_index = None
+    metas: list[pd.DataFrame] = []
+    staged: list[tuple[str, pd.Series, pd.Series | None]] = []
 
     for file in files:
         df = pd.read_json(file, orient="records")
+        df = _normalize_concurrency_in_df(df, canonical="# of max concurrency.")
 
-        if drop_column in df.columns:
+        # BUGFIX: only drop rows for latency-like metrics; throughput rows may have
+        # NaN in P99/Median columns even if the column exists in the JSON.
+        metric_lc = str(data_column).lower()
+        is_latency_metric = (
+            "ttft" in metric_lc
+            or "tpot" in metric_lc
+            or "p99" in metric_lc
+            or "median" in metric_lc
+            or metric_lc.strip() in {"p99", "median"}
+        )
+        if is_latency_metric and drop_column in df.columns:
             df = df.dropna(subset=[drop_column], ignore_index=True)
 
         for c in (
@@ -105,35 +163,61 @@ def compare_data_columns(
             meta = meta.groupby(level=key_cols, dropna=False).first()
 
         file_label = "/".join(file.split("/")[:-1]) or os.path.basename(file)
-        s = df_idx[data_column]
-        if not s.index.is_unique:
-            s = s.groupby(level=key_cols, dropna=False).mean()
+
+        if data_column in df_idx.columns:
+            s = df_idx[data_column]
+            if not s.index.is_unique:
+                s = s.groupby(level=key_cols, dropna=False).mean()
+        else:
+            # keep NA series to preserve meta keys for union_index
+            s = pd.Series(pd.NA, index=meta.index)
         s.name = file_label
 
-        if not meta_added:
-            frames.append(meta)
-            meta_added = True
-
+        name_s = None
         if debug and name_column in df_idx.columns:
             name_s = df_idx[name_column]
             if not name_s.index.is_unique:
                 name_s = name_s.groupby(level=key_cols, dropna=False).first()
             name_s.name = f"{file_label}_name"
-            frames.append(name_s)
 
-        frames.append(s)
+        if union_index is None:
+            union_index = meta.index
+        else:
+            union_index = union_index.union(meta.index)
+        metas.append(meta)
+
+        staged.append((file_label, s, name_s))
+
+    if union_index is None:
+        raise ValueError("No data found after loading inputs.")
+
+    # meta first (union-aligned): build UNION meta across all files
+    if metas:
+        meta_union = pd.concat(metas, axis=0)
+        # Collapse duplicates on the MultiIndex; keep first non-null per column
+        meta_union = meta_union.groupby(level=key_cols, dropna=False).first()
+        frames.append(meta_union.reindex(union_index))
+
+    # values + ratios (union-aligned)
+    metric_series_aligned: list[pd.Series] = []
+    for file_label, s, name_s in staged:
+        s_aligned = s.reindex(union_index)
+        frames.append(s_aligned)
         raw_data_cols.append(file_label)
-        compare_frames.append(s)
+        metric_series_aligned.append(s_aligned)
 
-        if len(compare_frames) >= 2:
-            base = compare_frames[0]
-            current = compare_frames[-1]
-            if "P99" in data_column or "Median" in data_column:
+        if debug and name_s is not None:
+            frames.append(name_s.reindex(union_index))
+
+        if len(metric_series_aligned) >= 2:
+            base = metric_series_aligned[0]
+            current = metric_series_aligned[-1]
+            if "P99" in str(data_column) or "Median" in str(data_column):
                 ratio = base / current
             else:
                 ratio = current / base
             ratio = ratio.mask(base == 0)
-            ratio.name = f"Ratio 1 vs {len(compare_frames)}"
+            ratio.name = f"Ratio 1 vs {len(metric_series_aligned)}"
             frames.append(ratio)
 
     concat_df = pd.concat(frames, axis=1).reset_index(drop=True)
@@ -204,24 +288,10 @@ def split_json_by_tp_pp(
 # -----------------------------
 # Styling helpers
 # -----------------------------
-def _find_concurrency_col(df: pd.DataFrame) -> str:
-    for c in [
-        "# of max concurrency.",
-        "# of max concurrency",
-        "Max Concurrency",
-        "max_concurrency",
-        "Concurrency",
-    ]:
-        if c in df.columns:
-            return c
-    for c in df.columns:
-        if df[c].dtype.kind in "iu" and df[c].nunique() > 1 and df[c].min() >= 1:
-            return c
-    return "# of max concurrency."
-
-
 def _highlight_threshold(
-    df: pd.DataFrame, threshold: float
+    df: pd.DataFrame,
+    threshold: float,
+    slack_pct: float = 0.0,
 ) -> pd.io.formats.style.Styler:
     conc_col = _find_concurrency_col(df)
     key_cols = [
@@ -234,12 +304,24 @@ def _highlight_threshold(
     ]
     conf_cols = [c for c in conf_cols if pd.api.types.is_numeric_dtype(df[c])]
 
-    return df.style.map(
-        lambda v: "background-color:#e6ffe6;font-weight:bold;"
-        if pd.notna(v) and v <= threshold
-        else "",
-        subset=conf_cols,
-    )
+    try:
+        slack_pct = float(slack_pct or 0.0)
+    except Exception:
+        slack_pct = 0.0
+    slack_limit = threshold * (1.0 + slack_pct / 100.0)
+
+    def _cell(v):
+        if pd.isna(v):
+            return ""
+        if v <= threshold:
+            # Strict SLA
+            return "background-color:#e6ffe6;font-weight:bold;"
+        if v <= slack_limit:
+            # Within slack range
+            return "background-color:#ffe5cc;font-weight:bold;"
+        return ""
+
+    return df.style.map(_cell, subset=conf_cols)
 
 
 def highlight_ratio_columns(styler: pd.io.formats.style.Styler):
@@ -286,11 +368,30 @@ def _sanitize_sheet_name(name: str) -> str:
       - max 31 chars
       - cannot contain: : \ / ? * [ ]
       - cannot be empty
+
+    NOTE: Use fast, non-regex operations here to avoid the third-party `regex`
+    module's compile overhead/edge-cases on some systems.
     """
     name = "sheet" if name is None else str(name)
-    name = re.sub(r"[:\\/?*\[\]]", "_", name)
+
+    # Replace illegal characters with underscore.
+    trans = str.maketrans(
+        {
+            ":": "_",
+            "\\": "_",
+            "/": "_",
+            "?": "_",
+            "*": "_",
+            "[": "_",
+            "]": "_",
+        }
+    )
+    name = name.translate(trans)
+
+    # Strip quotes/spaces and collapse whitespace.
     name = name.strip().strip("'")
-    name = re.sub(r"\s+", " ", name)
+    name = " ".join(name.split())
+
     if not name:
         name = "sheet"
     return name[:31]
@@ -298,30 +399,57 @@ def _sanitize_sheet_name(name: str) -> str:
 
 def _group_to_sheet_base(group_cols: list[str], gkey_tuple) -> str:
     d = dict(zip(group_cols, gkey_tuple))
-    model = d.get("Model", "model")
-    model_short = str(model).split("/")[-1]
+
+    # Always keep input/output lengths (these are important).
     ilen = d.get("Input Len", "")
     olen = d.get("Output Len", "")
     lens = f"_{ilen}x{olen}" if ilen != "" and olen != "" else ""
+
+    # Shorten model name aggressively to make room for lens.
+    model = d.get("Model", "model")
+    leaf = str(model).split("/")[-1]
+
+    max_model_len = max(1, 31 - len(lens))
+    model_short = leaf[:max_model_len]
+
     return _sanitize_sheet_name(f"{model_short}{lens}")
 
 
 def _write_tables_to_excel_sheet(
     writer: pd.ExcelWriter, sheet: str, blocks: list[tuple[str, pd.DataFrame]]
 ):
-    startrow = 0
+    """Write all blocks to a sheet with a single to_excel() call.
+
+    Pandas+openpyxl can be extremely slow when called many times per sheet.
+    We flatten blocks into one table with a 'Section' column to keep structure
+    while making Excel generation fast and deterministic.
+    """
+    if not blocks:
+        pd.DataFrame().to_excel(writer, sheet_name=sheet, index=False)
+        return
+
+    combined_parts: list[pd.DataFrame] = []
     for title, df in blocks:
-        pd.DataFrame([[title]]).to_excel(
-            writer, sheet_name=sheet, index=False, header=False, startrow=startrow
-        )
-        startrow += 1
-        df.to_excel(writer, sheet_name=sheet, index=False, startrow=startrow)
-        startrow += len(df) + 3
+        df2 = df.copy()
+        # Put the section label as the first column for readability.
+        df2.insert(0, "Section", title)
+        combined_parts.append(df2)
+
+    combined = pd.concat(combined_parts, axis=0, ignore_index=True, sort=False)
+    combined.to_excel(writer, sheet_name=sheet, index=False)
 
 
 def _safe_filename(s: str) -> str:
-    s = re.sub(r"[^\w\-.]+", "_", str(s).strip())
-    return s[:180] if len(s) > 180 else s
+    # Fast path without the third-party `regex` module.
+    s = " ".join(str(s).strip().split())
+    allowed = []
+    for ch in s:
+        if ch.isalnum() or ch in "._-":
+            allowed.append(ch)
+        else:
+            allowed.append("_")
+    out = "".join(allowed)
+    return out[:180] if len(out) > 180 else out
 
 
 # -----------------------------
@@ -428,7 +556,11 @@ def _config_value_columns(df: pd.DataFrame, conc_col: str) -> list[str]:
 
 
 def _max_concurrency_ok(
-    df: pd.DataFrame, conc_col: str, cfg_col: str, threshold: float
+    df: pd.DataFrame,
+    conc_col: str,
+    cfg_col: str,
+    threshold: float,
+    slack_pct: float = 0.0,
 ):
     if df is None or conc_col not in df.columns or cfg_col not in df.columns:
         return pd.NA
@@ -441,7 +573,14 @@ def _max_concurrency_ok(
     if d.empty:
         return pd.NA
 
-    ok = d[d[cfg_col] <= threshold]
+    # Accept values up to (1 + slack_pct%) above the SLA.
+    try:
+        slack_pct = float(slack_pct or 0.0)
+    except Exception:
+        slack_pct = 0.0
+    effective_limit = float(threshold) * (1.0 + slack_pct / 100.0)
+
+    ok = d[d[cfg_col] <= effective_limit]
     if ok.empty:
         return pd.NA
 
@@ -507,15 +646,25 @@ def build_valid_max_concurrency_summary_html(
     if not cfg_cols:
         cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
 
+    # Display SLA ranges in the table header (SLA .. SLA*(1+slack))
+    ttft_hi = args.ttft_max_ms * (1.0 + args.ttft_slack_pct / 100.0)
+    tpot_hi = args.tpot_max_ms * (1.0 + args.tpot_slack_pct / 100.0)
+    ttft_range = f"{args.ttft_max_ms:g}–{ttft_hi:g} ms (+{args.ttft_slack_pct:g}%)"
+    tpot_range = f"{args.tpot_max_ms:g}–{tpot_hi:g} ms (+{args.tpot_slack_pct:g}%)"
+
     rows = []
     for cfg in cfg_cols:
         ttft_max = (
-            _max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
+            _max_concurrency_ok(
+                ttft_group_df, conc_col, cfg, args.ttft_max_ms, args.ttft_slack_pct
+            )
             if ttft_group_df is not None
             else pd.NA
         )
         tpot_max = (
-            _max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
+            _max_concurrency_ok(
+                tpot_group_df, conc_col, cfg, args.tpot_max_ms, args.tpot_slack_pct
+            )
             if tpot_group_df is not None
             else pd.NA
         )
@@ -544,8 +693,8 @@ def build_valid_max_concurrency_summary_html(
         rows.append(
             {
                 "Configuration": cfg,
-                f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
-                f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
+                f"Max {conc_col} (TTFT ≤ {ttft_range})": ttft_max,
+                f"Max {conc_col} (TPOT ≤ {tpot_range})": tpot_max,
                 f"Max {conc_col} (Both)": both,
                 "Output Tput @ Both (tok/s)": tput_at_both,
                 "TTFT @ Both (ms)": ttft_at_both,
@@ -620,15 +769,24 @@ def build_valid_max_concurrency_summary_df(
     if not cfg_cols:
         cfg_cols = sorted(set(ttft_cols) | set(tpot_cols) | set(tput_cols), key=str)
 
+    ttft_hi = args.ttft_max_ms * (1.0 + args.ttft_slack_pct / 100.0)
+    tpot_hi = args.tpot_max_ms * (1.0 + args.tpot_slack_pct / 100.0)
+    ttft_range = f"{args.ttft_max_ms:g}–{ttft_hi:g} ms (+{args.ttft_slack_pct:g}%)"
+    tpot_range = f"{args.tpot_max_ms:g}–{tpot_hi:g} ms (+{args.tpot_slack_pct:g}%)"
+
     rows = []
     for cfg in cfg_cols:
         ttft_max = (
-            _max_concurrency_ok(ttft_group_df, conc_col, cfg, args.ttft_max_ms)
+            _max_concurrency_ok(
+                ttft_group_df, conc_col, cfg, args.ttft_max_ms, args.ttft_slack_pct
+            )
             if ttft_group_df is not None
             else pd.NA
         )
         tpot_max = (
-            _max_concurrency_ok(tpot_group_df, conc_col, cfg, args.tpot_max_ms)
+            _max_concurrency_ok(
+                tpot_group_df, conc_col, cfg, args.tpot_max_ms, args.tpot_slack_pct
+            )
             if tpot_group_df is not None
             else pd.NA
         )
@@ -657,8 +815,8 @@ def build_valid_max_concurrency_summary_df(
         rows.append(
             {
                 "Configuration": cfg,
-                f"Max {conc_col} (TTFT ≤ {args.ttft_max_ms:g} ms)": ttft_max,
-                f"Max {conc_col} (TPOT ≤ {args.tpot_max_ms:g} ms)": tpot_max,
+                f"Max {conc_col} (TTFT ≤ {ttft_range})": ttft_max,
+                f"Max {conc_col} (TPOT ≤ {tpot_range})": tpot_max,
                 f"Max {conc_col} (Both)": both,
                 "Output Tput @ Both (tok/s)": tput_at_both,
                 "TTFT @ Both (ms)": ttft_at_both,
@@ -751,7 +909,21 @@ def build_parser() -> argparse.ArgumentParser:
         help="Reference limit for TPOT plots (ms)",
     )
 
-    # ---- NEW: export options ----
+    # ---- SLA tolerance (slack) options ----
+    parser.add_argument(
+        "--ttft-slack-pct",
+        type=float,
+        default=5.0,
+        help="Allowed percentage above TTFT SLA (default: 5).",
+    )
+    parser.add_argument(
+        "--tpot-slack-pct",
+        type=float,
+        default=5.0,
+        help="Allowed percentage above TPOT SLA (default: 5).",
+    )
+
+    # ---- export options ----
     parser.add_argument(
         "--excel-out",
         type=str,
@@ -843,9 +1015,13 @@ def render_metric_table_html(
 
     metric_name = metric_label.lower()
     if "ttft" in metric_name:
-        styler = _highlight_threshold(display_group, args.ttft_max_ms)
+        styler = _highlight_threshold(
+            display_group, args.ttft_max_ms, args.ttft_slack_pct
+        )
     elif ("tpot" in metric_name) or ("median" in metric_name) or ("p99" in metric_name):
-        styler = _highlight_threshold(display_group, args.tpot_max_ms)
+        styler = _highlight_threshold(
+            display_group, args.tpot_max_ms, args.tpot_slack_pct
+        )
     else:
         styler = display_group.style
 
@@ -962,22 +1138,46 @@ def write_report_group_first(
         csv_dir.mkdir(parents=True, exist_ok=True)
 
     excel_path = args.excel_out or "perf_comparison.xlsx"
-    with pd.ExcelWriter(excel_path, engine="openpyxl") as xw:
+    disable_excel = os.getenv("VLLM_COMPARE_DISABLE_EXCEL", "0") == "1"
+
+    # Prefer xlsxwriter for speed; fallback to openpyxl if unavailable.
+    excel_engine = (
+        os.getenv("VLLM_COMPARE_EXCEL_ENGINE", "xlsxwriter").strip() or "xlsxwriter"
+    )
+    if excel_engine == "xlsxwriter" and util.find_spec("xlsxwriter") is None:
+        excel_engine = "openpyxl"
+
+    excel_engine_kwargs = {}
+    if excel_engine == "xlsxwriter":
+        # Reduce memory pressure & usually faster writes.
+        excel_engine_kwargs = {"options": {"constant_memory": True}}
+
+    xw_ctx = (
+        nullcontext(None)
+        if disable_excel
+        else pd.ExcelWriter(
+            excel_path, engine=excel_engine, engine_kwargs=excel_engine_kwargs
+        )
+    )
+    with xw_ctx as xw:
+        used_sheets: set[str] = set()
         # ---- Environment sheet (first) ----
         env_sheet = _sanitize_sheet_name("Environment")
         env_df = _load_env_df_for_inputs(args, files)
-        if env_df is None or env_df.empty:
-            pd.DataFrame(
-                [
-                    {
-                        "Section": "Environment",
-                        "Key": "vllm_env.txt",
-                        "Value": "NOT FOUND (or empty)",
-                    }
-                ]
-            ).to_excel(xw, sheet_name=env_sheet, index=False)
-        else:
-            env_df.to_excel(xw, sheet_name=env_sheet, index=False)
+        if xw is not None:
+            if env_df is None or env_df.empty:
+                pd.DataFrame(
+                    [
+                        {
+                            "Section": "Environment",
+                            "Key": "vllm_env.txt",
+                            "Value": "NOT FOUND (or empty)",
+                        }
+                    ]
+                ).to_excel(xw, sheet_name=env_sheet, index=False)
+            else:
+                env_df.to_excel(xw, sheet_name=env_sheet, index=False)
+            used_sheets.add(env_sheet)
         with open("perf_comparison.html", "w", encoding="utf-8") as main_fh:
             main_fh.write('<meta charset="utf-8">\n')
             for gkey in group_keys:
@@ -993,12 +1193,19 @@ def write_report_group_first(
 
                 main_fh.write(group_header)
 
+                do_excel = xw is not None
                 sheet = _group_to_sheet_base(group_cols_canonical, gkey_tuple)
                 sheet_base = sheet
-                dedup_i = 1
-                while sheet in xw.sheets:
-                    dedup_i += 1
-                    sheet = _sanitize_sheet_name(f"{sheet_base}_{dedup_i}")
+                if do_excel:
+                    dedup_i = 1
+                    while sheet in used_sheets:
+                        dedup_i += 1
+                        suffix = f"_{dedup_i}"
+                        # Ensure uniqueness even when sheet names are truncated.
+                        base = str(sheet_base)
+                        keep = max(1, 31 - len(suffix))
+                        sheet = _sanitize_sheet_name(base[:keep] + suffix)
+                    used_sheets.add(sheet)
 
                 excel_blocks: list[tuple[str, pd.DataFrame]] = []
 
@@ -1059,7 +1266,7 @@ def write_report_group_first(
                         )
 
                         excel_blocks.append(
-                            (metric_label, display_group.reset_index(drop=True))
+                            (metric_label, group_df.reset_index(drop=True))
                         )
                         if csv_dir:
                             fn = _safe_filename(
@@ -1067,7 +1274,7 @@ def write_report_group_first(
                                     "/", "_"
                                 )
                             )
-                            display_group.to_csv(csv_dir / f"{fn}.csv", index=False)
+                            group_df.to_csv(csv_dir / f"{fn}.csv", index=False)
 
                     summary_html = build_valid_max_concurrency_summary_html(
                         tput_group_df=tput_group_df,
@@ -1097,9 +1304,13 @@ def write_report_group_first(
                             )
                             summary_df.to_csv(csv_dir / f"{fn}.csv", index=False)
 
-                _write_tables_to_excel_sheet(xw, sheet, excel_blocks)
+                if do_excel:
+                    _write_tables_to_excel_sheet(xw, sheet, excel_blocks)
 
-    print(f"Wrote Excel: {excel_path}")
+    if disable_excel:
+        print("Skipped Excel generation (VLLM_COMPARE_DISABLE_EXCEL=1).")
+    else:
+        print(f"Wrote Excel: {excel_path}")
     if csv_dir:
         print(f"Wrote CSVs under: {csv_dir}")
 

.buildkite/performance-benchmarks/scripts/run-performance-benchmarks.sh

@@ -12,6 +12,13 @@ DRY_RUN="${DRY_RUN:-0}"
 MODEL_FILTER="${MODEL_FILTER:-}"
 DTYPE_FILTER="${DTYPE_FILTER:-}"
 
+# Adaptive search controls
+ENABLE_ADAPTIVE_CONCURRENCY="${ENABLE_ADAPTIVE_CONCURRENCY:-0}"
+SLA_TTFT_MS="${SLA_TTFT_MS:-3000}"
+SLA_TPOT_MS="${SLA_TPOT_MS:-100}"
+ADAPTIVE_MAX_PROBES="${ADAPTIVE_MAX_PROBES:-8}"
+ADAPTIVE_MAX_CONCURRENCY="${ADAPTIVE_MAX_CONCURRENCY:-1024}"
+
 check_gpus() {
   if command -v nvidia-smi; then
     # check the number of GPUs and GPU type.
@@ -183,6 +190,304 @@ upload_to_buildkite() {
   $BUILDKITE_AGENT_COMMAND artifact upload "$RESULTS_FOLDER/*"
 }
 
+# -------------------------------
+# Adaptive concurrency helpers
+# -------------------------------
+result_json_path_for_serving() {
+  local test_name=$1
+  local qps=$2
+  local max_concurrency=$3
+  echo "$RESULTS_FOLDER/${test_name}_qps_${qps}_concurrency_${max_concurrency}.json"
+}
+
+extract_metric_ms() {
+  local metric_name=$1
+  local json_file=$2
+
+  [[ -f "$json_file" ]] || return 0
+
+  if [[ "$metric_name" == "ttft" ]]; then
+    jq -r '
+      [
+        .ttft_ms.p99?,
+        .metrics.ttft_ms.p99?,
+        .ttft.p99?,
+        .metrics.ttft.p99?,
+        .p99_ttft_ms?,
+        .ttft_ms.mean?,
+        .metrics.ttft_ms.mean?,
+        .ttft.mean?,
+        .metrics.ttft.mean?,
+        .mean_ttft_ms?
+      ] | map(select(. != null)) | .[0] // empty
+    ' "$json_file"
+  else
+    jq -r '
+      [
+        .tpot_ms.p99?,
+        .metrics.tpot_ms.p99?,
+        .tpot.p99?,
+        .metrics.tpot.p99?,
+        .p99_tpot_ms?,
+        .itl_ms.p99?,
+        .metrics.itl_ms.p99?,
+        .inter_token_latency_ms.p99?,
+        .tpot_ms.mean?,
+        .metrics.tpot_ms.mean?,
+        .tpot.mean?,
+        .metrics.tpot.mean?,
+        .itl_ms.mean?,
+        .metrics.itl_ms.mean?,
+        .mean_tpot_ms?,
+        .mean_itl_ms?
+      ] | map(select(. != null)) | .[0] // empty
+    ' "$json_file"
+  fi
+}
+
+evaluate_sla_from_json() {
+  local json_file=$1
+  local ttft
+  local tpot
+  local pass
+
+  [[ -f "$json_file" ]] || return 2
+
+  ttft=$(extract_metric_ms ttft "$json_file")
+  tpot=$(extract_metric_ms tpot "$json_file")
+
+  [[ -n "$ttft" && -n "$tpot" ]] || return 2
+
+  pass=$(jq -n \
+    --argjson ttft "$ttft" \
+    --argjson tpot "$tpot" \
+    --argjson sla_ttft "$SLA_TTFT_MS" \
+    --argjson sla_tpot "$SLA_TPOT_MS" \
+    '($ttft <= $sla_ttft) and ($tpot <= $sla_tpot)')
+
+  [[ "$pass" == "true" ]]
+}
+
+write_adaptive_summary_json() {
+  local summary_file=$1
+  local test_name=$2
+  local qps=$3
+  local static_last_pass=$4
+  local static_first_fail=$5
+  local final_last_pass=$6
+  local final_first_fail=$7
+
+  jq -n \
+    --arg test_name "$test_name" \
+    --arg qps "$qps" \
+    --argjson sla_ttft "$SLA_TTFT_MS" \
+    --argjson sla_tpot "$SLA_TPOT_MS" \
+    --arg static_last_pass "${static_last_pass:-}" \
+    --arg static_first_fail "${static_first_fail:-}" \
+    --arg final_last_pass "${final_last_pass:-}" \
+    --arg final_first_fail "${final_first_fail:-}" \
+    '{
+      test_name: $test_name,
+      qps: $qps,
+      sla_ttft_ms: $sla_ttft,
+      sla_tpot_ms: $sla_tpot,
+      static_last_pass: (if $static_last_pass == "" then null else ($static_last_pass | tonumber) end),
+      static_first_fail: (if $static_first_fail == "" then null else ($static_first_fail | tonumber) end),
+      final_last_pass: (if $final_last_pass == "" then null else ($final_last_pass | tonumber) end),
+      final_first_fail: (if $final_first_fail == "" then null else ($final_first_fail | tonumber) end)
+    }' > "$summary_file"
+}
+
+run_single_serving_probe() {
+  local test_name=$1
+  local qps=$2
+  local max_concurrency=$3
+  local tp=$4
+  local compilation_config_mode=$5
+  local optimization_level=$6
+  local client_args_effective=$7
+  local client_remote_args=$8
+  local server_command=$9
+
+  local new_test_name="${test_name}_qps_${qps}_concurrency_${max_concurrency}"
+  local result_json
+  local num_prompts_arg=""
+  local client_command
+
+  result_json=$(result_json_path_for_serving "$test_name" "$qps" "$max_concurrency")
+
+  if [[ -f "$result_json" ]]; then
+    evaluate_sla_from_json "$result_json"
+    return $?
+  fi
+
+  if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
+    num_prompts=$(( max_concurrency * PROMPTS_PER_CONCURRENCY ))
+    if (( num_prompts < MIN_NUM_PROMPTS )); then num_prompts=$MIN_NUM_PROMPTS; fi
+    if (( num_prompts > MAX_NUM_PROMPTS )); then num_prompts=$MAX_NUM_PROMPTS; fi
+    num_prompts_arg="--num-prompts $num_prompts"
+  fi
+
+  client_command="vllm bench serve \
+    --save-result \
+    --result-dir $RESULTS_FOLDER \
+    --result-filename ${new_test_name}.json \
+    --request-rate $qps \
+    --max-concurrency $max_concurrency \
+    $num_prompts_arg \
+    --metadata tensor_parallel_size=$tp compilation_config.mode=$compilation_config_mode optimization_level=$optimization_level adaptive_search=1 \
+    $client_args_effective $client_remote_args "
+
+  echo "Adaptive probe: $client_command"
+
+  if [[ "${DRY_RUN:-0}" != "1" ]]; then
+    bash -c "$client_command"
+  fi
+
+  jq_output=$(jq -n \
+    --arg server "$server_command" \
+    --arg client "$client_command" \
+    --arg gpu "$gpu_type" \
+    '{
+      server_command: $server,
+      client_command: $client,
+      gpu_type: $gpu,
+      adaptive_search: true
+    }')
+  echo "$jq_output" > "$RESULTS_FOLDER/${new_test_name}.commands"
+
+  evaluate_sla_from_json "$result_json"
+}
+
+adaptive_refine_from_static_results() {
+  local test_name=$1
+  local qps=$2
+  local max_concurrency_list_raw=$3
+  local tp=$4
+  local compilation_config_mode=$5
+  local optimization_level=$6
+  local client_args_effective=$7
+  local client_remote_args=$8
+  local server_command=$9
+
+  local sorted_points
+  local point
+  local rc
+  local static_last_pass=""
+  local static_first_fail=""
+  local largest_static=""
+  local step_hint=1
+  local previous_point=""
+  local low
+  local high
+  local mid
+  local probes=0
+  local summary_file="$RESULTS_FOLDER/${test_name}_qps_${qps}_sla_summary.json"
+
+  [[ "${ENABLE_ADAPTIVE_CONCURRENCY}" == "1" ]] || return 0
+  [[ "${DRY_RUN:-0}" != "1" ]] || return 0
+
+  sorted_points=$(for point in $max_concurrency_list_raw; do printf '%s\n' "$point"; done | tr -d "'" | awk '/^[0-9]+$/' | sort -n | uniq)
+  [[ -n "$sorted_points" ]] || return 0
+
+  while read -r point; do
+    [[ -z "$point" ]] && continue
+    largest_static="$point"
+    evaluate_sla_from_json "$(result_json_path_for_serving "$test_name" "$qps" "$point")"
+    rc=$?
+    if (( rc == 0 )); then
+      static_last_pass="$point"
+    elif (( rc == 1 )); then
+      if [[ -n "$static_last_pass" ]]; then
+        static_first_fail="$point"
+        break
+      fi
+    fi
+
+    if [[ -n "$previous_point" ]]; then
+      step_hint=$(( point - previous_point ))
+      if (( step_hint < 1 )); then step_hint=1; fi
+    fi
+    previous_point="$point"
+  done <<< "$sorted_points"
+
+  if [[ -z "$static_last_pass" ]]; then
+    write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "" "$static_first_fail" "" "$static_first_fail"
+    return 0
+  fi
+
+  if [[ -n "$static_first_fail" ]]; then
+    low=$static_last_pass
+    high=$static_first_fail
+    while (( low + 1 < high )) && (( probes < ADAPTIVE_MAX_PROBES )); do
+      mid=$(( (low + high) / 2 ))
+      probes=$(( probes + 1 ))
+      run_single_serving_probe \
+        "$test_name" "$qps" "$mid" "$tp" \
+        "$compilation_config_mode" "$optimization_level" \
+        "$client_args_effective" "$client_remote_args" "$server_command"
+      rc=$?
+      if (( rc == 0 )); then
+        low=$mid
+      elif (( rc == 1 )); then
+        high=$mid
+      else
+        break
+      fi
+    done
+    write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "$static_last_pass" "$static_first_fail" "$low" "$high"
+    return 0
+  fi
+
+  low=$largest_static
+  high=""
+  while (( probes < ADAPTIVE_MAX_PROBES )); do
+    point=$(( low + step_hint ))
+    if (( point > ADAPTIVE_MAX_CONCURRENCY )); then
+      point=$ADAPTIVE_MAX_CONCURRENCY
+    fi
+    (( point > low )) || break
+    probes=$(( probes + 1 ))
+    run_single_serving_probe \
+      "$test_name" "$qps" "$point" "$tp" \
+      "$compilation_config_mode" "$optimization_level" \
+      "$client_args_effective" "$client_remote_args" "$server_command"
+    rc=$?
+    if (( rc == 0 )); then
+      low=$point
+      (( point == ADAPTIVE_MAX_CONCURRENCY )) && break
+      step_hint=$(( step_hint * 2 ))
+      if (( step_hint < 1 )); then step_hint=1; fi
+    elif (( rc == 1 )); then
+      high=$point
+      break
+    else
+      break
+    fi
+  done
+
+  if [[ -n "$high" ]]; then
+    while (( low + 1 < high )) && (( probes < ADAPTIVE_MAX_PROBES )); do
+      mid=$(( (low + high) / 2 ))
+      probes=$(( probes + 1 ))
+      run_single_serving_probe \
+        "$test_name" "$qps" "$mid" "$tp" \
+        "$compilation_config_mode" "$optimization_level" \
+        "$client_args_effective" "$client_remote_args" "$server_command"
+      rc=$?
+      if (( rc == 0 )); then
+        low=$mid
+      elif (( rc == 1 )); then
+        high=$mid
+      else
+        break
+      fi
+    done
+  fi
+
+  write_adaptive_summary_json "$summary_file" "$test_name" "$qps" "$static_last_pass" "" "$low" "$high"
+}
+
 run_benchmark_tests() {
   # run benchmark tests using `vllm bench <test_type>` command
   # $1: test type (latency or throughput)
@@ -347,10 +652,48 @@ run_serving_tests() {
     server_envs=$(echo "$params" | jq -r '.server_environment_variables')
     client_params=$(echo "$params" | jq -r '.client_parameters')
 
-    server_args=$(json2args "$server_params")
+    # vLLM serve CLI: model must be positional (no --model). Convert server_parameters accordingly.
+    server_model=$(echo "$server_params" | jq -r '.model // empty')
+    if [[ -z "$server_model" || "$server_model" == "null" ]]; then
+      echo "Error: serving test '$test_name' is missing server_parameters.model" >&2
+      exit 1
+    fi
+    server_params_no_model=$(echo "$server_params" | jq -c 'del(.model)')
+    server_args=$(json2args "$server_params_no_model")
+
     server_envs=$(json2envs "$server_envs")
     client_args=$(json2args "$client_params")
 
+    # ------------------------------------------------------------
+    # Option 1: Dynamic num-prompts scaling based on max_concurrency
+    #
+    # If PROMPTS_PER_CONCURRENCY is set, override JSON num_prompts with:
+    #   num_prompts = max_concurrency * PROMPTS_PER_CONCURRENCY
+    #
+    # If PROMPTS_PER_CONCURRENCY is NOT set, keep JSON num_prompts behavior
+    # unchanged (i.e., whatever is in serving-tests-*.json).
+    # ------------------------------------------------------------
+    PROMPTS_PER_CONCURRENCY="${PROMPTS_PER_CONCURRENCY-}"  # no default on purpose
+    MIN_NUM_PROMPTS="${MIN_NUM_PROMPTS:-1}"
+    MAX_NUM_PROMPTS="${MAX_NUM_PROMPTS:-1000000}"
+
+    if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
+      # Remove any fixed --num-prompts from JSON-derived args (avoid duplicates)
+      # Remove any fixed --num-prompts from JSON-derived args (avoid duplicates)
+      # Handles: --num-prompts 123   and   --num-prompts=123
+      client_args_no_np="$(
+        printf ' %s ' "$client_args" \
+        | sed -E \
+          -e 's/[[:space:]]--num-prompts=([^[:space:]]+)([[:space:]]|$)/ /g' \
+          -e 's/[[:space:]]--num-prompts[[:space:]]+([^[:space:]]+)([[:space:]]|$)/ /g'
+      )"
+      # normalize whitespace
+      client_args_no_np="$(echo "$client_args_no_np" | tr -s ' ' | sed -E 's/^ //; s/ $//')"
+      client_args_no_np="$(echo "$client_args_no_np" | xargs)"
+      client_args_effective="$client_args_no_np"
+    else
+      client_args_effective="$client_args"
+    fi
     # qps_list
     qps_list=$(echo "$params" | jq -r '.qps_list')
     qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
@@ -382,14 +725,13 @@ run_serving_tests() {
     fi
 
     # check if server model and client model is aligned
-    server_model=$(echo "$server_params" | jq -r '.model')
     client_model=$(echo "$client_params" | jq -r '.model')
     if [[ $server_model != "$client_model" ]]; then
       echo "Server model and client model must be the same. Skip testcase $test_name."
       continue
     fi
 
-    server_command="$server_envs vllm serve \
+    server_command="$server_envs vllm serve $server_model \
       $server_args"
 
     # run the server
@@ -436,6 +778,14 @@ run_serving_tests() {
       for max_concurrency in $max_concurrency_list; do
         new_test_name="${test_name}_qps_${qps}_concurrency_${max_concurrency}"
         echo " new test name $new_test_name"
+        # If PROMPTS_PER_CONCURRENCY is set, compute per-concurrency --num-prompts.
+        num_prompts_arg=""
+        if [[ -n "${PROMPTS_PER_CONCURRENCY}" ]]; then
+          num_prompts=$(( max_concurrency * PROMPTS_PER_CONCURRENCY ))
+          if (( num_prompts < MIN_NUM_PROMPTS )); then num_prompts=$MIN_NUM_PROMPTS; fi
+          if (( num_prompts > MAX_NUM_PROMPTS )); then num_prompts=$MAX_NUM_PROMPTS; fi
+          num_prompts_arg="--num-prompts $num_prompts"
+        fi
         # pass the tensor parallel size, the compilation mode, and the optimization
         # level to the client so that they can be used on the benchmark dashboard
         client_command="vllm bench serve \
@@ -444,8 +794,9 @@ run_serving_tests() {
           --result-filename ${new_test_name}.json \
           --request-rate $qps \
           --max-concurrency $max_concurrency \
+          $num_prompts_arg \
           --metadata tensor_parallel_size=$tp compilation_config.mode=$compilation_config_mode optimization_level=$optimization_level \
-          $client_args $client_remote_args "
+          $client_args_effective $client_remote_args "
 
         echo "Running test case $test_name with qps $qps"
         echo "Client command: $client_command"
@@ -467,6 +818,11 @@ run_serving_tests() {
         echo "$jq_output" >"$RESULTS_FOLDER/${new_test_name}.commands"
 
       done
+
+      adaptive_refine_from_static_results \
+        "$test_name" "$qps" "$max_concurrency_list" "$tp" \
+        "$compilation_config_mode" "$optimization_level" \
+        "$client_args_effective" "$client_remote_args" "$server_command"
     done
 
     # clean up
@@ -532,6 +888,7 @@ main() {
   # postprocess benchmarking results
   pip install tabulate pandas
   python3 $QUICK_BENCHMARK_ROOT/scripts/convert-results-json-to-markdown.py
+  python3 $QUICK_BENCHMARK_ROOT/scripts/compare-json-results.py -f $RESULTS_FOLDER/benchmark_results.json
 
   upload_to_buildkite
 }

.buildkite/performance-benchmarks/tests/latency-tests-hpu.json

@@ -51,5 +51,56 @@
             "max-model-len": 256,
             "async-scheduling": ""
         }
+    },
+    {
+        "test_name": "latency_deepseek_r1",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "tensor_parallel_size": 8,
+            "load_format": "dummy",
+            "max-model-len": 2048,
+            "dtype": "bfloat16"
+        }
+    },
+    {
+        "test_name": "latency_llama4_maverick_17b128e_instruct_fp8",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "tensor_parallel_size": 8,
+            "max-model-len": 512,
+            "max-num-seqs": 128,
+            "async-scheduling": "",
+            "gpu-memory-utilization": 0.95,
+            "enable_expert_parallel": ""
+        }
+    },
+    {
+        "test_name": "latency_qwen3_8b",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "Qwen/Qwen3-8B",
+            "tensor_parallel_size": 1,
+            "max-model-len": 2048,
+            "max-num-seqs": 128,
+            "dtype": "bfloat16",
+            "async-scheduling": ""
+        }
     }
 ]

.buildkite/performance-benchmarks/tests/serving-tests-arm64-cpu.json

@@ -36,6 +36,7 @@
       "model": "meta-llama/Llama-3.1-8B-Instruct",
       "backend": "vllm",
       "ignore-eos": "",
+      "temperature": 0,
       "num_prompts": 200
     }
   },
@@ -127,4 +128,4 @@
       }
     }
   ]
-}
+}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-asr.json

@@ -0,0 +1,38 @@
+{
+  "defaults": {
+    "qps_list": [
+      "inf"
+    ],
+    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
+    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
+      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120
+    },
+    "server_parameters": {
+      "dtype": "bfloat16",
+      "model": "openai/whisper-large-v3-turbo"
+    },
+    "client_parameters": {
+      "model": "openai/whisper-large-v3-turbo",
+      "backend": "openai-audio",
+      "endpoint": "/v1/audio/transcriptions",
+      "dataset_name": "hf",
+      "dataset_path": "openslr/librispeech_asr",
+      "hf_subset": "clean",
+      "hf_split": "test",
+      "no_stream": "",
+      "no_oversample": "",
+      "temperature": 0,
+      "num_prompts": 200
+    }
+  },
+  "tests": [
+    {
+      "test_name": "serving_whisper_large_v3_turbo_librispeech_clean_tp1",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {}
+    }
+  ]
+}

.buildkite/performance-benchmarks/tests/serving-tests-cpu-text.json

@@ -26,6 +26,7 @@
       "model": "meta-llama/Llama-3.1-8B-Instruct",
       "backend": "vllm",
       "ignore-eos": "",
+      "temperature": 0,
       "num_prompts": 200
     }
   },
@@ -149,6 +150,39 @@
         "random-output-len": 128
       }
     },
+    {
+      "test_name": "serving_llama8B_tp1_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
+      }
+    },
     {
       "test_name": "serving_llama8B_int4_tp1_random_128_128",
       "server_parameters": {
@@ -188,6 +222,45 @@
         "random-output-len": 128
       }
     },
+    {
+      "test_name": "serving_llama8B_int8_tp1_random_128_128",
+      "server_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int8_tp2_random_128_128",
+      "server_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int8_tp4_random_128_128",
+      "server_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
     {
       "test_name": "serving_llama3B_tp1_random_128_128",
       "server_parameters": {

.buildkite/performance-benchmarks/tests/serving-tests-cpu.json

@@ -26,6 +26,7 @@
       "model": "meta-llama/Llama-3.1-8B-Instruct",
       "backend": "vllm",
       "ignore-eos": "",
+      "temperature": 0,
       "num_prompts": 200
     }
   },
@@ -72,17 +73,6 @@
         "random-output-len": 128
       }
     },
-    {
-      "test_name": "serving_llama8B_tp4_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
     {
       "test_name": "serving_llama8B_tp1_random_128_2048",
       "server_parameters": {
@@ -105,17 +95,6 @@
         "random-output-len": 2048
       }
     },
-    {
-      "test_name": "serving_llama8B_tp4_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
     {
       "test_name": "serving_llama8B_tp1_random_2048_128",
       "server_parameters": {
@@ -139,14 +118,25 @@
       }
     },
     {
-      "test_name": "serving_llama8B_tp4_random_2048_128",
+      "test_name": "serving_llama8B_tp1_random_2048_2048",
       "server_parameters": {
-        "tensor_parallel_size": 4
+        "tensor_parallel_size": 1
       },
       "client_parameters": {
         "dataset_name": "random",
         "random-input-len": 2048,
-        "random-output-len": 128
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_2048_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 2048
       }
     }
   ]

.buildkite/performance-benchmarks/tests/serving-tests-hpu.json

@@ -10,7 +10,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy",
             "max-model-len": 2048,
@@ -22,6 +21,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -37,7 +37,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
             "tensor_parallel_size": 4,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy",
             "max-model-len": 2048,
@@ -49,6 +48,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -64,7 +64,6 @@
         "server_parameters": {
             "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
             "tensor_parallel_size": 2,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy",
             "max-model-len": 2048,
@@ -76,6 +75,88 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_deepseek_r1",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "server_parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "tensor_parallel_size": 8,
+            "disable_log_stats": "",
+            "load_format": "dummy",
+            "max-model-len": 2048,
+            "max-num-seqs": 200,
+            "async-scheduling": "",
+            "dtype": "bfloat16"
+        },
+        "client_parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama4_maverick_17b128e_instruct_fp8",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "server_parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "tensor_parallel_size": 8,
+            "disable_log_stats": "",
+            "max-model-len": 2048,
+            "max-num-seqs": 128,
+            "async-scheduling": "",
+            "enable_expert_parallel": "",
+            "max-num-batched-tokens": 4096
+        },
+        "client_parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_qwen3_8b",
+        "qps_list": [1, 4, 10, "inf"],
+        "server_environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "server_parameters": {
+            "model": "Qwen/Qwen-3-8B",
+            "tensor_parallel_size": 1,
+            "dtype": "bfloat16",
+            "disable_log_stats": "",
+            "async-scheduling": ""
+        },
+        "client_parameters": {
+            "model": "Qwen/Qwen-3-8B",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     }

.buildkite/performance-benchmarks/tests/serving-tests.json

@@ -5,7 +5,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
             "tensor_parallel_size": 1,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },
@@ -14,6 +13,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -23,7 +23,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
             "tensor_parallel_size": 4,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },
@@ -32,6 +31,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -41,7 +41,6 @@
         "server_parameters": {
             "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
             "tensor_parallel_size": 2,
-            "swap_space": 16,
             "disable_log_stats": "",
             "load_format": "dummy"
         },
@@ -50,6 +49,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200
         }
     },
@@ -59,7 +59,6 @@
         "server_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct", 
             "tensor_parallel_size": 4,
-            "swap_space": 16,
             "speculative_config": {
                 "model": "turboderp/Qwama-0.5B-Instruct",
                 "num_speculative_tokens": 4,
@@ -71,6 +70,7 @@
             "backend": "vllm",
             "dataset_name": "sharegpt",
             "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
             "num_prompts": 200 
         }
     }

.buildkite/performance-benchmarks/tests/throughput-tests-hpu.json

@@ -57,5 +57,67 @@
             "max-num-seqs": 512,
             "async-scheduling": ""
         }
+    },
+    {
+        "test_name": "throughput_deepseek_r1",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "deepseek-ai/DeepSeek-R1",
+            "tensor_parallel_size": 8,
+            "load_format": "dummy",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "dataset_name": "sharegpt",
+            "num_prompts": 1000,
+            "backend": "vllm",
+            "max-model-len": 2048,
+            "max-num-seqs": 384,
+            "async-scheduling": ""
+        }
+    },
+    {
+        "test_name": "throughput_llama4_maverick_17b128e_instruct_fp8",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            "tensor_parallel_size": 8,
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "dataset_name": "sharegpt",
+            "num_prompts": 1000,
+            "backend": "vllm",
+            "max-model-len": 2048,
+            "max-num-seqs": 512,
+            "async-scheduling": "",
+            "enable_expert_parallel": ""
+        }
+    },
+    {
+        "test_name": "throughput_qwen3_8b",
+        "environment_variables": {
+            "PT_HPU_LAZY_MODE": 1,
+            "PT_HPU_ENABLE_LAZY_COLLECTIVES": 1,
+            "VLLM_CONTIGUOUS_PA": 1,
+            "VLLM_DEFRAG": 1
+        },
+        "parameters": {
+            "model": "Qwen/Qwen-3-8B",
+            "tensor_parallel_size": 1,
+            "load_format": "dummy",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "dataset_name": "sharegpt",
+            "num_prompts": 1000,
+            "max-num-seqs": 512,
+            "backend": "vllm",
+            "async-scheduling": ""
+        }
     }
 ]

.buildkite/scripts/annotate-release.sh

@@ -11,107 +11,17 @@ fi
 buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
 To download the wheel (by commit):
 \`\`\`
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_31_aarch64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_35_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux_2_35_aarch64.whl .
 
-(Optional) For CUDA 13.0:
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu130-cp38-abi3-manylinux_2_35_aarch64.whl .
+(Optional) For CUDA 12.9:
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux_2_31_x86_64.whl .
+aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cu129-cp38-abi3-manylinux_2_31_aarch64.whl .
 
 (Optional) For CPU:
 aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl .
 aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
 \`\`\`
 
-
-To download and upload the image:
-
-\`\`\`
-# Download images:
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
-docker pull public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
-docker pull public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
-
-# Tag and push images:
-
-## CUDA
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
-docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
-docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai:latest-x86_64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu130 vllm/vllm-openai:x86_64-cu130
-docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:latest-x86_64-cu130
-docker tag vllm/vllm-openai:x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
-docker push vllm/vllm-openai:latest-x86_64-cu130
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
-docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
-docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker push vllm/vllm-openai:latest-aarch64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu130 vllm/vllm-openai:aarch64-cu130
-docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:latest-aarch64-cu130
-docker tag vllm/vllm-openai:aarch64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
-docker push vllm/vllm-openai:latest-aarch64-cu130
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
-
-## ROCm
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-docker push vllm/vllm-openai-rocm:latest
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
-docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-docker push vllm/vllm-openai-rocm:latest-base
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-
-## CPU
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:x86_64
-docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:latest-x86_64
-docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai-cpu:latest-x86_64
-docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:arm64
-docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:latest-arm64
-docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-docker push vllm/vllm-openai-cpu:latest-arm64
-docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-
-# Create multi-arch manifest:
-
-docker manifest rm vllm/vllm-openai:latest
-docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker manifest push vllm/vllm-openai:latest
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
-
-docker manifest rm vllm/vllm-openai:latest-cu130
-docker manifest create vllm/vllm-openai:latest-cu130 vllm/vllm-openai:latest-x86_64-cu130 vllm/vllm-openai:latest-aarch64-cu130
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu130 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu130
-docker manifest push vllm/vllm-openai:latest-cu130
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu130
-
-docker manifest rm vllm/vllm-openai-cpu:latest || true
-docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
-docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-docker manifest push vllm/vllm-openai-cpu:latest
-docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
-\`\`\`
+Docker images are published automatically by the "Publish release images to DockerHub" pipeline step.
 EOF

.buildkite/scripts/annotate-rocm-release.sh

@@ -5,20 +5,21 @@
 # Generate Buildkite annotation for ROCm wheel release
 set -ex
 
-# Get build configuration from meta-data
+# Extract build configuration from Dockerfile.rocm_base (single source of truth)
 # Extract ROCm version dynamically from Dockerfile.rocm_base
 # BASE_IMAGE format: rocm/dev-ubuntu-22.04:7.0-complete -> extracts "7.0"
 ROCM_VERSION=$(grep -E '^ARG BASE_IMAGE=' docker/Dockerfile.rocm_base | sed -E 's/.*:([0-9]+\.[0-9]+).*/\1/' || echo "unknown")
-PYTHON_VERSION=$(buildkite-agent meta-data get rocm-python-version 2>/dev/null || echo "3.12")
-PYTORCH_ROCM_ARCH=$(buildkite-agent meta-data get rocm-pytorch-rocm-arch 2>/dev/null || echo "gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151")
+PYTHON_VERSION=$(grep '^ARG PYTHON_VERSION=' docker/Dockerfile.rocm_base | sed 's/^ARG PYTHON_VERSION=//')
+PYTORCH_ROCM_ARCH=$(grep '^ARG PYTORCH_ROCM_ARCH=' docker/Dockerfile.rocm_base | sed 's/^ARG PYTORCH_ROCM_ARCH=//')
 
-# TODO: Enable the nightly build for ROCm
 # Get release version, default to 1.0.0.dev for nightly/per-commit builds
 RELEASE_VERSION=$(buildkite-agent meta-data get release-version 2>/dev/null || echo "")
 if [ -z "${RELEASE_VERSION}" ]; then
   RELEASE_VERSION="1.0.0.dev"
 fi
 
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+
 # S3 URLs
 S3_BUCKET="${S3_BUCKET:-vllm-wheels}"
 S3_REGION="${AWS_DEFAULT_REGION:-us-west-2}"
@@ -68,7 +69,7 @@ aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/triton
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchvision-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/torchaudio-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amdsmi-*.whl .
-aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/aiter-*.whl .
+aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/amd_aiter-*.whl .
 aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-attn-*.whl .
 \`\`\`
 
@@ -80,7 +81,7 @@ aws s3 cp s3://${S3_BUCKET}/rocm/${BUILDKITE_COMMIT}/${ROCM_VERSION_PATH}/flash-
 - **torchvision**: TorchVision for ROCm PyTorch
 - **torchaudio**: Torchaudio for ROCm PyTorch
 - **amdsmi**: AMD SMI Python bindings
-- **aiter**: Aiter for ROCm
+- **amd_aiter**: Aiter for ROCm
 - **flash-attn**: Flash Attention for ROCm
 
 ### :warning: Notes
@@ -96,7 +97,7 @@ To download and upload the image:
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base
 docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
 
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
 docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
 docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
 docker push vllm/vllm-openai-rocm:latest-base

.buildkite/scripts/cache-rocm-base-wheels.sh

@@ -15,8 +15,6 @@
 #
 # Environment variables:
 #   S3_BUCKET          - S3 bucket name (default: vllm-wheels)
-#   PYTHON_VERSION     - Python version (affects cache key)
-#   PYTORCH_ROCM_ARCH  - GPU architectures (affects cache key)
 #
 # Note: ROCm version is determined by BASE_IMAGE in Dockerfile.rocm_base,
 #       so changes to ROCm version are captured by the Dockerfile hash.
@@ -36,13 +34,7 @@ generate_cache_key() {
     fi
     local dockerfile_hash=$(sha256sum "$DOCKERFILE" | cut -c1-16)
 
-    # Include key build args that affect the output
-    # These should match the ARGs in Dockerfile.rocm_base that change the build output
-    # Note: ROCm version is determined by BASE_IMAGE in the Dockerfile, so it's captured by dockerfile_hash
-    local args_string="${PYTHON_VERSION:-}|${PYTORCH_ROCM_ARCH:-}"
-    local args_hash=$(echo "$args_string" | sha256sum | cut -c1-8)
-
-    echo "${dockerfile_hash}-${args_hash}"
+    echo "${dockerfile_hash}"
 }
 
 CACHE_KEY=$(generate_cache_key)
@@ -52,9 +44,6 @@ case "${1:-}" in
     check)
         echo "Checking cache for key: ${CACHE_KEY}" >&2
         echo "Cache path: ${CACHE_PATH}" >&2
-        echo "Variables used in cache key:" >&2
-        echo "  PYTHON_VERSION: ${PYTHON_VERSION:-<not set>}" >&2
-        echo "  PYTORCH_ROCM_ARCH: ${PYTORCH_ROCM_ARCH:-<not set>}" >&2
 
         # Check if cache exists by listing objects
         # We look for at least one .whl file
@@ -104,14 +93,16 @@ case "${1:-}" in
         echo "Cache key: ${CACHE_KEY}"
         echo "Cache path: ${CACHE_PATH}"
         echo ""
-
         mkdir -p artifacts/rocm-base-wheels
-        aws s3 cp --recursive "${CACHE_PATH}" artifacts/rocm-base-wheels/
-
+        
+        # Use sync with include/exclude to only download .whl files
+        aws s3 sync "${CACHE_PATH}" artifacts/rocm-base-wheels/ \
+            --exclude "*" \
+            --include "*.whl"
+        
         echo ""
         echo "Downloaded wheels:"
         find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' -exec ls -lh {} \;
-
         WHEEL_COUNT=$(find artifacts/rocm-base-wheels -maxdepth 1 -name '*.whl' 2>/dev/null | wc -l)
         echo ""
         echo "Total: $WHEEL_COUNT wheels"

.buildkite/scripts/check-ray-compatibility.sh

@@ -0,0 +1,235 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Check if Ray LLM can generate lock files that are compatible with this
+# version of vllm. Downloads Ray's requirement files and runs a full
+# dependency resolution with the installed vllm's constraints to see if
+# a valid lock file can be produced.
+#
+# See: https://github.com/vllm-project/vllm/issues/33599
+
+set -eo pipefail
+
+RAY_BASE_URL="https://raw.githubusercontent.com/ray-project/ray/master/python"
+
+WORK_DIR=$(mktemp -d)
+trap 'rm -rf "$WORK_DIR"' EXIT
+
+# ── Detect PyTorch index URL ─────────────────────────────────────────────
+
+if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
+    ROCM_VER=$(python3 -c "import torch; print(torch.version.hip.rsplit('.', 1)[0])")
+    CANDIDATE_URL="https://download.pytorch.org/whl/rocm${ROCM_VER}"
+    if curl -fsSL --head "${CANDIDATE_URL}/" >/dev/null 2>&1; then
+        TORCH_INDEX_URL="${CANDIDATE_URL}"
+    else
+        echo ">>> WARNING: ROCm ${ROCM_VER} wheel index not found at ${CANDIDATE_URL}"
+        echo ">>>          Falling back to default PyPI (resolution may be incomplete)"
+        TORCH_INDEX_URL=""
+    fi
+else
+    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
+fi
+echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"
+
+# Fetch all Ray requirement files used in the LLM depset pipeline
+echo ">>> Fetching Ray requirement files"
+RAY_FILES=(
+    "requirements.txt"
+    "requirements/cloud-requirements.txt"
+    "requirements/base-test-requirements.txt"
+    "requirements/llm/llm-requirements.txt"
+    "requirements/llm/llm-test-requirements.txt"
+)
+for FILE in "${RAY_FILES[@]}"; do
+    LOCAL_PATH="${WORK_DIR}/$(basename "$FILE")"
+    echo "    ${FILE}"
+    curl -fsSL -o "$LOCAL_PATH" "${RAY_BASE_URL}/${FILE}"
+done
+
+# Extract installed vllm deps
+echo ">>> Extracting installed vllm dependency constraints"
+python3 - "${WORK_DIR}/vllm-constraints.txt" <<'PYEOF'
+"""Write out the installed vllm's dependencies as pip constraint lines.
+
+Ray uses vllm[audio], so audio-extra deps are included with their extra
+markers stripped. The resolver cannot evaluate extra markers for a
+package that is not itself being resolved from an index, so we activate
+them manually here.
+"""
+import importlib.metadata
+import re
+import sys
+
+out_path = sys.argv[1]
+raw_reqs = importlib.metadata.requires("vllm") or []
+
+# Ray uses vllm[audio] – activate that extra.
+ACTIVE_EXTRAS = {"audio"}
+EXTRA_RE = re.compile(r"""extra\s*==\s*['"]([^'"]+)['"]""")
+
+lines = []
+for r in raw_reqs:
+    if ";" not in r:
+        # Unconditional dep — always include.
+        lines.append(r.strip())
+        continue
+
+    req_part, _, marker_part = r.partition(";")
+    marker_part = marker_part.strip()
+
+    extra_matches = EXTRA_RE.findall(marker_part)
+    if not extra_matches:
+        # Non-extra marker (python_version, etc.) — keep as-is.
+        lines.append(r.strip())
+        continue
+
+    if not ACTIVE_EXTRAS.intersection(extra_matches):
+        continue  # Skip inactive extras (tensorizer, bench, …).
+
+    # Strip the extra== conditions but keep any remaining markers
+    # (e.g. python_version).
+    cleaned = EXTRA_RE.sub("", marker_part)
+    cleaned = re.sub(r"\band\b\s*\band\b", "and", cleaned)
+    cleaned = re.sub(r"^\s*and\s+|\s+and\s*$", "", cleaned).strip()
+
+    if cleaned:
+        lines.append(f"{req_part.strip()} ; {cleaned}")
+    else:
+        lines.append(req_part.strip())
+
+with open(out_path, "w") as f:
+    for line in lines:
+        f.write(line + "\n")
+
+print(f"Wrote {len(lines)} constraints to {out_path}")
+PYEOF
+
+echo ">>> Installed vllm deps (first 20 lines):"
+head -20 "${WORK_DIR}/vllm-constraints.txt"
+
+# Remove Ray's vllm pin — the installed vllm's transitive deps
+# (written above) replace it in the resolution. vllm itself cannot
+# be resolved from PyPI for in-development versions, so we test
+# whether Ray's requirements can coexist with vllm's dependency
+# constraints instead.
+sed -i '/^vllm/d' "${WORK_DIR}/llm-requirements.txt"
+
+# Install uv if needed
+if ! command -v uv &>/dev/null; then
+    echo ">>> Installing uv"
+    pip install uv -q
+fi
+
+# Resolve: given vllm's constraints, can Ray compile a lock file?
+#
+# vllm's dependency constraints are the fixed side — Ray is flexible and
+# can regenerate its lock files. We pass vllm's constraints via -c so
+# the resolver treats them as non-negotiable bounds, then check whether
+# Ray's own requirements can still be satisfied within those bounds.
+echo ""
+echo "============================================================"
+echo ">>> Resolving: Can Ray generate compatible lock files?"
+echo "============================================================"
+
+EXTRA_INDEX_ARGS=()
+if [[ -n "${TORCH_INDEX_URL}" ]]; then
+    EXTRA_INDEX_ARGS+=(--extra-index-url "${TORCH_INDEX_URL}")
+fi
+
+set +e
+uv pip compile \
+    "${WORK_DIR}/requirements.txt" \
+    "${WORK_DIR}/cloud-requirements.txt" \
+    "${WORK_DIR}/base-test-requirements.txt" \
+    "${WORK_DIR}/llm-requirements.txt" \
+    "${WORK_DIR}/llm-test-requirements.txt" \
+    -c "${WORK_DIR}/vllm-constraints.txt" \
+    --python-version 3.12 \
+    --python-platform x86_64-manylinux_2_31 \
+    "${EXTRA_INDEX_ARGS[@]}" \
+    --index-strategy unsafe-best-match \
+    --unsafe-package setuptools \
+    --unsafe-package ray \
+    --no-header \
+    -o "${WORK_DIR}/resolved.txt" \
+    2>&1
+EXIT_CODE=$?
+set -e
+
+echo ""
+echo "=========================================="
+if [ $EXIT_CODE -eq 0 ]; then
+    echo "SUCCESS: Ray can generate lock files compatible with this vllm."
+    echo ""
+    echo "Key resolved versions:"
+    grep -E '^(protobuf|torch|numpy|transformers)==' \
+        "${WORK_DIR}/resolved.txt" | sort || true
+    echo "=========================================="
+    exit 0
+fi
+
+echo "FAILURE: Ray cannot generate lock files compatible with this vllm."
+echo "This means a fundamental dependency conflict exists that Ray"
+echo "cannot resolve by regenerating its lock files."
+echo "See: https://github.com/vllm-project/vllm/issues/33599"
+echo "=========================================="
+
+# Buildkite annotation
+if [ -f /usr/bin/buildkite-agent ]; then
+    buildkite-agent annotate --style 'warning' --context 'ray-compat' << EOF
+### :warning: Ray Dependency Compatibility Warning
+This PR introduces dependencies that **cannot** be resolved with Ray's requirements.
+Ray would not be able to regenerate its lock files to accommodate this vllm version.
+
+Please check the **Ray Dependency Compatibility Check** step logs for details.
+See [issue #33599](https://github.com/vllm-project/vllm/issues/33599) for context.
+EOF
+fi
+
+# Notify Slack if webhook is configured and PR/branch are valid.
+if [ -n "$RAY_COMPAT_SLACK_WEBHOOK_URL" ]; then
+    PR="${BUILDKITE_PULL_REQUEST:-}"
+    BRANCH="${BUILDKITE_BRANCH:-}"
+
+    # Skip notification if PR is invalid or branch is empty
+    if [[ "$PR" = "false" || -z "$PR" || -z "$BRANCH" ]]; then
+        echo ">>> Skipping Slack notification (invalid PR or empty branch: PR=$PR, branch=$BRANCH)"
+    else
+        echo ">>> Sending Slack notification"
+        # Single quotes are intentional: the f-string expressions are Python, not shell.
+        # shellcheck disable=SC2016
+        PAYLOAD=$(python3 -c '
+import json, os, sys
+pr = os.getenv("BUILDKITE_PULL_REQUEST", "N/A")
+branch = os.getenv("BUILDKITE_BRANCH", "unknown")
+url = os.getenv("BUILDKITE_BUILD_URL", "#")
+data = {
+    "text": ":warning: Ray Dependency Compatibility Check Failed",
+    "blocks": [{
+        "type": "section",
+        "text": {
+            "type": "mrkdwn",
+            "text": (
+                "*:warning: Ray Dependency Compatibility Check Failed*\n"
+                f"PR #{pr} on branch `{branch}` introduces dependencies "
+                f"that cannot be resolved with Ray'\''s requirements.\n"
+                f"<{url}|View Build>"
+            ),
+        },
+    }],
+}
+print(json.dumps(data))
+')
+
+        HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" -X POST "$RAY_COMPAT_SLACK_WEBHOOK_URL" \
+            -H 'Content-type: application/json' \
+            -d "$PAYLOAD")
+        echo "    Slack webhook response: $HTTP_CODE"
+    fi
+else
+    echo ">>> Skipping Slack notification (RAY_COMPAT_SLACK_WEBHOOK_URL not set)"
+fi
+
+exit 1

.buildkite/scripts/ci-fetch-log.sh

@@ -0,0 +1,55 @@
+#!/bin/bash
+# Usage: ./ci-fetch-log.sh <buildkite_job_url> [output_file]
+#        ./ci-fetch-log.sh <build_number> <job_uuid> [output_file]
+#
+# Downloads the raw log for a Buildkite job from the public, unauthenticated
+# /organizations/<org>/pipelines/<pipeline>/builds/<n>/jobs/<uuid>/download
+# endpoint, then strips ANSI/timestamps via ci-clean-log.sh.
+#
+# Find <build_number> and <job_uuid> via:
+#   gh pr checks <PR> --repo vllm-project/vllm
+# Each failing row's URL is .../builds/<build_number>#<job_uuid>.
+
+set -euo pipefail
+
+ORG="vllm"
+PIPELINE="ci"
+
+usage() {
+    echo "Usage: $0 <buildkite_job_url> [output_file]"
+    echo "       $0 <build_number> <job_uuid> [output_file]"
+    exit 1
+}
+
+if [ $# -lt 1 ]; then usage; fi
+
+if [[ "$1" == https://* ]]; then
+    BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
+    JOB=$(echo "$1" | grep -oE '[0-9a-f]{8}-[0-9a-f-]+' | head -n 1)
+    OUT="${2:-ci-${BUILD}-${JOB:0:8}.log}"
+else
+    if [ $# -lt 2 ]; then usage; fi
+    BUILD="$1"
+    JOB="$2"
+    OUT="${3:-ci-${BUILD}-${JOB:0:8}.log}"
+fi
+
+if [ -z "$BUILD" ] || [ -z "$JOB" ]; then
+    echo "Could not parse build number or job UUID from: $1" >&2
+    usage
+fi
+
+COOKIES=$(mktemp)
+trap 'rm -f "$COOKIES"' EXIT
+
+# Buildkite issues a session cookie on first hit; subsequent /download needs it.
+curl -fsSL -c "$COOKIES" -A "vllm-ci-fetch-log" \
+    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}" -o /dev/null
+
+curl -fsSL -b "$COOKIES" -A "vllm-ci-fetch-log" \
+    "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/${JOB}/download" \
+    -o "$OUT"
+
+bash "$(dirname "$0")/ci-clean-log.sh" "$OUT"
+
+echo "$OUT"

.buildkite/scripts/cleanup-nightly-builds.sh

@@ -4,16 +4,19 @@ set -ex
 
 # Clean up old nightly builds from DockerHub, keeping only the last 14 builds
 # This script uses DockerHub API to list and delete old tags with specified prefix
-# Usage: cleanup-nightly-builds.sh [TAG_PREFIX]
-# Example: cleanup-nightly-builds.sh "nightly-" or cleanup-nightly-builds.sh "cu130-nightly-"
+# Usage: cleanup-nightly-builds.sh [TAG_PREFIX] [REPO]
+# Example: cleanup-nightly-builds.sh "nightly-"
+# Example: cleanup-nightly-builds.sh "cu130-nightly-"
+# Example: cleanup-nightly-builds.sh "nightly-" "vllm/vllm-openai-rocm"
 
-# Get tag prefix from argument, default to "nightly-" if not provided
+# Get tag prefix and repo from arguments
 TAG_PREFIX="${1:-nightly-}"
+REPO="${2:-vllm/vllm-openai}"
 
-echo "Cleaning up tags with prefix: $TAG_PREFIX"
+echo "Cleaning up tags with prefix: $TAG_PREFIX in repository: $REPO"
 
-# DockerHub API endpoint for vllm/vllm-openai repository
-REPO_API_URL="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags"
+# DockerHub API endpoint for the repository
+REPO_API_URL="https://hub.docker.com/v2/repositories/${REPO}/tags"
 
 # Get DockerHub credentials from environment
 if [ -z "$DOCKERHUB_TOKEN" ]; then
@@ -70,7 +73,7 @@ delete_tag() {
     local tag_name="$1"
     echo "Deleting tag: $tag_name"
     
-    local delete_url="https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags/$tag_name"
+    local delete_url="https://hub.docker.com/v2/repositories/${REPO}/tags/$tag_name"
     set +x
     local response=$(curl -s -X DELETE -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
     set -x

.buildkite/scripts/detect-manylinux-tag.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Detect the manylinux platform tag for a wheel and rename it in place.
+
+vLLM's build images produce wheels with the generic ``linux_<arch>`` platform
+tag, which installers like ``pip`` won't accept off PyPI/our index. We need to
+rewrite the platform tag to the appropriate ``manylinux_<major>_<minor>_<arch>``
+before uploading.
+
+Historically the tag was hard-coded per build (``manylinux_2_31`` for the
+Ubuntu 20.04-based image, ``manylinux_2_35`` for the Ubuntu 22.04-based
+images). That is brittle: bumping the base image silently produces wheels
+labelled with the wrong glibc requirement. This script asks ``auditwheel``
+to derive the tag from the symbol versions actually referenced by the
+binaries inside the wheel, so the label tracks reality.
+
+We can't simply call ``auditwheel repair`` -- it tries to graft external
+shared libraries into the wheel and fails on vLLM's CUDA/cuBLAS dependencies.
+Instead we use ``auditwheel.wheel_abi.analyze_wheel_abi`` directly, which is
+the same call that powers ``auditwheel show``, and read off
+``winfo.sym_policy.name``.
+
+Usage:
+    detect-manylinux-tag.py <wheel_path>
+
+The wheel is renamed in place; the new path is printed on stdout. All
+diagnostics go to stderr so callers can capture stdout safely.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+from auditwheel.error import (
+    AuditwheelError,
+    NonPlatformWheelError,
+    WheelToolsError,
+)
+from auditwheel.wheel_abi import analyze_wheel_abi
+from auditwheel.wheeltools import get_wheel_architecture, get_wheel_libc
+
+
+def detect_platform_tag(wheel_path: Path) -> str:
+    """Return the most precise platform tag the wheel is consistent with.
+
+    Mirrors ``auditwheel show`` but returns ``sym_policy`` rather than
+    ``overall_policy``: we only care about the glibc symbol versions used,
+    not about other policy axes (ISA extensions, blacklist, etc.) that
+    ``overall_policy`` folds in.
+    """
+    fn = wheel_path.name
+
+    try:
+        arch = get_wheel_architecture(fn)
+    except (WheelToolsError, NonPlatformWheelError):
+        # Architecture isn't deducible from the filename; let auditwheel
+        # infer it from the ELF binaries inside the wheel.
+        arch = None
+
+    try:
+        libc = get_wheel_libc(fn)
+    except WheelToolsError:
+        # An unrepaired wheel uses ``linux_<arch>``, which doesn't encode
+        # libc. Let auditwheel infer it from the ELF binaries.
+        libc = None
+
+    winfo = analyze_wheel_abi(
+        libc,
+        arch,
+        wheel_path,
+        frozenset(),
+        disable_isa_ext_check=False,
+        allow_graft=False,
+    )
+    return winfo.sym_policy.name
+
+
+def rename_wheel(wheel_path: Path, new_platform_tag: str) -> Path:
+    """Rename the wheel in place, replacing only its platform tag."""
+    # Wheel filename per PEP 427:
+    #   {distribution}-{version}(-{build})?-{python}-{abi}-{platform}.whl
+    # The platform tag is always the last ``-``-separated token before
+    # ``.whl``. Compound tags like ``manylinux_2_31_x86_64`` use ``_`` as the
+    # internal separator, so ``-``-splitting is unambiguous.
+    parts = wheel_path.stem.split("-")
+    if len(parts) < 5:
+        raise ValueError(f"Unrecognised wheel filename: {wheel_path.name}")
+    parts[-1] = new_platform_tag
+    new_path = wheel_path.with_name("-".join(parts) + ".whl")
+    if new_path != wheel_path:
+        wheel_path.rename(new_path)
+    return new_path
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description="Detect a wheel's manylinux platform tag with "
+        "auditwheel and rename the wheel in place."
+    )
+    parser.add_argument(
+        "wheel",
+        type=Path,
+        help="Path to the wheel to inspect and rename.",
+    )
+    args = parser.parse_args()
+
+    wheel_path: Path = args.wheel
+    if not wheel_path.is_file():
+        print(f"error: {wheel_path} is not a file", file=sys.stderr)
+        return 1
+
+    # Catch the things that ``analyze_wheel_abi`` and ``rename_wheel`` can
+    # raise: any subclass of ``AuditwheelError`` (pure-Python wheels,
+    # invalid libc, malformed wheels), filesystem errors, or our own
+    # ``ValueError`` for an unrecognised wheel filename. Print a single
+    # ``ERROR_TYPE: message`` line to stderr instead of a Python
+    # traceback, which is much friendlier in CI logs.
+    try:
+        new_tag = detect_platform_tag(wheel_path)
+        print(f"detected platform tag: {new_tag}", file=sys.stderr)
+        new_path = rename_wheel(wheel_path, new_tag)
+    except (AuditwheelError, ValueError, OSError) as e:
+        print(
+            f"error: failed to retag {wheel_path.name}: {type(e).__name__}: {e}",
+            file=sys.stderr,
+        )
+        return 2
+
+    if new_path != wheel_path:
+        print(f"renamed {wheel_path.name} -> {new_path.name}", file=sys.stderr)
+    else:
+        print(f"wheel already tagged {new_tag}", file=sys.stderr)
+
+    print(new_path)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

.buildkite/scripts/docker-build-metadata-args.sh

@@ -0,0 +1,54 @@
+#!/bin/bash
+# Emit docker build flags for release image provenance metadata.
+# Keep this helper best-effort: missing Buildkite metadata should fall back to
+# local/default values instead of blocking the Docker build.
+
+# Variant examples: "", "cu129", "ubuntu2404", "cu129-ubuntu2404".
+variant="${1:-}"
+variant_suffix="${variant:+-${variant}}"
+
+image_name="${VLLM_DOCKER_IMAGE_NAME:-vllm/vllm-openai}"
+staging_repo="${VLLM_STAGING_IMAGE_REPO:-public.ecr.aws/q9t5s3a7/vllm-release-repo}"
+build_commit="${VLLM_BUILD_COMMIT:-${BUILDKITE_COMMIT:-unknown}}"
+build_pipeline="${VLLM_BUILD_PIPELINE:-${BUILDKITE_PIPELINE_ID:-${BUILDKITE_PIPELINE_SLUG:-local}}}"
+build_url="${VLLM_BUILD_URL:-${BUILDKITE_BUILD_URL:-}}"
+tag_commit="${BUILDKITE_COMMIT:-${build_commit}}"
+
+if [[ -n "${BUILDKITE:-}" || -n "${BUILDKITE_COMMIT:-}" ]]; then
+  release_version="${RELEASE_VERSION:-}"
+  if command -v buildkite-agent >/dev/null 2>&1; then
+    release_version="${release_version:-$(buildkite-agent meta-data get release-version 2>/dev/null)}"
+  fi
+  release_version="${release_version#v}"
+  release_version="${release_version:-${tag_commit}}"
+
+  staging_image_ref="${staging_repo}:${tag_commit}-$(uname -m)${variant_suffix}"
+
+  if [[ "${NIGHTLY:-}" == "1" ]]; then
+    if [[ -z "${variant}" ]]; then
+      image_tag="${image_name}:nightly-${tag_commit}"
+    elif [[ "${variant}" == cu* ]]; then
+      cuda_variant="${variant%%-*}"
+      remaining_variant="${variant#${cuda_variant}}"
+      image_tag="${image_name}:${cuda_variant}-nightly-${tag_commit}${remaining_variant}"
+    else
+      image_tag="${image_name}:nightly-${tag_commit}${variant_suffix}"
+    fi
+  else
+    image_tag="${image_name}:v${release_version}${variant_suffix}"
+  fi
+else
+  image_tag="${VLLM_IMAGE_TAG:-local/vllm-openai:dev}"
+  staging_image_ref="${image_tag}"
+fi
+
+emit_arg() {
+  printf -- "--build-arg %s=%s " "$1" "$2"
+}
+
+emit_arg VLLM_BUILD_COMMIT "${build_commit}"
+emit_arg VLLM_BUILD_PIPELINE "${build_pipeline}"
+emit_arg VLLM_BUILD_URL "${build_url}"
+# This is the intended public tag. The final digest is only known after push.
+emit_arg VLLM_IMAGE_TAG "${image_tag}"
+printf -- "--tag %s " "${staging_image_ref}"

.buildkite/scripts/generate-and-upload-nightly-index.sh

@@ -0,0 +1,77 @@
+#!/usr/bin/env bash
+
+set -ex
+
+# Generate and upload wheel indices for all wheels in the commit directory.
+# This script should run once after all wheels have been built and uploaded.
+
+# ======== setup ========
+
+BUCKET="vllm-wheels"
+INDICES_OUTPUT_DIR="indices"
+DEFAULT_VARIANT_ALIAS="cu130" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
+SUBPATH=$BUILDKITE_COMMIT
+S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
+
+# Select python3 (>= 3.12) -- local if available, else a docker fallback.
+# shellcheck source=lib/select-python.sh
+source .buildkite/scripts/lib/select-python.sh
+select_python
+
+# ======== generate and upload indices ========
+
+# list all wheels in the commit directory
+echo "Existing wheels on S3:"
+aws s3 ls "$S3_COMMIT_PREFIX"
+obj_json="objects.json"
+aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$SUBPATH/" --delimiter / --output json > "$obj_json"
+mkdir -p "$INDICES_OUTPUT_DIR"
+
+# call script to generate indices for all existing wheels
+# these indices have relative paths that work as long as they are next to the wheel directory in s3
+# i.e., the wheels are always in s3://vllm-wheels/<commit>/
+# and indices can be placed in /<commit>/, or /nightly/, or /<version>/
+alias_args=()
+if [[ -n "$DEFAULT_VARIANT_ALIAS" ]]; then
+    alias_args=(--alias-to-default "$DEFAULT_VARIANT_ALIAS")
+fi
+
+# HACK: we do not need regex module here, but it is required by pre-commit hook
+# To avoid any external dependency, we simply replace it back to the stdlib re module
+sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
+$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" "${alias_args[@]}"
+
+# copy indices to /<commit>/ unconditionally
+echo "Uploading indices to $S3_COMMIT_PREFIX"
+aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
+
+# copy to /nightly/ only if it is on the main branch and not a PR
+if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
+    echo "Uploading indices to overwrite /nightly/"
+    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/nightly/"
+fi
+
+# detect version from any wheel in the commit directory
+# download the first wheel we find to extract version metadata
+first_wheel_key=$($PYTHON -c "import json; obj=json.load(open('$obj_json')); print(next((c['Key'] for c in obj.get('Contents', []) if c['Key'].endswith('.whl')), ''))")
+if [[ -z "$first_wheel_key" ]]; then
+    echo "Error: No wheels found in $S3_COMMIT_PREFIX"
+    exit 1
+fi
+first_wheel=$(basename "$first_wheel_key")
+aws s3 cp "s3://$BUCKET/${first_wheel_key}" "/tmp/${first_wheel}"
+version=$(unzip -p "/tmp/${first_wheel}" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
+rm -f "/tmp/${first_wheel}"
+echo "Version in wheel: $version"
+pure_version="${version%%+*}"
+echo "Pure version (without variant): $pure_version"
+
+# re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
+if [[ "$version" != *"dev"* ]]; then
+    echo "Re-generating indices for /$pure_version/"
+    rm -rf "${INDICES_OUTPUT_DIR:?}"
+    mkdir -p "$INDICES_OUTPUT_DIR"
+    # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
+    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" "${alias_args[@]}"
+    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
+fi

.buildkite/scripts/hardware_ci/run-amd-test.sh

@@ -1,25 +1,40 @@
 #!/bin/bash
 
-# This script runs test inside the corresponding ROCm docker container.
+# This script runs tests inside the corresponding ROCm docker container.
+# It handles both single-node and multi-node test configurations.
+#
+# Multi-node detection: Instead of matching on fragile group names, we detect
+# multi-node jobs structurally by looking for the bracket command syntax
+# "[node0_cmds] && [node1_cmds]" or via the NUM_NODES environment variable.
+#
+###############################################################################
+# QUOTING / COMMAND PASSING
+#
+# Passing commands as positional arguments ($*) is fragile when the command
+# string itself contains double quotes, e.g.:
+#
+#   bash run-amd-test.sh "export FLAGS="value" && pytest -m "not slow""
+#
+# The outer shell resolves the nested quotes *before* this script runs, so
+# the script receives mangled input it cannot fully recover.
+#
+# Preferred: pass commands via the VLLM_TEST_COMMANDS environment variable:
+#
+#   export VLLM_TEST_COMMANDS='export FLAGS="value" && pytest -m "not slow"'
+#   bash run-amd-test.sh
+#
+# Single-quoted assignment preserves all inner double quotes verbatim.
+# The $* path is kept for backward compatibility but callers should migrate.
+###############################################################################
 set -o pipefail
 
 # Export Python path
 export PYTHONPATH=".."
 
-# Print ROCm version
-echo "--- Confirming Clean Initial State"
-while true; do
-        sleep 3
-        if grep -q clean /opt/amdgpu/etc/gpu_state; then
-                echo "GPUs state is \"clean\""
-                break
-        fi
-done
+###############################################################################
+# Helper Functions
+###############################################################################
 
-echo "--- ROCm info"
-rocminfo
-
-# cleanup older docker images
 cleanup_docker() {
   # Get Docker's root directory
   docker_root=$(docker info -f '{{.DockerRootDir}}')
@@ -28,15 +43,12 @@ cleanup_docker() {
     exit 1
   fi
   echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
+
   disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
   threshold=70
   if [ "$disk_usage" -gt "$threshold" ]; then
     echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
     docker image prune -f
-    # Remove unused volumes / force the system prune for old images as well.
     docker volume prune -f && docker system prune --force --filter "until=72h" --all
     echo "Docker images and volumes cleanup completed."
   else
@@ -45,193 +57,450 @@ cleanup_docker() {
 }
 
 cleanup_network() {
-  for node in $(seq 0 $((NUM_NODES-1))); do
-    if docker pr -a -q -f name="node${node}" | grep -q .; then
-      docker stop "node${node}"
+  local max_nodes=${NUM_NODES:-2}
+  for node in $(seq 0 $((max_nodes - 1))); do
+    if docker ps -a -q -f name="node${node}" | grep -q .; then
+      docker stop "node${node}" || true
     fi
   done
-  if docker network ls | grep docker-net; then
-    docker network rm docker-net
+  if docker network ls | grep -q docker-net; then
+    docker network rm docker-net || true
   fi
 }
 
-# Call the cleanup docker function
+is_multi_node() {
+  local cmds="$1"
+  # Primary signal: NUM_NODES environment variable set by the pipeline
+  if [[ "${NUM_NODES:-1}" -gt 1 ]]; then
+    return 0
+  fi
+  # Fallback: detect the bracket syntax structurally
+  # Pattern: [...] && [...] (per-node command arrays)
+  if [[ "$cmds" =~ \[.*\].*\&\&.*\[.*\] ]]; then
+    return 0
+  fi
+  return 1
+}
+
+handle_pytest_exit() {
+  local exit_code=$1
+  if [ "$exit_code" -eq 5 ]; then
+    echo "Pytest exit code 5 (no tests collected) - treating as success."
+    exit 0
+  fi
+  exit "$exit_code"
+}
+
+###############################################################################
+# Pytest marker/keyword re-quoting
+#
+# When commands are passed through Buildkite -> shell -> $* -> bash -c,
+# quotes around multi-word pytest -m/-k expressions get stripped:
+#   pytest -v -s -m 'not cpu_test' v1/core
+# becomes:
+#   pytest -v -s -m not cpu_test v1/core
+#
+# pytest then interprets "cpu_test" as a file path, not part of the marker.
+#
+# This function detects unquoted expressions after -m/-k and re-quotes them
+# by collecting tokens until a recognizable boundary is reached:
+#   - test path (contains '/')
+#   - test file (ends with '.py')
+#   - another pytest flag (--xxx or -x single-char flags)
+#   - command separator (&& || ; |)
+#   - environment variable assignment (FOO=bar)
+#
+# Single-word markers (e.g. -m cpu_test, -m hybrid_model) pass through
+# unquoted since they have no spaces and work fine.
+#
+# Already-quoted expressions (containing literal single quotes) are passed
+# through untouched to avoid double-quoting values injected by
+# apply_rocm_test_overrides.
+#
+# NOTE: This ONLY fixes -m/-k flags. It cannot recover arbitrary inner
+# double-quotes stripped by the calling shell (see header comment).
+# Use VLLM_TEST_COMMANDS to avoid the problem entirely.
+###############################################################################
+re_quote_pytest_markers() {
+  local input="$1"
+  local output=""
+  local collecting=false
+  local marker_buf=""
+
+  # Strip backslash-newline continuations, then flatten remaining newlines
+  local flat="${input//$'\\\n'/ }"
+  flat="${flat//$'\n'/ }"
+
+  # Disable globbing to prevent *.py etc. from expanding during read -ra
+  local restore_glob
+  restore_glob="$(shopt -p -o noglob 2>/dev/null || true)"
+  set -o noglob
+  local -a words
+  read -ra words <<< "$flat"
+  eval "$restore_glob"
+
+  for word in "${words[@]}"; do
+    if $collecting; then
+      # If the token we're about to collect already contains a literal
+      # single quote, the expression was already quoted upstream.
+      # Flush and stop collecting.
+      if [[ "$word" == *"'"* ]]; then
+        if [[ -n "$marker_buf" ]]; then
+          # Should not normally happen (partial buf + quote), flush raw
+          output+="${marker_buf} "
+          marker_buf=""
+        fi
+        output+="${word} "
+        collecting=false
+        continue
+      fi
+
+      local is_boundary=false
+      case "$word" in
+        # Line-continuation artifact
+        "\\")
+          is_boundary=true ;;
+        # Command separators
+        "&&"|"||"|";"|"|")
+          is_boundary=true ;;
+        # Long flags (--ignore, --shard-id, etc.)
+        --*)
+          is_boundary=true ;;
+        # Short flags (-v, -s, -x, etc.) but NOT negative marker tokens
+        # like "not" which don't start with "-". Also skip -k/-m which
+        # would start a new marker (handled below).
+        -[a-zA-Z])
+          is_boundary=true ;;
+        # Test path (contains /)
+        */*)
+          is_boundary=true ;;
+        # Test file (ends with .py, possibly with ::method)
+        *.py|*.py::*)
+          is_boundary=true ;;
+        # Environment variable assignment preceding a command (FOO=bar)
+        *=*)
+          # Only treat as boundary if it looks like VAR=value, not
+          # pytest filter expressions like num_gpus=2 inside markers
+          if [[ "$word" =~ ^[A-Z_][A-Z0-9_]*= ]]; then
+            is_boundary=true
+          fi
+          ;;
+      esac
+
+      if $is_boundary; then
+        # Strip surrounding double quotes if present (from upstream
+        # single-to-double conversion); without this, wrapping below
+        # would produce '"expr"' with literal double-quote characters.
+        if [[ "$marker_buf" == '"'*'"' ]]; then
+          marker_buf="${marker_buf#\"}"
+          marker_buf="${marker_buf%\"}"
+        fi
+        # Flush the collected marker expression
+        if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+          output+="'${marker_buf}' "
+        else
+          output+="${marker_buf} "
+        fi
+        collecting=false
+        marker_buf=""
+        # Check if this boundary word itself starts a new -m/-k
+        if [[ "$word" == "-m" || "$word" == "-k" ]]; then
+          output+="${word} "
+          collecting=true
+        # Drop stray backslash tokens silently
+        elif [[ "$word" == "\\" ]]; then
+          :
+        else
+          output+="${word} "
+        fi
+      else
+        # Accumulate into marker buffer
+        if [[ -n "$marker_buf" ]]; then
+          marker_buf+=" ${word}"
+        else
+          marker_buf="${word}"
+        fi
+      fi
+    elif [[ "$word" == "-m" || "$word" == "-k" ]]; then
+      output+="${word} "
+      collecting=true
+      marker_buf=""
+    else
+      output+="${word} "
+    fi
+  done
+
+  # Flush any trailing marker expression (marker at end of command)
+  if $collecting && [[ -n "$marker_buf" ]]; then
+    # Strip surrounding double quotes (see mid-stream flush comment)
+    if [[ "$marker_buf" == '"'*'"' ]]; then
+      marker_buf="${marker_buf#\"}"
+      marker_buf="${marker_buf%\"}"
+    fi
+    if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+      output+="'${marker_buf}'"
+    else
+      output+="${marker_buf}"
+    fi
+  fi
+
+  echo "${output% }"
+}
+
+###############################################################################
+# ROCm-specific pytest command rewrites
+#
+# These apply ignore flags and environment overrides for tests that are not
+# yet supported or behave differently on ROCm hardware. Kept as a single
+# function so new exclusions are easy to add in one place.
+###############################################################################
+
+apply_rocm_test_overrides() {
+  local cmds="$1"
+
+  # --- Model registry filter ---
+  if [[ $cmds == *"pytest -v -s models/test_registry.py"* ]]; then
+    cmds=${cmds//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
+  fi
+
+  # --- LoRA: disable custom paged attention ---
+  if [[ $cmds == *"pytest -v -s lora"* ]]; then
+    cmds=${cmds//"pytest -v -s lora"/"pytest -v -s lora"}
+  fi
+
+  # --- Kernel ignores ---
+  if [[ $cmds == *" kernels/core"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/core/test_fused_quant_layernorm.py \
+    --ignore=kernels/core/test_permute_cols.py"
+  fi
+
+  if [[ $cmds == *" kernels/attention"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/attention/test_attention_selector.py \
+    --ignore=kernels/attention/test_encoder_decoder_attn.py \
+    --ignore=kernels/attention/test_flash_attn.py \
+    --ignore=kernels/attention/test_flashinfer.py \
+    --ignore=kernels/attention/test_prefix_prefill.py \
+    --ignore=kernels/attention/test_cascade_flash_attn.py \
+    --ignore=kernels/attention/test_mha_attn.py \
+    --ignore=kernels/attention/test_lightning_attn.py \
+    --ignore=kernels/attention/test_attention.py"
+  fi
+
+  if [[ $cmds == *" kernels/quantization"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/quantization/test_int8_quant.py \
+    --ignore=kernels/quantization/test_machete_mm.py \
+    --ignore=kernels/quantization/test_block_fp8.py \
+    --ignore=kernels/quantization/test_block_int8.py \
+    --ignore=kernels/quantization/test_marlin_gemm.py \
+    --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
+    --ignore=kernels/quantization/test_int8_kernel.py"
+  fi
+
+  if [[ $cmds == *" kernels/mamba"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/mamba/test_mamba_mixer2.py \
+    --ignore=kernels/mamba/test_causal_conv1d.py \
+    --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
+  fi
+
+  if [[ $cmds == *" kernels/moe"* ]]; then
+    cmds="${cmds} \
+    --ignore=kernels/moe/test_moe.py \
+    --ignore=kernels/moe/test_cutlass_moe.py"
+  fi
+
+  # --- Entrypoint ignores ---
+  if [[ $cmds == *" entrypoints/openai "* ]]; then
+    cmds=${cmds//" entrypoints/openai "/" entrypoints/openai \
+    --ignore=entrypoints/openai/chat_completion/test_audio.py \
+    --ignore=entrypoints/openai/completion/test_shutdown.py \
+    --ignore=entrypoints/openai/test_completion.py \
+    --ignore=entrypoints/openai/models/test_models.py \
+    --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
+    --ignore=entrypoints/openai/chat_completion/test_root_path.py \
+    --ignore=entrypoints/openai/completion/test_prompt_validation.py "}
+  fi
+
+  if [[ $cmds == *" entrypoints/serve"* ]]; then
+    cmds="${cmds} \
+    --ignore=entrypoints/serve/lora/test_lora_adapters.py"
+  fi
+
+  if [[ $cmds == *" entrypoints/llm "* ]]; then
+    cmds=${cmds//" entrypoints/llm "/" entrypoints/llm \
+    --ignore=entrypoints/llm/test_chat.py \
+    --ignore=entrypoints/llm/test_accuracy.py \
+    --ignore=entrypoints/llm/test_init.py \
+    --ignore=entrypoints/llm/test_prompt_validation.py "}
+  fi
+
+  # Clean up escaped newlines from --ignore appends
+  cmds=$(echo "$cmds" | sed 's/ \\ / /g')
+
+  echo "$cmds"
+}
+
+###############################################################################
+# Main
+###############################################################################
+
+# --- GPU initialization ---
+echo "--- ROCm info"
+rocminfo
+
+# --- Docker housekeeping ---
 cleanup_docker
 
-echo "--- Resetting GPUs"
-
-echo "reset" > /opt/amdgpu/etc/gpu_state
-
-while true; do
-        sleep 3
-        if grep -q clean /opt/amdgpu/etc/gpu_state; then
-                echo "GPUs state is \"clean\""
-                break
-        fi
-done
-
+# --- Pull test image ---
 echo "--- Pulling container"
 image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 docker pull "${image_name}"
 
 remove_docker_container() {
-   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
+  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
 trap remove_docker_container EXIT
 
+# --- Prepare commands ---
 echo "--- Running container"
 
 HF_CACHE="$(realpath ~)/huggingface"
 mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"
 
-commands=$@
+# ---- Command source selection ----
+# Prefer VLLM_TEST_COMMANDS (preserves all inner quoting intact).
+# Fall back to $* for backward compatibility, but warn that inner
+# double-quotes will have been stripped by the calling shell.
+if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
+  commands="${VLLM_TEST_COMMANDS}"
+  commands_source="env"
+  echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
+else
+  commands="$*"
+  commands_source="argv"
+  if [[ -z "$commands" ]]; then
+    echo "Error: No test commands provided." >&2
+    echo "Usage:" >&2
+    echo "  Preferred:  VLLM_TEST_COMMANDS='...' bash $0" >&2
+    echo "  Legacy:     bash $0 \"commands here\"" >&2
+    exit 1
+  fi
+  echo "Commands sourced from positional args (legacy mode)"
+  echo "WARNING: Inner double-quotes in the command string may have been"
+  echo "  stripped by the calling shell. If you see syntax errors, switch to:"
+  echo "  export VLLM_TEST_COMMANDS='your commands here'"
+  echo "  bash $0"
+fi
+
 echo "Raw commands: $commands"
 
-commands=${commands//"pytest -v -s basic_correctness/test_basic_correctness.py"/"pytest -v -s basic_correctness/test_basic_correctness.py"}
-
-if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
-  commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
+# Only try to repair stripped pytest -m/-k quoting in legacy argv mode.
+# VLLM_TEST_COMMANDS preserves inner quoting already, and re-quoting that path
+# can corrupt embedded echo strings or otherwise well-formed shell fragments.
+if [[ "$commands_source" == "argv" ]]; then
+  commands=$(re_quote_pytest_markers "$commands")
+  echo "After re-quoting: $commands"
+else
+  echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
 fi
 
-commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"pytest -v -s compile/test_basic_correctness.py"}
-
-if [[ $commands == *"pytest -v -s lora"* ]]; then
-  commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
-fi
-
-#ignore certain kernels tests
-if [[ $commands == *" kernels/core"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/core/test_fused_quant_layernorm.py \
-  --ignore=kernels/core/test_permute_cols.py"
-fi
-
-if [[ $commands == *" kernels/attention"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/attention/test_attention_selector.py \
-  --ignore=kernels/attention/test_encoder_decoder_attn.py \
-  --ignore=kernels/attention/test_flash_attn.py \
-  --ignore=kernels/attention/test_flashinfer.py \
-  --ignore=kernels/attention/test_prefix_prefill.py \
-  --ignore=kernels/attention/test_cascade_flash_attn.py \
-  --ignore=kernels/attention/test_mha_attn.py \
-  --ignore=kernels/attention/test_lightning_attn.py \
-  --ignore=kernels/attention/test_attention.py"
-fi
-
-if [[ $commands == *" kernels/quantization"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/quantization/test_int8_quant.py \
-  --ignore=kernels/quantization/test_machete_mm.py \
-  --ignore=kernels/quantization/test_block_fp8.py \
-  --ignore=kernels/quantization/test_block_int8.py \
-  --ignore=kernels/quantization/test_marlin_gemm.py \
-  --ignore=kernels/quantization/test_cutlass_scaled_mm.py \
-  --ignore=kernels/quantization/test_int8_kernel.py"
-fi
-
-if [[ $commands == *" kernels/mamba"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/mamba/test_mamba_mixer2.py \
-  --ignore=kernels/mamba/test_causal_conv1d.py \
-  --ignore=kernels/mamba/test_mamba_ssm_ssd.py"
-fi
-
-if [[ $commands == *" kernels/moe"* ]]; then
-  commands="${commands} \
-  --ignore=kernels/moe/test_moe.py \
-  --ignore=kernels/moe/test_cutlass_moe.py \
-  --ignore=kernels/moe/test_triton_moe_ptpc_fp8.py"
-fi
-
-#ignore certain Entrypoints/openai tests
-if [[ $commands == *" entrypoints/openai "* ]]; then
-  commands=${commands//" entrypoints/openai "/" entrypoints/openai \
-  --ignore=entrypoints/openai/test_audio.py \
-  --ignore=entrypoints/openai/test_shutdown.py \
-  --ignore=entrypoints/openai/test_completion.py \
-  --ignore=entrypoints/openai/test_models.py \
-  --ignore=entrypoints/openai/test_lora_adapters.py \
-  --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
-  --ignore=entrypoints/openai/test_root_path.py \
-  --ignore=entrypoints/openai/test_tokenization.py \
-  --ignore=entrypoints/openai/test_prompt_validation.py "}
-fi
-
-#ignore certain Entrypoints/llm tests
-if [[ $commands == *" entrypoints/llm "* ]]; then
-  commands=${commands//" entrypoints/llm "/" entrypoints/llm \
-  --ignore=entrypoints/llm/test_chat.py \
-  --ignore=entrypoints/llm/test_accuracy.py \
-  --ignore=entrypoints/llm/test_init.py \
-  --ignore=entrypoints/llm/test_prompt_validation.py "}
-fi
-
-commands=$(echo "$commands" | sed 's/ \\ / /g')
+commands=$(apply_rocm_test_overrides "$commands")
 echo "Final commands: $commands"
 
-# --ignore=entrypoints/openai/test_encoder_decoder.py \
-# --ignore=entrypoints/openai/test_embedding.py \
-# --ignore=entrypoints/openai/test_oot_registration.py
-# --ignore=entrypoints/openai/test_accuracy.py \
-# --ignore=entrypoints/openai/test_models.py <= Fails on MI250 but passes on MI300 as of 2025-03-13
-
-
 MYPYTHONPATH=".."
 
-# Test that we're launching on the machine that has
-# proper access to GPUs
+# Verify GPU access
 render_gid=$(getent group render | cut -d: -f3)
 if [[ -z "$render_gid" ]]; then
   echo "Error: 'render' group not found. This is required for GPU access." >&2
   exit 1
 fi
 
-if [[ $commands == *"VLLM_TEST_GROUP_NAME=mi325_4-2-node-tests-4-gpus-in-total"* ]]; then
+# --- RDMA device passthrough (conditional) ---
+# If the host has RDMA devices, pass them through so tests like
+# test_moriio_connector can access ibverbs. On hosts without RDMA
+# hardware the tests will gracefully skip via _rdma_available().
+RDMA_FLAGS=""
+if [ -d /dev/infiniband ]; then
+  echo "RDMA devices detected on host, enabling passthrough"
+  RDMA_FLAGS="--device /dev/infiniband --cap-add=IPC_LOCK"
+else
+  echo "No RDMA devices found on host, RDMA tests will be skipped"
+fi
 
+# --- Route: multi-node vs single-node ---
+if is_multi_node "$commands"; then
+  echo "--- Multi-node job detected"
   export DCKR_VER=$(docker --version | sed 's/Docker version \(.*\), build .*/\1/')
 
-  if [[ "$commands" =~ ^(.*)"["(.*)"] && ["(.*)"]"$ ]]; then
-      prefix=$( echo "${BASH_REMATCH[1]}" | sed 's/;//g')
-      echo "PREFIX: ${prefix}"
-      export composite_command="(command rocm-smi || true)"
-      myIFS=$IFS
-      IFS=','
-      read -ra node0 <<< ${BASH_REMATCH[2]}
-      read -ra node1 <<< ${BASH_REMATCH[3]}
-      IFS=$myIFS
-      for i in "${!node0[@]}";do 
-        command_node_0=$(echo ${node0[i]} | sed 's/\"//g')
-        command_node_1=$(echo ${node1[i]} | sed 's/\"//g')
-        
-        export commands="./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 ${image_name} '${command_node_0}' '${command_node_1}'"
-        echo "COMMANDS: ${commands}"
-        composite_command=$(echo "${composite_command} && ${commands}")
-      done
-      /bin/bash -c "${composite_command}"
-      cleanup_network
+  # Parse the bracket syntax:  prefix ; [node0_cmds] && [node1_cmds]
+  #   BASH_REMATCH[1] = prefix (everything before first bracket)
+  #   BASH_REMATCH[2] = comma-separated node0 commands
+  #   BASH_REMATCH[3] = comma-separated node1 commands
+  if [[ "$commands" =~ ^(.*)\[(.*)"] && ["(.*)\]$ ]]; then
+    prefix=$(echo "${BASH_REMATCH[1]}" | sed 's/;//g')
+    echo "PREFIX: ${prefix}"
+
+    export composite_command="(command rocm-smi || true)"
+    saved_IFS=$IFS
+    IFS=','
+    read -ra node0 <<< "${BASH_REMATCH[2]}"
+    read -ra node1 <<< "${BASH_REMATCH[3]}"
+    IFS=$saved_IFS
+
+    if [[ ${#node0[@]} -ne ${#node1[@]} ]]; then
+      echo "Warning: node0 has ${#node0[@]} commands, node1 has ${#node1[@]}. They will be paired by index."
+    fi
+
+    for i in "${!node0[@]}"; do
+      command_node_0=$(echo "${node0[i]}" | sed 's/\"//g')
+      command_node_1=$(echo "${node1[i]}" | sed 's/\"//g')
+
+      step_cmd="./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 ${image_name} '${command_node_0}' '${command_node_1}'"
+      echo "COMMANDS: ${step_cmd}"
+      composite_command="${composite_command} && ${step_cmd}"
+    done
+
+    /bin/bash -c "${composite_command}"
+    exit_code=$?
+    cleanup_network
+    handle_pytest_exit "$exit_code"
   else
-      echo "Failed to parse node commands! Exiting."
-      cleanup_network
-      exit 111
+    echo "Multi-node job detected but failed to parse bracket command syntax."
+    echo "Expected format: prefix ; [node0_cmd1, node0_cmd2] && [node1_cmd1, node1_cmd2]"
+    echo "Got: $commands"
+    cleanup_network
+    exit 111
   fi
 else
+  echo "--- Single-node job"
   echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
+
   docker run \
-          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-          --network=host \
-          --shm-size=16gb \
-          --group-add "$render_gid" \
-          --rm \
-          -e HF_TOKEN \
-          -e AWS_ACCESS_KEY_ID \
-          -e AWS_SECRET_ACCESS_KEY \
-          -v "${HF_CACHE}:${HF_MOUNT}" \
-          -e "HF_HOME=${HF_MOUNT}" \
-          -e "PYTHONPATH=${MYPYTHONPATH}" \
-          --name "${container_name}" \
-          "${image_name}" \
-          /bin/bash -c "${commands}"
+    --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+    $RDMA_FLAGS \
+    --network=host \
+    --shm-size=16gb \
+    --group-add "$render_gid" \
+    --rm \
+    -e HF_TOKEN \
+    -e AWS_ACCESS_KEY_ID \
+    -e AWS_SECRET_ACCESS_KEY \
+    -e BUILDKITE_PARALLEL_JOB \
+    -e BUILDKITE_PARALLEL_JOB_COUNT \
+    -v "${HF_CACHE}:${HF_MOUNT}" \
+    -e "HF_HOME=${HF_MOUNT}" \
+    -e "PYTHONPATH=${MYPYTHONPATH}" \
+    -e "PYTORCH_ROCM_ARCH=" \
+    --name "${container_name}" \
+    "${image_name}" \
+    /bin/bash -c "${commands}"
+
+  exit_code=$?
+  handle_pytest_exit "$exit_code"
 fi

.buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+set -euox pipefail
+
+export VLLM_CPU_KVCACHE_SPACE=1 
+export VLLM_CPU_CI_ENV=1
+# Reduce sub-processes for acceleration
+export TORCH_COMPILE_DISABLE=1 
+export VLLM_ENABLE_V1_MULTIPROCESSING=0
+
+SDE_ARCHIVE="sde-external-10.7.0-2026-02-18-lin.tar.xz"
+SDE_CHECKSUM="CA3D4086DE4ACB3FAEDF9F57B541C6936B7D5E19AE2BF763B6EA933573A0A217"
+wget "https://downloadmirror.intel.com/913594/${SDE_ARCHIVE}"
+echo "${SDE_CHECKSUM}  ${SDE_ARCHIVE}" | sha256sum --check
+mkdir -p sde
+tar -xvf "./${SDE_ARCHIVE}" --strip-components=1 -C ./sde/
+
+wait_for_pid_and_check_log() {
+    local pid="$1"
+    local log_file="$2"
+    local exit_status
+
+    if [ -z "$pid" ] || [ -z "$log_file" ]; then
+        echo "Usage: wait_for_pid_and_check_log <PID> <LOG_FILE>"
+        return 1
+    fi
+
+    echo "Waiting for process $pid to finish..."
+    
+    # Use the 'wait' command to pause the script until the specific PID exits.
+    # The 'wait' command's own exit status will be that of the waited-for process.
+    if wait "$pid"; then
+        exit_status=$?
+        echo "Process $pid finished with exit status $exit_status (Success)."
+    else
+        exit_status=$?
+        echo "Process $pid finished with exit status $exit_status (Failure)."
+    fi
+
+    if [ "$exit_status" -ne 0 ]; then
+        echo "Process exited with a non-zero status."
+        echo "--- Last few lines of log file: $log_file ---"
+        tail -n 50 "$log_file"
+        echo "---------------------------------------------"
+        return 1 # Indicate failure based on exit status
+    fi
+
+    echo "No errors detected in log file and process exited successfully."
+    return 0
+}
+
+# Test Sky Lake (AVX512F)
+./sde/sde64 -skl -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_0.log 2>&1 &
+PID_TEST_0=$!
+
+# Test Cascade Lake (AVX512F + VNNI)
+./sde/sde64 -clx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_1.log 2>&1 &
+PID_TEST_1=$!
+
+# Test Cooper Lake (AVX512F + VNNI + BF16)
+./sde/sde64 -cpx -- python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --dtype bfloat16 > test_2.log 2>&1 &
+PID_TEST_2=$!
+
+wait_for_pid_and_check_log $PID_TEST_0 test_0.log
+wait_for_pid_and_check_log $PID_TEST_1 test_1.log
+wait_for_pid_and_check_log $PID_TEST_2 test_2.log

.buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh

@@ -1,26 +1,39 @@
 #!/bin/bash
 set -euox pipefail
+export VLLM_CPU_CI_ENV=0
+export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM
 
-echo "--- PP+TP"
-vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
-server_pid=$!
-timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-vllm bench serve \
-    --backend vllm \
-    --dataset-name random \
-    --model meta-llama/Llama-3.2-3B-Instruct \
-    --num-prompts 20 \
-    --endpoint /v1/completions
-kill -s SIGTERM $server_pid &
+MODE=${1:-all}
 
-echo "--- DP+TP"
-vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 &
-server_pid=$!
-timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-vllm bench serve \
-    --backend vllm \
-    --dataset-name random \
-    --model meta-llama/Llama-3.2-3B-Instruct \
-    --num-prompts 20 \
-    --endpoint /v1/completions
-kill -s SIGTERM $server_pid &
+run_scenario() {
+    local label="$1" result_file="$2"
+    shift 2
+    echo "--- $label"
+    vllm serve meta-llama/Llama-3.2-3B-Instruct "$@" --max-model-len=4096 &
+    local server_pid=$!
+    timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+    vllm bench serve \
+        --backend vllm \
+        --dataset-name random \
+        --model meta-llama/Llama-3.2-3B-Instruct \
+        --num-prompts 20 \
+        --result-dir ./test_results \
+        --result-filename "$result_file" \
+        --save-result \
+        --endpoint /v1/completions
+    kill -s SIGTERM "$server_pid"; wait "$server_pid" || true
+    if [ "$(jq '.failed' "./test_results/$result_file")" -ne 0 ]; then
+        echo "Some requests were failed in $label!"
+        exit 1
+    fi
+}
+
+case "$MODE" in
+    tp_pp) run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2 ;;
+    dp_tp) run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2 ;;
+    all)
+        run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2
+        run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2
+        ;;
+    *) echo "ERROR: unknown mode '$MODE' (expected: tp_pp | dp_tp | all)" >&2; exit 1 ;;
+esac

.buildkite/scripts/hardware_ci/run-cpu-test-arm.sh

@@ -5,8 +5,8 @@
 set -ex
 
 # allow to bind to different cores
-CORE_RANGE=${CORE_RANGE:-0-16}
-OMP_CORE_RANGE=${OMP_CORE_RANGE:-0-16}
+CORE_RANGE=${CORE_RANGE:-0-31}
+OMP_CORE_RANGE=${OMP_CORE_RANGE:-0-31}
 
 export CMAKE_BUILD_PARALLEL_LEVEL=16
 
@@ -31,22 +31,36 @@ function cpu_tests() {
     set -e
     pip list"
 
+  # Run kernel tests
+  docker exec cpu-test bash -c "
+    set -e
+    pytest -x -v -s tests/kernels/test_onednn.py
+    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+    pytest -x -v -s tests/kernels/core/test_cpu_activation.py
+    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
+
+  # skip tests requiring model downloads if HF_TOKEN is not set
+  # due to rate-limits
+  if [ -z "$HF_TOKEN" ]; then
+    echo "Warning: HF_TOKEN is not set. Skipping tests that require model downloads."
+    return
+  fi
+
   # offline inference
   docker exec cpu-test bash -c "
     set -e
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m"
 
   # Run model tests
   docker exec cpu-test bash -c "
     set -e
     pytest -x -v -s tests/models/multimodal/generation/test_whisper.py -m cpu_model"
 
-  # Run kernel tests
+  # Run quantized model tests
   docker exec cpu-test bash -c "
     set -e
-    pytest -x -v -s tests/kernels/test_onednn.py
-    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
+    pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
+
 
   # basic online serving
   docker exec cpu-test bash -c '
@@ -61,6 +75,21 @@ function cpu_tests() {
       --num-prompts 20 \
       --endpoint /v1/completions
     kill -s SIGTERM $server_pid &'
+
+  # smoke test for Gated DeltaNet
+  docker exec cpu-test bash -c '
+    set -e
+    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS vllm serve Qwen/Qwen3.5-0.8B --max-model-len 2048 &
+    server_pid=$!
+    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
+    vllm bench serve \
+      --backend vllm \
+      --dataset-name random \
+      --model Qwen/Qwen3.5-0.8B \
+      --num-prompts 20 \
+      --endpoint /v1/completions
+    kill -s SIGTERM $server_pid &'
+
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh

@@ -27,7 +27,7 @@ function cpu_tests() {
   podman exec -it "$container_id" bash -c "
     export TORCH_COMPILE_DISABLE=1
     set -xve
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m" >> "$HOME"/test_basic.log
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m" >> "$HOME"/test_basic.log
 
   # Run basic model test
   podman exec -it "$container_id" bash -c "

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -16,5 +16,5 @@ echo "--- :docker: Building Docker image"
 docker build --progress plain --tag "$IMAGE_NAME" --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 --shm-size=4g "$IMAGE_NAME" \
+docker run --rm --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN -e VLLM_CPU_KVCACHE_SPACE=16 -e VLLM_CPU_CI_ENV=1 -e VLLM_CPU_SIM_MULTI_NUMA=1 -e VLLM_CPU_ATTN_SPLIT_KV=0 --shm-size=4g "$IMAGE_NAME" \
         timeout "$TIMEOUT_VAL" bash -c "set -euox pipefail; echo \"--- Print packages\"; pip list; echo \"--- Running tests\"; ${TEST_COMMAND}"

.buildkite/scripts/hardware_ci/run-gh200-test.sh

@@ -25,5 +25,5 @@ remove_docker_container
 
 # Run the image and test offline inference
 docker run -e HF_TOKEN -e VLLM_WORKER_MULTIPROC_METHOD=spawn -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
-    python3 examples/offline_inference/basic/generate.py --model meta-llama/Llama-3.2-1B
+    python3 examples/basic/offline_inference/generate.py --model meta-llama/Llama-3.2-1B
 '

.buildkite/scripts/hardware_ci/run-hpu-test.sh

@@ -1,9 +1,27 @@
 #!/bin/bash
 
-# This script build the CPU docker image and run the offline inference inside the container.
+# This script builds the HPU docker image and runs the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
+#
+# vllm-gaudi compatibility pinning:
+#   The vllm-gaudi plugin is installed on top of the vllm upstream checkout used by this CI job.
+#   When upstream vllm changes its API, the plugin may break before it has been updated.
+#   To handle this, the vllm-gaudi repository maintains a file:
+#     vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT
+#   The first line of that file controls what version of vllm is used inside the Docker image:
+#     - "latest"        : no checkout override; the current Buildkite CI commit is used as-is.
+#     - "<commit SHA>"  : vllm is checked out to that specific commit before building, pinning
+#                         the test to a known-compatible baseline.
+#   To unpin (resume testing against the live vllm tip), set the file content back to "latest".
 set -exuo pipefail
 
+# Fetch the vllm community commit reference from vllm-gaudi (first line only).
+VLLM_COMMUNITY_COMMIT=$(curl -s \
+  https://raw.githubusercontent.com/vllm-project/vllm-gaudi/vllm/last-good-commit-for-vllm-gaudi/VLLM_COMMUNITY_COMMIT \
+  | head -1 | tr -d '\n')
+
+echo "Using vllm community commit: ${VLLM_COMMUNITY_COMMIT}"
+
 # Try building the docker image
 image_name="hpu/upstream-vllm-ci:${BUILDKITE_COMMIT}"
 container_name="hpu-upstream-vllm-ci-${BUILDKITE_COMMIT}-container"
@@ -12,12 +30,19 @@ FROM gaudi-base-image:latest
 
 COPY ./ /workspace/vllm
 
+# If VLLM_COMMUNITY_COMMIT is a specific commit (not "latest"), check it out to pin vllm
+# to the version known to be compatible with vllm-gaudi. When the value is "latest",
+# the current checkout (the Buildkite CI commit) is used unchanged.
+RUN if [ "${VLLM_COMMUNITY_COMMIT}" != "latest" ]; then \
+      cd /workspace/vllm && git fetch --unshallow 2>/dev/null || true && git checkout ${VLLM_COMMUNITY_COMMIT}; \
+    fi
+
 WORKDIR /workspace/vllm
 
 ENV no_proxy=localhost,127.0.0.1
 ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true
 
-RUN bash -c 'pip install -r <(sed "/^torch/d" requirements/build.txt)'
+RUN bash -c 'pip install -r <(sed "/^torch/d" requirements/build/cuda.txt)'
 RUN VLLM_TARGET_DEVICE=empty pip install --no-build-isolation -e .
 RUN pip install git+https://github.com/vllm-project/vllm-gaudi.git
 
@@ -51,7 +76,7 @@ docker run --rm --runtime=habana --name="${container_name}" --network=host \
   -e PT_HPU_LAZY_MODE=1 \
   "${image_name}" \
   /bin/bash -c '
-  cd vllm; timeout 120s python -u examples/offline_inference/basic/generate.py --model facebook/opt-125m
+  cd vllm; timeout 120s python -u examples/basic/offline_inference/generate.py --model facebook/opt-125m
 '
 
 EXITCODE=$?

.buildkite/scripts/hardware_ci/run-intel-test.sh

@@ -0,0 +1,368 @@
+#!/bin/bash
+
+# This script runs tests inside the Intel XPU docker container.
+# It mirrors the structure of run-amd-test.sh while keeping Intel-specific
+# container setup and allowing commands to be sourced from YAML or env.
+#
+# Command sources (in priority order):
+#   1) VLLM_TEST_COMMANDS env var (preferred, preserves quoting)
+#   2) Positional args (legacy)
+#   3) One or more YAML files with a commands list (test-area style)
+###############################################################################
+set -o pipefail
+
+DRY_RUN=${DRY_RUN:-0}
+if [[ "${1:-}" == "--dry-run" ]]; then
+  DRY_RUN=1
+  shift
+fi
+
+# Export Python path
+export PYTHONPATH=".."
+
+###############################################################################
+# Helper Functions
+###############################################################################
+
+cleanup_docker() {
+  # Share the same lock with image pull to avoid cleanup/pull races on one node.
+  local docker_lock="/tmp/docker-pull.lock"
+  exec 9>"$docker_lock"
+  flock 9
+
+  docker_root=$(docker info -f '{{.DockerRootDir}}')
+  if [ -z "$docker_root" ]; then
+    echo "Failed to determine Docker root directory." >&2
+    flock -u 9
+    return 1
+  fi
+  echo "Docker root directory: $docker_root"
+
+  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
+  threshold=70
+  if [ "$disk_usage" -gt "$threshold" ]; then
+    echo "Disk usage is above $threshold%. Running aggressive CI image cleanup..."
+    cleanup_old_ci_images "${REGISTRY}/${REPO}" "${image_name}" "${DOCKER_IMAGE_CLEANUP_HOURS:-72}" 1
+  else
+    echo "Disk usage is below $threshold%. Checking old CI images anyway."
+    cleanup_old_ci_images "${REGISTRY}/${REPO}" "${image_name}" "${DOCKER_IMAGE_CLEANUP_HOURS:-72}" 0
+  fi
+  echo "Old CI image cleanup completed."
+
+  flock -u 9
+}
+
+cleanup_old_ci_images() {
+  local repo_prefix="$1"
+  local current_image_ref="$2"
+  local ttl_hours="$3"
+  local aggressive_cleanup="$4"
+
+  if [[ -z "$repo_prefix" || "$repo_prefix" == "/" ]]; then
+    echo "Skip old-image cleanup: invalid repo prefix '${repo_prefix}'"
+    return 0
+  fi
+
+  if ! [[ "$ttl_hours" =~ ^[0-9]+$ ]]; then
+    echo "Invalid DOCKER_IMAGE_CLEANUP_HOURS='${ttl_hours}', fallback to 72"
+    ttl_hours=72
+  fi
+
+  local now_epoch cutoff_epoch
+  now_epoch=$(date +%s)
+  cutoff_epoch=$((now_epoch - ttl_hours * 3600))
+
+  local -a used_image_ids
+  mapfile -t used_image_ids < <(docker ps -aq | xargs -r docker inspect --format '{{.Image}}' | sort -u)
+
+  local removed_count=0
+  local examined_count=0
+  declare -A seen_ids=()
+
+  while read -r image_ref image_id; do
+    [[ -z "$image_ref" || -z "$image_id" ]] && continue
+    ((examined_count++))
+
+    # Keep the image this job is going to use.
+    if [[ "$image_ref" == "$current_image_ref" ]]; then
+      continue
+    fi
+
+    # Avoid duplicate deletes when multiple tags point to same image id.
+    if [[ -n "${seen_ids[$image_id]:-}" ]]; then
+      continue
+    fi
+    seen_ids[$image_id]=1
+
+    # Never delete images that are used by any container on this node.
+    if printf '%s\n' "${used_image_ids[@]}" | grep -qx "$image_id"; then
+      continue
+    fi
+
+    local created created_epoch
+    created=$(docker image inspect -f '{{.Created}}' "$image_id" 2>/dev/null || true)
+    [[ -z "$created" ]] && continue
+    created_epoch=$(date -d "$created" +%s 2>/dev/null || true)
+    [[ -z "$created_epoch" ]] && continue
+
+    if (( created_epoch < cutoff_epoch )) || [[ "$aggressive_cleanup" == "1" ]]; then
+      if docker image rm -f "$image_id" >/dev/null 2>&1; then
+        ((removed_count++))
+      fi
+    fi
+  done < <(docker image ls --no-trunc "$repo_prefix" --format '{{.Repository}}:{{.Tag}} {{.ID}}')
+
+  # Also trim old dangling layers; this is safe and does not remove referenced images.
+  docker image prune -f --filter "until=${ttl_hours}h" >/dev/null 2>&1 || true
+
+  if [[ "$aggressive_cleanup" == "1" ]]; then
+    echo "Examined ${examined_count} images under ${repo_prefix}, removed ${removed_count} unused images under disk pressure."
+  else
+    echo "Examined ${examined_count} images under ${repo_prefix}, removed ${removed_count} old images (>${ttl_hours}h)."
+  fi
+}
+
+re_quote_pytest_markers() {
+  local input="$1"
+  local output=""
+  local collecting=false
+  local marker_buf=""
+
+  local flat="${input//$'\n'/ }"
+  local restore_glob
+  restore_glob="$(shopt -p -o noglob 2>/dev/null || true)"
+  set -o noglob
+  local -a words
+  read -ra words <<< "$flat"
+  eval "$restore_glob"
+
+  for word in "${words[@]}"; do
+    if $collecting; then
+      if [[ "$word" == *"'"* ]]; then
+        if [[ -n "$marker_buf" ]]; then
+          output+="${marker_buf} "
+          marker_buf=""
+        fi
+        output+="${word} "
+        collecting=false
+        continue
+      fi
+
+      local is_boundary=false
+      case "$word" in
+        "&&"|"||"|";"|"|")
+          is_boundary=true ;;
+        --*)
+          is_boundary=true ;;
+        -[a-zA-Z])
+          is_boundary=true ;;
+        */*)
+          is_boundary=true ;;
+        *.py|*.py::*)
+          is_boundary=true ;;
+        *=*)
+          if [[ "$word" =~ ^[A-Z_][A-Z0-9_]*= ]]; then
+            is_boundary=true
+          fi
+          ;;
+      esac
+
+      if $is_boundary; then
+        if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+          output+="'${marker_buf}' "
+        else
+          output+="${marker_buf} "
+        fi
+        collecting=false
+        marker_buf=""
+        if [[ "$word" == "-m" || "$word" == "-k" ]]; then
+          output+="${word} "
+          collecting=true
+        else
+          output+="${word} "
+        fi
+      else
+        if [[ -n "$marker_buf" ]]; then
+          marker_buf+=" ${word}"
+        else
+          marker_buf="${word}"
+        fi
+      fi
+    elif [[ "$word" == "-m" || "$word" == "-k" ]]; then
+      output+="${word} "
+      collecting=true
+      marker_buf=""
+    else
+      output+="${word} "
+    fi
+  done
+
+  if $collecting && [[ -n "$marker_buf" ]]; then
+    if [[ "$marker_buf" == *" "* || "$marker_buf" == *"("* ]]; then
+      output+="'${marker_buf}'"
+    else
+      output+="${marker_buf}"
+    fi
+  fi
+
+  echo "${output% }"
+}
+
+apply_intel_test_overrides() {
+  local cmds="$1"
+  # Placeholder for Intel-specific exclusions/overrides.
+  echo "$cmds"
+}
+
+is_yaml_file() {
+  local p="$1"
+  [[ -f "$p" && "$p" == *.yaml ]]
+}
+
+extract_yaml_commands() {
+  local yaml_path="$1"
+  awk '
+    $1 == "commands:" { in_cmds=1; next }
+    in_cmds && $0 ~ /^[[:space:]]*-[[:space:]]/ {
+      sub(/^[[:space:]]*-[[:space:]]/, "");
+      print;
+      next
+    }
+    in_cmds && $0 ~ /^[^[:space:]]/ { exit }
+  ' "$yaml_path"
+}
+
+###############################################################################
+# Main
+###############################################################################
+
+default_image_name="${REGISTRY}/${REPO}:${BUILDKITE_COMMIT}-xpu"
+#default_image_name="public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${BUILDKITE_COMMIT}-xpu"
+image_name="${IMAGE_TAG_XPU:-${default_image_name}}"
+container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+
+# ---- Command source selection ----
+commands=""
+if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
+  commands="${VLLM_TEST_COMMANDS}"
+  echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
+elif [[ $# -gt 0 ]]; then
+  all_yaml=true
+  for arg in "$@"; do
+    if ! is_yaml_file "$arg"; then
+      all_yaml=false
+      break
+    fi
+  done
+
+  if $all_yaml; then
+    for yaml in "$@"; do
+      mapfile -t COMMANDS < <(extract_yaml_commands "$yaml")
+      if [[ ${#COMMANDS[@]} -eq 0 ]]; then
+        echo "Error: No commands found in ${yaml}" >&2
+        exit 1
+      fi
+      for cmd in "${COMMANDS[@]}"; do
+        if [[ -z "$commands" ]]; then
+          commands="${cmd}"
+        else
+          commands+=" && ${cmd}"
+        fi
+      done
+    done
+    echo "Commands sourced from YAML files: $*"
+  else
+    commands="$*"
+    echo "Commands sourced from positional args (legacy mode)"
+  fi
+else
+  SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+  DEFAULT_YAML="${SCRIPT_DIR}/intel-test.yaml"
+  if [[ ! -f "${DEFAULT_YAML}" ]]; then
+    echo "Error: YAML file not found: ${DEFAULT_YAML}" >&2
+    exit 1
+  fi
+  mapfile -t COMMANDS < <(extract_yaml_commands "${DEFAULT_YAML}")
+  if [[ ${#COMMANDS[@]} -eq 0 ]]; then
+    echo "Error: No commands found in ${DEFAULT_YAML}" >&2
+    exit 1
+  fi
+  for cmd in "${COMMANDS[@]}"; do
+    if [[ -z "$commands" ]]; then
+      commands="${cmd}"
+    else
+      commands+=" && ${cmd}"
+    fi
+  done
+  echo "Commands sourced from default YAML: ${DEFAULT_YAML}"
+fi
+
+if [[ -z "$commands" ]]; then
+  echo "Error: No test commands provided." >&2
+  exit 1
+fi
+
+echo "Raw commands: $commands"
+commands=$(re_quote_pytest_markers "$commands")
+echo "After re-quoting: $commands"
+commands=$(apply_intel_test_overrides "$commands")
+echo "Final commands: $commands"
+
+# Dry-run mode prints final commands and exits before Docker.
+if [[ "$DRY_RUN" == "1" ]]; then
+  echo "DRY_RUN=1 set, skipping Docker execution."
+  exit 0
+fi
+
+# --- Docker housekeeping ---
+cleanup_docker
+
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+
+# --- Build or pull test image ---
+IMAGE="${IMAGE_TAG_XPU:-${image_name}}"
+
+echo "Using image: ${IMAGE}"
+
+if docker image inspect "${IMAGE}" >/dev/null 2>&1; then
+  echo "Image already exists locally, skipping pull"
+else
+  echo "Image not found locally, waiting for lock..."
+
+  flock /tmp/docker-pull.lock bash -c "
+    if docker image inspect '${IMAGE}' >/dev/null 2>&1; then
+      echo 'Image already pulled by another runner'
+    else
+      echo 'Pulling image...'
+      timeout 900 docker pull '${IMAGE}'
+    fi
+  "
+
+  echo "Pull step completed"
+fi
+
+remove_docker_container() {
+  docker rm -f "${container_name}" || true
+}
+trap remove_docker_container EXIT
+
+# --- Single-node job ---
+
+if [[ -z "${ZE_AFFINITY_MASK:-}" ]]; then
+  echo "Warning: ZE_AFFINITY_MASK is not set. Proceeding without device affinity." >&2
+fi
+
+docker run \
+    --device /dev/dri:/dev/dri \
+    --net=host \
+    --ipc=host \
+    --privileged \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
+    --entrypoint="" \
+    -e "HF_TOKEN=${HF_TOKEN:-}" \
+    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK:-}" \
+    -e "CMDS=${commands}" \
+    --name "${container_name}" \
+    "${image_name}" \
+    bash -c 'set -e; echo "ZE_AFFINITY_MASK is ${ZE_AFFINITY_MASK:-}"; eval "$CMDS"'

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -127,7 +127,7 @@ run_and_track_test() {
 
 # --- Actual Test Execution ---
 run_and_track_test 1 "test_struct_output_generate.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
+    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
 run_and_track_test 2 "test_moe_pallas.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
 run_and_track_test 3 "test_lora.py" \

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -136,8 +136,6 @@ run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
     "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
-run_and_track_test 5 "examples/offline_inference/tpu.py" \
-    "python3 /workspace/vllm/examples/offline_inference/tpu.py"
 run_and_track_test 6 "test_tpu_model_runner.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
 run_and_track_test 7 "test_sampler.py" \

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -12,9 +12,7 @@ docker build -t "${image_name}" -f docker/Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() {
-  docker rm -f "${container_name}" || true;
-  docker image rm -f "${image_name}" || true;
-  docker system prune -f || true;
+  docker rm -f "${container_name}" || true
 }
 trap remove_docker_container EXIT
 
@@ -33,23 +31,23 @@ docker run \
     bash -c '
     set -e
     echo $ZE_AFFINITY_MASK
-    pip install tblib==3.1.0
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8
-    python3 examples/offline_inference/basic/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4  --block-size 64 --enforce-eager
-    python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2
-    python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 -O3 -cc.cudagraph_mode=NONE
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --attention-backend=TRITON_ATTN
+    python3 examples/basic/offline_inference/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager --quantization fp8
+    python3 examples/basic/offline_inference/generate.py --model superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4  --block-size 64 --enforce-eager --max-model-len 8192
+    python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2
+    python3 examples/basic/offline_inference/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel
+    python3 examples/basic/offline_inference/generate.py --model OPEA/Qwen2.5-0.5B-Instruct-int4-sym-inc --block-size 64 --enforce-eager --max-model-len 8192
     cd tests
-    pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py
+    pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py --ignore=v1/core/test_scheduler_e2e.py
     pytest -v -s v1/engine
     pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
-    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
+    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py
     pytest -v -s v1/structured_output
     pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py
-    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py
+    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py
     pytest -v -s v1/test_serial_utils.py
 '

.buildkite/scripts/lib/manylinux.sh

@@ -0,0 +1,127 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Shared helper for rewriting a wheel's platform tag from the generic
+# ``linux_<arch>`` to the correct ``manylinux_<major>_<minor>_<arch>``.
+# After sourcing, call ``apply_manylinux_tag <wheel>`` on each wheel
+# that still carries the generic tag; the renamed path is printed on
+# stdout (logs go to stderr).
+#
+# Why a pinned Docker container instead of using whatever Python
+# happens to be on the agent:
+#   - vLLM's release agents are heterogeneous -- they don't agree on
+#     a Python minor version, and we can't rely on a particular
+#     ``auditwheel`` being installed.
+#   - ``detect-manylinux-tag.py`` reads ``auditwheel.wheel_abi`` and
+#     ``Policy.sym_policy``, which are *internal* APIs without a
+#     stability promise. Pinning both Python and auditwheel makes the
+#     detected tag a function of the inputs alone, and shifts version
+#     bumps from "implicit drift" to "deliberate, retested change".
+#   - Other release scripts (``generate-and-upload-nightly-index.sh``,
+#     ``upload-rocm-wheels.sh``) already use the python:3-slim image
+#     when the agent's interpreter is too old; this is the same idea
+#     made stricter.
+#
+# To keep the per-wheel cost down (the ROCm upload retags ~10 wheels
+# each run), we install auditwheel into a long-lived helper container
+# once on source, then ``docker exec`` into it for each call.
+#
+# Trap behaviour:
+# - Sourcing installs an EXIT trap that calls ``manylinux_cleanup`` to
+#   tear down the helper container. Any EXIT trap that was already in
+#   place when this file was sourced is captured and run AFTER our
+#   cleanup, so we don't silently clobber it.
+# - If a caller sets a new EXIT trap *after* sourcing, that trap will
+#   replace ours; in that case the caller should call
+#   ``manylinux_cleanup`` from their own handler.
+
+if [[ -n "${_MANYLINUX_LIB_SOURCED:-}" ]]; then
+    return 0
+fi
+_MANYLINUX_LIB_SOURCED=1
+
+# Pin both sides. Bump these deliberately and re-run a representative
+# wheel from each build target through the detection.
+_MANYLINUX_PYTHON_IMAGE="python:3.12-slim"
+_MANYLINUX_AUDITWHEEL_VERSION="6.6.0"
+
+# Resolve our own directory (and the sibling detect script) using the
+# canonical, symlink-resolved path. The container mounts cwd at the
+# same absolute path on both sides, so all paths we hand to it -- the
+# script, the wheel -- must canonicalise to a location under cwd.
+_MANYLINUX_LIB_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)"
+_MANYLINUX_DETECT_SCRIPT="$(cd "${_MANYLINUX_LIB_DIR}/.." && pwd -P)/detect-manylinux-tag.py"
+_MANYLINUX_CWD="$(pwd -P)"
+
+docker pull --quiet "$_MANYLINUX_PYTHON_IMAGE" >/dev/null
+
+# Spin up a long-lived helper container so we install auditwheel once
+# and then ``docker exec`` into it for each wheel.
+#
+# The container runs as root so ``pip install`` can write into the
+# system site-packages; individual ``docker exec`` calls below pin
+# themselves to the host UID so any file rename happens with host
+# ownership, not root.
+_MANYLINUX_CONTAINER="$(docker run -d --rm \
+    -v "$_MANYLINUX_CWD:$_MANYLINUX_CWD" \
+    -w "$_MANYLINUX_CWD" \
+    "$_MANYLINUX_PYTHON_IMAGE" \
+    sleep infinity)"
+docker exec "$_MANYLINUX_CONTAINER" \
+    pip install --quiet --disable-pip-version-check \
+    --root-user-action=ignore \
+    "auditwheel==${_MANYLINUX_AUDITWHEEL_VERSION}"
+
+# Public cleanup -- safe to call multiple times.
+manylinux_cleanup() {
+    if [[ -n "${_MANYLINUX_CONTAINER:-}" ]]; then
+        docker rm -f "$_MANYLINUX_CONTAINER" >/dev/null 2>&1 || true
+        _MANYLINUX_CONTAINER=""
+    fi
+}
+
+# Capture any EXIT trap that was already in place so we can chain to
+# it rather than overwrite it. ``trap -p EXIT`` prints the handler in
+# eval-able form (``trap -- 'CMD' EXIT``) or nothing if unset; we
+# strip the wrapper to recover ``CMD``. Handles the common case --
+# CMDs without embedded single quotes -- and degrades gracefully (we
+# still run our own cleanup) for the pathological case.
+_manylinux_prev_exit_trap_cmd=""
+_manylinux_existing_exit_trap="$(trap -p EXIT)"
+if [[ -n "$_manylinux_existing_exit_trap" ]]; then
+    _tmp="${_manylinux_existing_exit_trap#trap -- \'}"
+    _manylinux_prev_exit_trap_cmd="${_tmp%\' EXIT}"
+    unset _tmp
+fi
+unset _manylinux_existing_exit_trap
+
+_manylinux_run_exit_chain() {
+    manylinux_cleanup
+    if [[ -n "$_manylinux_prev_exit_trap_cmd" ]]; then
+        eval "$_manylinux_prev_exit_trap_cmd"
+    fi
+}
+trap _manylinux_run_exit_chain EXIT
+
+# Detect the manylinux platform tag for a single wheel and rename it
+# in place, printing the renamed wheel path on stdout. Returns
+# non-zero on failure (which under ``set -e`` propagates to caller).
+#
+# The wheel must be reachable via a path under the host cwd so it's
+# visible inside the helper container; in CI the wheels always live
+# under ``artifacts/`` so this is fine.
+apply_manylinux_tag() {
+    local wheel="$1"
+    local abs_wheel
+    abs_wheel="$(realpath "$wheel")"
+    local new_wheel
+    new_wheel="$(docker exec -u "$(id -u):$(id -g)" \
+        "$_MANYLINUX_CONTAINER" \
+        python "$_MANYLINUX_DETECT_SCRIPT" "$abs_wheel")"
+    if [[ -z "$new_wheel" || ! -f "$new_wheel" ]]; then
+        echo "apply_manylinux_tag: detect-manylinux-tag.py did not produce a valid wheel path for $wheel" >&2
+        return 1
+    fi
+    printf '%s\n' "$new_wheel"
+}

.buildkite/scripts/lib/select-python.sh

@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Pick a Python interpreter for buildkite scripts: prefer a local
+# ``python3`` if it is recent enough (>= 3.12), otherwise fall back to
+# a one-shot Docker container running ``python:3-slim``. After
+# ``select_python`` returns, ``$PYTHON`` is set in the caller's shell
+# and is safe to use as a command (e.g. ``$PYTHON some_script.py``).
+#
+# The 3.12 threshold matches what the existing nightly-index work
+# expects -- typing features used by ``generate-nightly-index.py``.
+# This helper does not pin the *minor* version; if you need stricter
+# reproducibility (e.g. relying on auditwheel internals), invoke
+# Docker yourself with a pinned tag rather than calling this.
+
+if [[ -n "${_SELECT_PYTHON_LIB_SOURCED:-}" ]]; then
+    return 0
+fi
+_SELECT_PYTHON_LIB_SOURCED=1
+
+# Sets ``PYTHON`` in the caller's shell and exports it. Idempotent --
+# calling twice is safe and the second call simply re-runs the probe.
+select_python() {
+    local py="${PYTHON_PROG:-python3}"
+    local has_new_python
+    has_new_python=$("$py" -c \
+        "print(1 if __import__('sys').version_info >= (3,12) else 0)" \
+        2>/dev/null || echo 0)
+    if [[ "$has_new_python" -eq 0 ]]; then
+        # ``-u $(id -u):$(id -g)`` so files created via the container
+        # end up owned by the host user, not root.
+        docker pull python:3-slim
+        PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+    else
+        PYTHON="$py"
+    fi
+    export PYTHON
+    echo "Using python interpreter: $PYTHON"
+    echo "Python version: $($PYTHON --version)"
+}

.buildkite/scripts/publish-release-images.sh

@@ -0,0 +1,180 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Publish release Docker images from ECR to DockerHub.
+# Pulls per-arch images, tags with latest and versioned tags, pushes them,
+# then creates and pushes multi-arch manifests.
+
+set -euo pipefail
+
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
+if [ -z "${RELEASE_VERSION}" ]; then
+  echo "ERROR: release-version metadata not set"
+  exit 1
+fi
+
+COMMIT="$BUILDKITE_COMMIT"
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+
+echo "========================================"
+echo "Publishing release images v${RELEASE_VERSION}"
+echo "  Commit: ${COMMIT}"
+echo "  ROCm base cache key: ${ROCM_BASE_CACHE_KEY}"
+echo "========================================"
+
+# Login to ECR to pull staging images
+aws ecr-public get-login-password --region us-east-1 | \
+  docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+
+# ---- CUDA (default: 13.0) ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:latest-x86_64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai:latest-x86_64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:latest-aarch64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker push vllm/vllm-openai:latest-aarch64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+
+docker manifest rm vllm/vllm-openai:latest || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION} || true
+docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker manifest push vllm/vllm-openai:latest
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
+
+# ---- CUDA 12.9 ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:latest-x86_64-cu129
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+docker push vllm/vllm-openai:latest-x86_64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker push vllm/vllm-openai:latest-aarch64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+
+docker manifest rm vllm/vllm-openai:latest-cu129 || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129 || true
+docker manifest create vllm/vllm-openai:latest-cu129 vllm/vllm-openai:latest-x86_64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker manifest push vllm/vllm-openai:latest-cu129
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129
+
+# ---- Ubuntu 24.04 (CUDA 13.0) ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
+docker push vllm/vllm-openai:latest-x86_64-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
+docker push vllm/vllm-openai:latest-aarch64-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
+
+docker manifest rm vllm/vllm-openai:latest-ubuntu2404 || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 || true
+docker manifest create vllm/vllm-openai:latest-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
+docker manifest push vllm/vllm-openai:latest-ubuntu2404
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404
+
+# ---- Ubuntu 24.04 (CUDA 12.9) ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
+docker push vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
+docker push vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
+
+docker manifest rm vllm/vllm-openai:latest-cu129-ubuntu2404 || true
+docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 || true
+docker manifest create vllm/vllm-openai:latest-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
+docker manifest push vllm/vllm-openai:latest-cu129-ubuntu2404
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404
+
+# ---- ROCm ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:latest
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:latest-base
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+docker push vllm/vllm-openai-rocm:latest-base
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+
+# ---- CPU ----
+# CPU images are behind separate block steps and may not have been built.
+# All-or-nothing: inspect both arches first, then either publish everything
+# (per-arch + multi-arch manifest) or skip everything. Publishing only one
+# arch would leave `:latest-x86_64` pointing at the new release while the
+# `:latest` multi-arch manifest still resolves to the previous release.
+
+CPU_X86_TAG=public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
+CPU_ARM_TAG=public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
+
+CPU_X86_AVAILABLE=false
+CPU_ARM_AVAILABLE=false
+docker manifest inspect "${CPU_X86_TAG}" >/dev/null 2>&1 && CPU_X86_AVAILABLE=true
+docker manifest inspect "${CPU_ARM_TAG}" >/dev/null 2>&1 && CPU_ARM_AVAILABLE=true
+
+if [ "$CPU_X86_AVAILABLE" = "true" ] && [ "$CPU_ARM_AVAILABLE" = "true" ]; then
+  docker pull "${CPU_X86_TAG}"
+  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:latest-x86_64
+  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+  docker push vllm/vllm-openai-cpu:latest-x86_64
+  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+
+  docker pull "${CPU_ARM_TAG}"
+  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:latest-arm64
+  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+  docker push vllm/vllm-openai-cpu:latest-arm64
+  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+
+  docker manifest rm vllm/vllm-openai-cpu:latest || true
+  docker manifest rm vllm/vllm-openai-cpu:v${RELEASE_VERSION} || true
+  docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
+  docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+  docker manifest push vllm/vllm-openai-cpu:latest
+  docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
+elif [ "$CPU_X86_AVAILABLE" = "false" ] && [ "$CPU_ARM_AVAILABLE" = "false" ]; then
+  echo "WARNING: Neither CPU image found in ECR, skipping CPU publish (ensure block-cpu-release-image-build and block-arm64-cpu-release-image-build were unblocked and the builds finished pushing)"
+else
+  # Partial state: one arch built, the other did not. Fail loudly rather than
+  # ship a Docker Hub state where `:latest-${arch}` and `:latest` (multi-arch)
+  # disagree on which release they point at.
+  echo "ERROR: Partial CPU build detected (x86_64=${CPU_X86_AVAILABLE}, arm64=${CPU_ARM_AVAILABLE})."
+  echo "       Refusing to publish to avoid split-tag drift between per-arch and multi-arch tags."
+  echo "       Re-run the missing CPU build and retry, or manually publish if a single-arch release is intended."
+  exit 1
+fi
+
+echo ""
+echo "Successfully published release images for v${RELEASE_VERSION}"

.buildkite/scripts/push-nightly-builds-rocm.sh

@@ -0,0 +1,62 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Push ROCm nightly base image and nightly image from ECR 
+# to Docker Hub as vllm/vllm-openai-rocm:base-nightly and vllm/vllm-openai-rocm:nightly
+# and vllm/vllm-openai-rocm:base-nightly-<commit> and vllm/vllm-openai-rocm:nightly-<commit>.
+# Run when NIGHTLY=1 after build-rocm-release-image has pushed to ECR.
+#
+# Local testing (no push to Docker Hub):
+#   BUILDKITE_COMMIT=<commit-with-rocm-image-in-ecr> DRY_RUN=1 bash .buildkite/scripts/push-nightly-builds-rocm.sh
+# Requires: AWS CLI configured (for ECR public login), Docker. For full run: Docker Hub login.
+
+set -ex
+
+# Use BUILDKITE_COMMIT from env (required; set to a commit that has ROCm image in ECR for local test)
+BUILDKITE_COMMIT="${BUILDKITE_COMMIT:?Set BUILDKITE_COMMIT to the commit SHA that has the ROCm image in ECR (e.g. from a previous release pipeline run)}"
+DRY_RUN="${DRY_RUN:-0}"
+
+# Get the base image ECR tag (set by build-rocm-release-image pipeline step)
+BASE_ORIG_TAG="$(buildkite-agent meta-data get rocm-base-ecr-tag 2>/dev/null || echo "")"
+if [ -z "$BASE_ORIG_TAG" ]; then
+  echo "WARNING: rocm-base-ecr-tag metadata not found, falling back to commit-based tag"
+  BASE_ORIG_TAG="public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm-base"
+fi
+
+ORIG_TAG="${BUILDKITE_COMMIT}-rocm"
+BASE_TAG_NAME="base-nightly"
+TAG_NAME="nightly"
+BASE_TAG_NAME_COMMIT="base-nightly-${BUILDKITE_COMMIT}"
+TAG_NAME_COMMIT="nightly-${BUILDKITE_COMMIT}"
+
+echo "Pushing ROCm base image from ECR: $BASE_ORIG_TAG"
+echo "Pushing ROCm release image from ECR tag: $ORIG_TAG to Docker Hub as $TAG_NAME and $TAG_NAME_COMMIT"
+[[ "$DRY_RUN" == "1" ]] && echo "[DRY_RUN] Skipping push to Docker Hub"
+
+# Login to ECR and pull the image built by build-rocm-release-image
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+docker pull "$BASE_ORIG_TAG"
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG"
+
+# Tag for Docker Hub (base-nightly and base-nightly-<commit>, nightly and nightly-<commit>)
+docker tag "$BASE_ORIG_TAG" vllm/vllm-openai-rocm:"$BASE_TAG_NAME"
+docker tag "$BASE_ORIG_TAG" vllm/vllm-openai-rocm:"$BASE_TAG_NAME_COMMIT"
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG" vllm/vllm-openai-rocm:"$TAG_NAME"
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG" vllm/vllm-openai-rocm:"$TAG_NAME_COMMIT"
+
+if [[ "$DRY_RUN" == "1" ]]; then
+  echo "[DRY_RUN] Would push vllm/vllm-openai-rocm:$BASE_TAG_NAME and vllm/vllm-openai-rocm:$BASE_TAG_NAME_COMMIT"
+  echo "[DRY_RUN] Would push vllm/vllm-openai-rocm:$TAG_NAME and vllm/vllm-openai-rocm:$TAG_NAME_COMMIT"
+  echo "[DRY_RUN] Local tags created. Exiting without push."
+  exit 0
+fi
+
+# Push to Docker Hub (docker-login plugin runs before this step in CI)
+docker push vllm/vllm-openai-rocm:"$BASE_TAG_NAME"
+docker push vllm/vllm-openai-rocm:"$BASE_TAG_NAME_COMMIT"
+docker push vllm/vllm-openai-rocm:"$TAG_NAME"
+docker push vllm/vllm-openai-rocm:"$TAG_NAME_COMMIT"
+
+echo "Pushed vllm/vllm-openai-rocm:$BASE_TAG_NAME and vllm/vllm-openai-rocm:$BASE_TAG_NAME_COMMIT"
+echo "Pushed vllm/vllm-openai-rocm:$TAG_NAME and vllm/vllm-openai-rocm:$TAG_NAME_COMMIT"

.buildkite/scripts/run-prime-rl-test.sh

@@ -1,64 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Setup script for Prime-RL integration tests
-# This script prepares the environment for running Prime-RL tests with nightly vLLM
-
-set -euo pipefail
-
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
-PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
-PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
-
-if command -v rocm-smi &> /dev/null || command -v rocminfo &> /dev/null; then
-    echo "AMD GPU detected. Prime-RL currently only supports NVIDIA. Skipping..."
-    exit 0
-fi
-
-echo "Setting up Prime-RL integration test environment..."
-
-# Clean up any existing Prime-RL directory
-if [ -d "${PRIME_RL_DIR}" ]; then
-    echo "Removing existing Prime-RL directory..."
-    rm -rf "${PRIME_RL_DIR}"
-fi
-
-# Install UV if not available
-if ! command -v uv &> /dev/null; then
-    echo "Installing UV package manager..."
-    curl -LsSf https://astral.sh/uv/install.sh | sh
-    source "$HOME"/.local/bin/env
-fi
-
-# Clone Prime-RL repository at specific branch for reproducible tests
-PRIME_RL_BRANCH="integ-vllm-main"
-echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
-git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
-cd "${PRIME_RL_DIR}"
-
-echo "Setting up UV project environment..."
-export UV_PROJECT_ENVIRONMENT=/usr/local
-ln -s /usr/bin/python3 /usr/local/bin/python
-
-# Remove vllm pin from pyproject.toml
-echo "Removing vllm pin from pyproject.toml..."
-sed -i '/vllm==/d' pyproject.toml
-
-# Sync Prime-RL dependencies
-echo "Installing Prime-RL dependencies..."
-uv sync --inexact && uv sync --inexact --all-extras
-
-# Verify installation
-echo "Verifying installations..."
-uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
-uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
-
-echo "Prime-RL integration test environment setup complete!"
-
-echo "Running Prime-RL integration tests..."
-export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
-uv run pytest -vs tests/integration/test_rl.py -m gpu
-
-echo "Prime-RL integration tests completed!"

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh

@@ -0,0 +1,70 @@
+#!/usr/bin/env bash
+set -euxo pipefail
+# Nightly e2e test for prefetch offloading with a MoE model.
+# Runs DeepSeek-V2-Lite with prefetch offloading of MoE expert weights
+# and validates GSM8K accuracy matches baseline (no offloading).
+#
+# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
+#
+# Environment variables:
+#   ATTENTION_BACKEND   - attention backend to use (e.g., FLASH_ATTN,
+#                         ROCM_ATTN, FLASHINFER). If unset, uses vllm default.
+THRESHOLD=${1:-0.25}
+NUM_Q=${2:-1319}
+PORT=${3:-8030}
+OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
+mkdir -p "${OUT_DIR}"
+
+wait_for_server() {
+  local port=$1
+  timeout 600 bash -c '
+    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
+      sleep 1
+    done'
+}
+
+MODEL="deepseek-ai/DeepSeek-V2-Lite"
+
+# ── Build optional vllm serve flags ─────────────────────────────────────
+
+EXTRA_ARGS=()
+if [[ -n "${ATTENTION_BACKEND:-}" ]]; then
+  echo "Using attention backend: ${ATTENTION_BACKEND}"
+  EXTRA_ARGS+=(--attention-backend "${ATTENTION_BACKEND}")
+fi
+
+cleanup() {
+  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
+    kill "${SERVER_PID}" 2>/dev/null || true
+    for _ in {1..20}; do
+      kill -0 "${SERVER_PID}" 2>/dev/null || break
+      sleep 0.5
+    done
+    kill -9 "${SERVER_PID}" 2>/dev/null || true
+  fi
+}
+trap cleanup EXIT
+
+vllm serve "$MODEL" \
+  --max-model-len 2048 \
+  --offload-group-size 8 \
+  --offload-num-in-group 2 \
+  --offload-prefetch-step 1 \
+  --offload-params w13_weight w2_weight \
+  --generation-config vllm \
+  --port "$PORT" \
+  ${EXTRA_ARGS+"${EXTRA_ARGS[@]}"} &
+SERVER_PID=$!
+wait_for_server "$PORT"
+
+TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
+OUT="${OUT_DIR}/${TAG}_prefetch_offload.json"
+python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+python3 - <<PY
+import json; acc=json.load(open('${OUT}'))['accuracy']
+print(f"${MODEL} prefetch_offload: accuracy {acc:.3f}")
+assert acc >= ${THRESHOLD}, f"${MODEL} prefetch_offload accuracy {acc}"
+PY
+
+cleanup
+SERVER_PID=

.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh

@@ -0,0 +1,55 @@
+#!/usr/bin/env bash
+set -euxo pipefail
+
+# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
+THRESHOLD=${1:-0.8}
+NUM_Q=${2:-1319}
+PORT=${3:-8050}
+OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
+mkdir -p "${OUT_DIR}"
+
+wait_for_server() {
+  local port=$1
+  timeout 600 bash -c '
+    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
+      sleep 1
+    done'
+}
+
+MODEL="Qwen/Qwen3-30B-A3B-FP8"
+BACK="allgather_reducescatter"
+
+cleanup() {
+  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
+    kill "${SERVER_PID}" 2>/dev/null || true
+    for _ in {1..20}; do
+      kill -0 "${SERVER_PID}" 2>/dev/null || break
+      sleep 0.5
+    done
+    kill -9 "${SERVER_PID}" 2>/dev/null || true
+  fi
+}
+trap cleanup EXIT
+
+VLLM_DEEP_GEMM_WARMUP=skip \
+vllm serve "$MODEL" \
+--enforce-eager \
+--data-parallel-size 4 \
+--enable-expert-parallel \
+--enable-eplb \
+--all2all-backend "$BACK" \
+--eplb-config '{"window_size":20, "step_interval":100, "use_async":true}' \
+--trust-remote-code \
+--max-model-len 2048 \
+--port "$PORT" &
+SERVER_PID=$!
+wait_for_server "$PORT"
+
+TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
+OUT="${OUT_DIR}/${TAG}_${BACK}.json"
+python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port "$PORT" --num-questions "${NUM_Q}" --save-results "${OUT}"
+python3 - <<PY
+import json; acc=json.load(open('${OUT}'))['accuracy']
+print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
+assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
+PY

.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh

@@ -24,7 +24,7 @@ if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:
   BACKENDS=("allgather_reducescatter")
   # Disable MOE padding for ROCm since it is causing eplb to fail
   export VLLM_ROCM_MOE_PADDING=0
-  PLATFORM_ARGS=("--no-async-scheduling")
+  PLATFORM_ARGS=("--no-async-scheduling" "--attention-backend=TRITON_ATTN")
   echo "Disabled async scheduling for ROCm platform due to issues with spec decode."
 else
   # Non-ROCm platform (CUDA/other)

.buildkite/scripts/tool_call/run-bfcl-eval.sh

@@ -0,0 +1,252 @@
+#!/bin/bash
+# Run BFCL (Berkeley Function Call Leaderboard) tool-calling correctness
+# evaluation against a local vLLM server.
+#
+# Usage:
+#   # Run with defaults (gpt-oss-20b, multi_turn)
+#   bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
+#
+#   # Run with gpt-oss-120b and multiple test categories
+#   BFCL_MODEL="openai/gpt-oss-120b" BFCL_TP_SIZE=4 \
+#     BFCL_TEST_CATEGORY="live_simple, multiple, parallel_multiple" \
+#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
+#
+#   # Chain both API types (use BFCL_OUTPUT_DIR to avoid overwriting results)
+#   BFCL_OUTPUT_DIR=./bfcl-chat-completions BFCL_API_TYPE=chat_completions \
+#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh && \
+#   BFCL_OUTPUT_DIR=./bfcl-responses BFCL_API_TYPE=responses \
+#     bash .buildkite/scripts/tool_call/run-bfcl-eval.sh
+#
+# Environment variables (all optional, with defaults):
+#   BFCL_MODEL          - HF model name (default: openai/gpt-oss-20b)
+#   BFCL_API_TYPE       - API type: "chat_completions" or "responses" (default: chat_completions)
+#   BFCL_OUTPUT_DIR     - Directory for BFCL results (default: current working directory)
+#   BFCL_TEST_CATEGORY  - BFCL test categories (default: multi_turn)
+#   BFCL_TOOL_CALL_PARSER - Tool call parser name (default: openai)
+#   BFCL_NUM_THREADS    - Threads for BFCL generate (default: 8)
+#   BFCL_TP_SIZE        - Tensor parallel size (default: 1)
+#   BFCL_MAX_MODEL_LEN  - Max model length (default: 4096)
+#   BFCL_PORT           - Server port (default: 8000)
+#   BFCL_REASONING_PARSER - Reasoning parser name (default: disabled)
+#   BFCL_TEMPERATURE    - Temperature (default: 0.0)
+#   BFCL_EXTRA_ARGS     - Additional vLLM server args
+
+set -euo pipefail
+
+# ---- Configuration ----
+MODEL="${BFCL_MODEL:-openai/gpt-oss-20b}"
+API_TYPE="${BFCL_API_TYPE:-chat_completions}"
+OUTPUT_DIR="${BFCL_OUTPUT_DIR:-}"
+TEST_CATEGORY="${BFCL_TEST_CATEGORY:-multi_turn}"
+TOOL_CALL_PARSER="${BFCL_TOOL_CALL_PARSER:-openai}"
+NUM_THREADS="${BFCL_NUM_THREADS:-8}"
+TP_SIZE="${BFCL_TP_SIZE:-1}"
+MAX_MODEL_LEN="${BFCL_MAX_MODEL_LEN:-4096}"
+PORT="${BFCL_PORT:-8000}"
+REASONING_PARSER="${BFCL_REASONING_PARSER:-}"
+TEMPERATURE="${BFCL_TEMPERATURE:-0.0}"
+EXTRA_ARGS="${BFCL_EXTRA_ARGS:-}"
+
+# Set up output directory
+if [ -n "$OUTPUT_DIR" ]; then
+    mkdir -p "$OUTPUT_DIR"
+    OUTPUT_DIR="$(cd "$OUTPUT_DIR" && pwd)"
+fi
+
+echo "============================================"
+echo "BFCL Tool Call Correctness Evaluation"
+echo "============================================"
+echo "Model:          $MODEL"
+echo "Tool parser:    $TOOL_CALL_PARSER"
+echo "API type:       $API_TYPE"
+echo "Output dir:     ${OUTPUT_DIR:-<cwd>}"
+echo "Test category:  $TEST_CATEGORY"
+echo "TP size:        $TP_SIZE"
+echo "Max model len:  $MAX_MODEL_LEN"
+echo "Port:           $PORT"
+echo "Num threads:    $NUM_THREADS"
+echo "============================================"
+
+# ---- Install bfcl-eval if missing ----
+if ! python3 -c "import bfcl_eval" 2>/dev/null; then
+    echo "Installing bfcl-eval..."
+    pip install "bfcl-eval>=2025.10.20.1,<2026"
+fi
+
+# ---- Cleanup handler ----
+SERVER_PID=""
+cleanup() {
+    if [ -n "$SERVER_PID" ]; then
+        echo "Stopping vLLM server (pid=$SERVER_PID)..."
+        kill "$SERVER_PID" 2>/dev/null || true
+        wait "$SERVER_PID" 2>/dev/null || true
+    fi
+    # Remove BFCL lock files (created by filelock for thread-safe writes)
+    rm -rf .file_locks/
+    if [ -n "${OUTPUT_DIR:-}" ]; then
+        rm -rf "$OUTPUT_DIR/.file_locks/"
+    fi
+}
+trap cleanup EXIT
+
+# ---- Start vLLM server ----
+echo "Starting vLLM server..."
+
+SERVE_ARGS=(
+    "$MODEL"
+    --port "$PORT"
+    --enable-auto-tool-choice
+    --tool-call-parser "$TOOL_CALL_PARSER"
+    --tensor-parallel-size "$TP_SIZE"
+    --max-model-len "$MAX_MODEL_LEN"
+    --enforce-eager
+    --no-enable-prefix-caching
+)
+
+# Append reasoning parser if specified
+if [ -n "$REASONING_PARSER" ]; then
+    SERVE_ARGS+=(--reasoning-parser "$REASONING_PARSER")
+fi
+
+# Append any extra args
+if [ -n "$EXTRA_ARGS" ]; then
+    read -ra EXTRA_ARGS_ARRAY <<< "$EXTRA_ARGS"
+    SERVE_ARGS+=("${EXTRA_ARGS_ARRAY[@]}")
+fi
+
+echo "Command: vllm serve ${SERVE_ARGS[*]}"
+vllm serve "${SERVE_ARGS[@]}" &
+SERVER_PID=$!
+
+# ---- Wait for server to be ready ----
+echo "Waiting for vLLM server to start (timeout: 600s)..."
+SECONDS_WAITED=0
+until curl -sf "http://localhost:${PORT}/health" > /dev/null 2>&1; do
+    if [ $SECONDS_WAITED -ge 600 ]; then
+        echo ""
+        echo "ERROR: vLLM server failed to start within 600s"
+        exit 1
+    fi
+    if (( SECONDS_WAITED % 30 == 0 && SECONDS_WAITED > 0 )); then
+        echo "  Still waiting... (${SECONDS_WAITED}s elapsed)"
+    fi
+    sleep 2
+    SECONDS_WAITED=$((SECONDS_WAITED + 2))
+done
+echo "vLLM server is ready. (started in ${SECONDS_WAITED}s)"
+
+# ---- Run BFCL evaluation ----
+# bfcl-eval has no CLI entry point; generate() and evaluate() are Typer
+# functions that must be called from Python. The MODEL_CONFIG_MAPPING must
+# be patched in-process so BFCL knows to use the OpenAI-compatible handler
+# against our local vLLM server.
+bfcl_exit_code=0
+python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$TEMPERATURE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
+import os
+import sys
+
+model = sys.argv[1]
+test_category = sys.argv[2]
+num_threads = int(sys.argv[3])
+port = sys.argv[4]
+api_type = sys.argv[5]
+temperature = float(sys.argv[6])
+output_dir = sys.argv[7] if len(sys.argv) > 7 and sys.argv[7] else os.getcwd()
+
+os.environ["OPENAI_BASE_URL"] = f"http://localhost:{port}/v1"
+os.environ["OPENAI_API_KEY"] = "dummy"
+os.environ["BFCL_PROJECT_ROOT"] = output_dir
+
+import bfcl_eval.constants.model_config as bfcl_model_config
+from bfcl_eval.constants.model_config import ModelConfig
+from bfcl_eval.model_handler.api_inference.openai_completion import (
+    OpenAICompletionsHandler,
+)
+from bfcl_eval.model_handler.api_inference.openai_response import (
+    OpenAIResponsesHandler,
+)
+
+if api_type == "responses":
+    handler = OpenAIResponsesHandler
+else:
+    handler = OpenAICompletionsHandler
+
+bfcl_model_config.MODEL_CONFIG_MAPPING[model] = ModelConfig(
+    model_name=model,
+    display_name=f"{model} (FC) (vLLM)",
+    url=f"https://huggingface.co/{model}",
+    org="",
+    license="apache-2.0",
+    model_handler=handler,
+    input_price=None,
+    output_price=None,
+    is_fc_model=True,
+    underscore_to_dot=True,
+)
+
+from bfcl_eval.__main__ import evaluate, generate
+import inspect
+import typer
+
+
+def _get_default_kwargs(function):
+    kwargs = {}
+    for k, v in inspect.signature(function).parameters.items():
+        if v.default is not inspect.Parameter.empty:
+            default = v.default
+            if isinstance(default, typer.models.OptionInfo):
+                default = default.default
+            kwargs[k] = default
+    return kwargs
+
+
+# ---- generate ----
+print(f"=== BFCL generate: model={model} test_category={test_category} ===")
+gen_kwargs = _get_default_kwargs(generate)
+gen_kwargs["model"] = [model]
+gen_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
+gen_kwargs["skip_server_setup"] = True
+gen_kwargs["num_threads"] = num_threads
+gen_kwargs["temperature"] = temperature
+generate(**gen_kwargs)
+
+# ---- evaluate ----
+print(f"=== BFCL evaluate: model={model} test_category={test_category} ===")
+eval_kwargs = _get_default_kwargs(evaluate)
+eval_kwargs["model"] = [model]
+eval_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
+evaluate(**eval_kwargs)
+
+print("=== BFCL evaluation completed successfully ===")
+PYEOF
+
+# ---- Upload results to buildkite ----
+if command -v buildkite-agent &>/dev/null; then
+    if [ $bfcl_exit_code -eq 0 ]; then
+        STYLE="success"
+        STATUS="PASSED"
+    else
+        STYLE="error"
+        STATUS="FAILED"
+    fi
+
+    buildkite-agent annotate --style "$STYLE" --context "bfcl-results" <<EOF
+### BFCL Tool Call Correctness - ${STATUS}
+- **Model:** \`${MODEL}\`
+- **Parser:** \`${TOOL_CALL_PARSER}\`
+- **API type:** \`${API_TYPE}\`
+- **Test category:** \`${TEST_CATEGORY}\`
+EOF
+
+    # BFCL writes results to $BFCL_PROJECT_ROOT/result/ and scores to
+    # $BFCL_PROJECT_ROOT/score/
+    RESULTS_ROOT="${OUTPUT_DIR:-.}"
+    if [ -d "$RESULTS_ROOT/result" ]; then
+        buildkite-agent artifact upload "$RESULTS_ROOT/result/**/*"
+    fi
+    if [ -d "$RESULTS_ROOT/score" ]; then
+        buildkite-agent artifact upload "$RESULTS_ROOT/score/**/*"
+    fi
+fi
+
+exit $bfcl_exit_code

.buildkite/scripts/upload-nightly-wheels.sh

@@ -2,27 +2,18 @@
 
 set -ex
 
-# ======== part 0: setup ========
+# Upload a single wheel to S3, after detecting and applying the appropriate
+# manylinux platform tag with auditwheel.
+# Index generation is handled separately by generate-and-upload-nightly-index.sh.
+
+# shellcheck source=lib/manylinux.sh
+source .buildkite/scripts/lib/manylinux.sh
 
 BUCKET="vllm-wheels"
-INDICES_OUTPUT_DIR="indices"
-DEFAULT_VARIANT_ALIAS="cu129" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
-PYTHON=${PYTHON_PROG:=python3} # try to read from env var, otherwise use python3
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# detect if python3.10+ is available
-has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
-if [[ "$has_new_python" -eq 0 ]]; then
-    # use new python from docker
-    docker pull python:3-slim
-    PYTHON="docker run --rm -v $(pwd):/app -w /app python:3-slim python3"
-fi
-
-echo "Using python interpreter: $PYTHON"
-echo "Python version: $($PYTHON --version)"
-
-# ========= part 1: collect, rename & upload the wheel ==========
+# ========= locate the wheel ==========
 
 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
@@ -34,74 +25,16 @@ if [[ ${#wheel_files[@]} -ne 1 ]]; then
 fi
 wheel="${wheel_files[0]}"
 
-# default build image uses ubuntu 20.04, which corresponds to manylinux_2_31
-# we also accept params as manylinux tag
-# refer to https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-distros-to-build-wheels
-manylinux_version="${1:-manylinux_2_31}"
+# ========= detect manylinux tag and rename ==========
 
-# Rename 'linux' to the appropriate manylinux version in the wheel filename
-if [[ "$wheel" != *"linux"* ]]; then
-  echo "Error: Wheel filename does not contain 'linux': $wheel"
-  exit 1
-fi
-new_wheel="${wheel/linux/$manylinux_version}"
-mv -- "$wheel" "$new_wheel"
-wheel="$new_wheel"
+wheel="$(apply_manylinux_tag "$wheel")"
 echo "Renamed wheel to: $wheel"
 
 # Extract the version from the wheel
 version=$(unzip -p "$wheel" '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
 echo "Version in wheel: $version"
-pure_version="${version%%+*}"
-echo "Pure version (without variant): $pure_version"
 
 # copy wheel to its own bucket
 aws s3 cp "$wheel" "$S3_COMMIT_PREFIX"
 
-# ========= part 2: generate and upload indices ==========
-# generate indices for all existing wheels in the commit directory
-# this script might be run multiple times if there are multiple variants being built
-# so we need to guarantee there is little chance for "TOCTOU" issues
-# i.e., one process is generating indices while another is uploading a new wheel
-# so we need to ensure no time-consuming operations happen below
-
-# list all wheels in the commit directory
-echo "Existing wheels on S3:"
-aws s3 ls "$S3_COMMIT_PREFIX"
-obj_json="objects.json"
-aws s3api list-objects-v2 --bucket "$BUCKET" --prefix "$SUBPATH/" --delimiter / --output json > "$obj_json"
-mkdir -p "$INDICES_OUTPUT_DIR"
-
-# call script to generate indicies for all existing wheels
-# this indices have relative paths that could work as long as it is next to the wheel directory in s3
-# i.e., the wheels are always in s3://vllm-wheels/<commit>/
-# and indices can be placed in /<commit>/, or /nightly/, or /<version>/
-alias_args=()
-if [[ -n "$DEFAULT_VARIANT_ALIAS" ]]; then
-    alias_args=(--alias-to-default "$DEFAULT_VARIANT_ALIAS")
-fi
-
-# HACK: we do not need regex module here, but it is required by pre-commit hook
-# To avoid any external dependency, we simply replace it back to the stdlib re module
-sed -i 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py
-$PYTHON .buildkite/scripts/generate-nightly-index.py --version "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "commit $BUILDKITE_COMMIT" "${alias_args[@]}"
-
-# copy indices to /<commit>/ unconditionally
-echo "Uploading indices to $S3_COMMIT_PREFIX"
-aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "$S3_COMMIT_PREFIX"
-
-# copy to /nightly/ only if it is on the main branch and not a PR 
-if [[ "$BUILDKITE_BRANCH" == "main" && "$BUILDKITE_PULL_REQUEST" == "false" ]]; then
-    echo "Uploading indices to overwrite /nightly/"
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/nightly/"
-fi
-
-# re-generate and copy to /<pure_version>/ only if it does not have "dev" in the version
-if [[ "$version" != *"dev"* ]]; then
-    echo "Re-generating indices for /$pure_version/"
-    rm -rf "${INDICES_OUTPUT_DIR:?}/*"
-    mkdir -p "$INDICES_OUTPUT_DIR"
-    # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
-    $PYTHON .buildkite/scripts/generate-nightly-index.py --version "$pure_version" --wheel-dir "$SUBPATH" --current-objects "$obj_json" --output-dir "$INDICES_OUTPUT_DIR" --comment "version $pure_version" "${alias_args[@]}"
-    aws s3 cp --recursive "$INDICES_OUTPUT_DIR/" "s3://$BUCKET/$pure_version/"
-fi
+echo "Wheel uploaded. Index generation is handled by a separate step."

.buildkite/scripts/upload-release-wheels-pypi.sh

@@ -39,10 +39,11 @@ fi
 
 set -x # avoid printing secrets above
 
-# install twine from pypi
+# install twine and sdist build prerequisites from pypi
 python3 -m venv /tmp/vllm-release-env
 source /tmp/vllm-release-env/bin/activate
 pip install twine
+pip install -r requirements/build/cuda.txt
 python3 -m twine --version
 
 # copy release wheels to local directory
@@ -54,10 +55,13 @@ mkdir -p $DIST_DIR
 # include only wheels for the release version, ignore all files with "dev" or "rc" in the name (without excluding 'aarch64')
 aws s3 cp --recursive --exclude "*" --include "vllm-${PURE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc[0-9]*" "$S3_COMMIT_PREFIX" $DIST_DIR
 echo "Wheels copied to local directory"
-# generate source tarball
-git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" "$BUILDKITE_COMMIT"
+# generate source distribution using setup.py
+python setup.py sdist --dist-dir=$DIST_DIR
 ls -la $DIST_DIR
 
+SDIST_FILE=$(find $DIST_DIR -name "vllm*.tar.gz")
+echo "Found sdist: $SDIST_FILE"
+
 # upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
 PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
 if [[ -z "$PYPI_WHEEL_FILES" ]]; then
@@ -65,6 +69,6 @@ if [[ -z "$PYPI_WHEEL_FILES" ]]; then
   exit 1
 fi
 
-python3 -m twine check "$PYPI_WHEEL_FILES"
-python3 -m twine upload --non-interactive --verbose "$PYPI_WHEEL_FILES"
-echo "Wheels uploaded to PyPI"
+python3 -m twine check "$PYPI_WHEEL_FILES" "$SDIST_FILE"
+python3 -m twine upload --non-interactive --verbose "$PYPI_WHEEL_FILES" "$SDIST_FILE"
+echo "Wheels and source distribution uploaded to PyPI"

.buildkite/scripts/upload-rocm-wheels.sh

@@ -20,10 +20,6 @@ BUCKET="${S3_BUCKET:-vllm-wheels}"
 ROCM_SUBPATH="rocm/${BUILDKITE_COMMIT}"
 S3_COMMIT_PREFIX="s3://$BUCKET/$ROCM_SUBPATH/"
 INDICES_OUTPUT_DIR="rocm-indices"
-PYTHON="${PYTHON_PROG:-python3}"
-
-# ROCm uses manylinux_2_35 (Ubuntu 22.04 based)
-MANYLINUX_VERSION="manylinux_2_35"
 
 echo "========================================"
 echo "ROCm Wheel Upload Configuration"
@@ -34,19 +30,21 @@ echo "Commit: $BUILDKITE_COMMIT"
 echo "Branch: $BUILDKITE_BRANCH"
 echo "========================================"
 
-# ======== Part 0: Setup Python ========
+# ======== Part 0: Setup Python and helpers ========
 
-# Detect if python3.12+ is available
-has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)" 2>/dev/null || echo 0)
-if [[ "$has_new_python" -eq 0 ]]; then
-    # Use new python from docker
-    # Use --user to ensure files are created with correct ownership (not root)
-    docker pull python:3-slim
-    PYTHON="docker run --rm --user $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
-fi
+# Pick a Python interpreter for index generation -- local if recent
+# enough, else a one-shot docker fallback.
+# shellcheck source=lib/select-python.sh
+source .buildkite/scripts/lib/select-python.sh
+select_python
 
-echo "Using python interpreter: $PYTHON"
-echo "Python version: $($PYTHON --version)"
+# Set up auditwheel-in-a-container for the manylinux retagging step.
+# Distinct from select_python: ``manylinux.sh`` deliberately pins both
+# the Python and auditwheel versions (the script reads auditwheel
+# internals) and so always runs in a known-good container regardless
+# of what's on the agent.
+# shellcheck source=lib/manylinux.sh
+source .buildkite/scripts/lib/manylinux.sh
 
 # ======== Part 1: Collect and prepare wheels ========
 
@@ -63,11 +61,18 @@ if [ "$WHEEL_COUNT" -eq 0 ]; then
     exit 1
 fi
 
-# Rename linux to manylinux in wheel filenames
+# Detect the appropriate manylinux platform tag for any wheel that still
+# carries the generic ``linux_<arch>`` tag, and rename it in place. We use
+# auditwheel via ``apply_manylinux_tag`` (see lib/manylinux.sh) rather than
+# a hard-coded ``manylinux_2_35`` string so that the label tracks the actual
+# glibc symbol versions used by the binaries (and stays correct if the
+# rocm_base image is rebased).
+#
+# The ``linux``/``manylinux`` filter below skips both pre-tagged wheels
+# (e.g. upstream torch) and pure-Python ``-any.whl`` wheels.
 for wheel in all-rocm-wheels/*.whl; do
     if [[ "$wheel" == *"linux"* ]] && [[ "$wheel" != *"manylinux"* ]]; then
-        new_wheel="${wheel/linux/$MANYLINUX_VERSION}"
-        mv -- "$wheel" "$new_wheel"
+        new_wheel="$(apply_manylinux_tag "$wheel")"
         echo "Renamed: $(basename "$wheel") -> $(basename "$new_wheel")"
     fi
 done

.buildkite/test_areas/attention.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: V1 attention (H100)
+  key: v1-attention-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -14,8 +15,9 @@ steps:
     - pytest -v -s v1/attention
 
 - label: V1 attention (B200)
+  key: v1-attention-b200
   timeout_in_minutes: 30
-  device: b200
+  device: b200-k8s
   source_file_dependencies:
     - vllm/config/attention.py
     - vllm/model_executor/layers/attention

.buildkite/test_areas/basic_correctness.yaml

@@ -3,9 +3,28 @@ depends_on:
   - image-build
 steps:
 - label: Basic Correctness
+  key: basic-correctness
   timeout_in_minutes: 30
+  device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/beam_search.py
+  - vllm/config/
+  - vllm/device_allocator/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/entrypoints/llm.py
+  - vllm/inputs/
+  - vllm/logging_utils/
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/sampling_params.py
+  - vllm/sequence.py
+  - vllm/tasks.py
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
+  - vllm/version.py
   - tests/basic_correctness/test_basic_correctness
   - tests/basic_correctness/test_cpu_offload
   - tests/basic_correctness/test_cumem.py
@@ -14,8 +33,3 @@ steps:
   - pytest -v -s basic_correctness/test_cumem.py
   - pytest -v -s basic_correctness/test_basic_correctness.py
   - pytest -v -s basic_correctness/test_cpu_offload.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd

.buildkite/test_areas/benchmarks.yaml

@@ -2,16 +2,10 @@ group: Benchmarks
 depends_on: 
   - image-build
 steps:
-- label: Benchmarks
-  timeout_in_minutes: 20
-  working_dir: "/vllm-workspace/.buildkite"
-  source_file_dependencies:
-  - benchmarks/
-  commands:
-  - bash scripts/run-benchmarks.sh
-
 - label: Benchmarks CLI Test
+  key: benchmarks-cli-test
   timeout_in_minutes: 20
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/benchmarks/
@@ -19,7 +13,8 @@ steps:
   - pytest -v -s benchmarks/
 
 - label: Attention Benchmarks Smoke Test (B200)
-  device: b200
+  key: attention-benchmarks-smoke-test-b200
+  device: b200-k8s
   num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/"

.buildkite/test_areas/compile.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Sequence Parallel Correctness Tests (2 GPUs)
+  key: sequence-parallel-correctness-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -17,6 +18,7 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: Sequence Parallel Correctness Tests (2xH100)
+  key: sequence-parallel-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -27,6 +29,7 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: AsyncTP Correctness Tests (2xH100)
+  key: asynctp-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -36,7 +39,19 @@ steps:
   - export VLLM_TEST_CLEAN_GPU_MEMORY=1
   - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
+- label: AsyncTP Correctness Tests (B200)
+  key: asynctp-correctness-tests-b200
+  timeout_in_minutes: 50
+  working_dir: "/vllm-workspace/"
+  device: b200-k8s
+  optional: true
+  num_devices: 2
+  commands:
+  - export VLLM_TEST_CLEAN_GPU_MEMORY=1
+  - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
+
 - label: Distributed Compile Unit Tests (2xH100)
+  key: distributed-compile-unit-tests-2xh100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -49,10 +64,11 @@ steps:
   - export VLLM_TEST_CLEAN_GPU_MEMORY=1
   - pytest -s -v tests/compile/passes/distributed
 
-- label: Fusion and Compile Unit Tests (B200)
+- label: Fusion and Compile Unit Tests (2xB200)
+  key: fusion-and-compile-unit-tests-2xb200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   source_file_dependencies:
   - csrc/quantization/fp4/
   - vllm/model_executor/layers/quantization/
@@ -62,6 +78,7 @@ steps:
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/compilation/ # TODO(luka) limit to vllm/compilation/passes
   - tests/compile/passes/test_fusion_attn.py
+  - tests/compile/passes/test_mla_attn_quant_fusion.py
   - tests/compile/passes/test_silu_mul_quant_fusion.py
   - tests/compile/passes/distributed/test_fusion_all_reduce.py
   - tests/compile/fullgraph/test_full_graph.py
@@ -69,6 +86,7 @@ steps:
     # b200 runners are limited, so we limit the tests to the minimum set only supported on Blackwell
     - nvidia-smi
     - pytest -v -s tests/compile/passes/test_fusion_attn.py -k FLASHINFER
+    - pytest -v -s tests/compile/passes/test_mla_attn_quant_fusion.py
     - pytest -v -s tests/compile/passes/test_silu_mul_quant_fusion.py
     # this runner has 2 GPUs available even though num_devices=2 is not set
     - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
@@ -77,6 +95,7 @@ steps:
     - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
 
 - label: Fusion E2E Quick (H100)
+  key: fusion-e2e-quick-h100
   timeout_in_minutes: 15
   working_dir: "/vllm-workspace/"
   device: h100
@@ -91,10 +110,11 @@ steps:
     - nvidia-smi
     # Run all models and attn backends but only Inductor partition and native custom ops
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and not +quant_fp8"
-    # Qwen requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
-    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and +quant_fp8 and qwen3"
+    # Qwen/Deepseek requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
+    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and +quant_fp8 and (qwen3 or deepseek)"
 
 - label: Fusion E2E Config Sweep (H100)
+  key: fusion-e2e-config-sweep-h100
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: h100
@@ -114,19 +134,21 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
 
 - label: Fusion E2E Config Sweep (B200)
+  key: fusion-e2e-config-sweep-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   num_devices: 1
   optional: true
   commands:
     - nvidia-smi
     # Run all models but only FLASHINFER, Inductor partition and native custom ops
-    # Qwen requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
+    # Qwen/Deepseek requires +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
     # Run just llama3 (fp8 & fp4) for all config combinations (only inductor partition)
-    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and (FLASHINFER and not +rms_norm and (not +quant_fp8 or +quant_fp8 and qwen3) or llama-3)"
+    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and (FLASHINFER and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek)) or llama-3)"
 
 - label: Fusion E2E TP2 Quick (H100)
+  key: fusion-e2e-tp2-quick-h100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -140,10 +162,11 @@ steps:
   commands:
     - nvidia-smi
     # Run all models and attn backends but only Inductor partition and native custom ops
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "inductor_partition and not +rms_norm and not +quant_fp8"
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "inductor_partition and not +rms_norm and not +quant_fp8"
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
 
 - label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
+  key: fusion-e2e-tp2-ar-rms-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -163,6 +186,7 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "llama-3"
 
 - label: Fusion E2E TP2 AsyncTP Config Sweep (H100)
+  key: fusion-e2e-tp2-asynctp-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -182,9 +206,10 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "llama-3"
 
 - label: Fusion E2E TP2 (B200)
+  key: fusion-e2e-tp2-b200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   num_devices: 2
   source_file_dependencies:
     - csrc/quantization/
@@ -195,7 +220,7 @@ steps:
   commands:
     - nvidia-smi
     # Run all models but only FLASHINFER, Inductor partition and native custom ops
-    # include qwen with +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
+    # include qwen/deepseek with +quant_fp8 as -quant_fp8 rms+quant fusion is not supported
     # for ar-rms-quant-fp4, also sweep llama3
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "(FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and qwen3)) or Llama-3.1-8B-Instruct-FP4"
-    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and qwen3)"
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "(FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))) or Llama-3.1-8B-Instruct-FP4"
+    - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "FLASHINFER and inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"

.buildkite/test_areas/cuda.yaml

@@ -3,15 +3,22 @@ depends_on:
   - image-build
 steps:
 - label: Platform Tests (CUDA)
+  key: platform-tests-cuda
   timeout_in_minutes: 15
+  device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/envs.py
+  - vllm/logger.py
+  - vllm/platforms/
+  - vllm/plugins/
+  - vllm/utils/
   - tests/cuda
   commands:
     - pytest -v -s cuda/test_cuda_context.py
     - pytest -v -s cuda/test_platform_no_cuda_init.py
 
 - label: Cudagraph
+  key: cudagraph
   timeout_in_minutes: 20
   source_file_dependencies:
   - tests/v1/cudagraph

.buildkite/test_areas/disaggregated.yaml

@@ -0,0 +1,106 @@
+group: Disaggregated
+depends_on: 
+  - image-build
+steps:
+- label: Distributed NixlConnector PD accuracy (4 GPUs)
+  key: distributed-nixlconnector-pd-accuracy-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+- label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
+  key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
+  key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
+  key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
+  key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+
+- label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
+  key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
+
+- label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
+  key: nixlconnector-pd-spec-decode-acceptance-2-gpus
+  timeout_in_minutes: 30
+  device: a100
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/v1/worker/kv_connector_model_runner_mixin.py
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+
+- label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
+  key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
+    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+    - tests/v1/kv_connector/nixl_integration/
+  commands:
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh

.buildkite/test_areas/distributed.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Comm Ops
+  key: distributed-comm-ops
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -15,8 +16,31 @@ steps:
   - pytest -v -s distributed/test_shm_buffer.py
   - pytest -v -s distributed/test_shm_storage.py
 
-- label: Distributed (2 GPUs)
-  timeout_in_minutes: 60
+- label: Distributed DP Tests (2 GPUs)
+  key: distributed-dp-tests-2-gpus
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/v1/distributed
+  - tests/entrypoints/openai/test_multi_api_servers.py
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
+  - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
+
+- label: Distributed Compile + RPC Tests (2 GPUs)
+  key: distributed-compile-rpc-tests-2-gpus
+  timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -29,61 +53,82 @@ steps:
   - vllm/v1/worker/
   - tests/compile/fullgraph/test_basic_correctness.py
   - tests/compile/test_wrapper.py
-  - tests/distributed/
   - tests/entrypoints/llm/test_collective_rpc.py
-  - tests/v1/distributed
-  - tests/v1/entrypoints/openai/test_multi_api_servers.py
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
+  - pytest -v -s entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s ./compile/test_wrapper.py
+
+- label: Distributed Torchrun + Shutdown Tests (2 GPUs)
+  key: distributed-torchrun-shutdown-tests-2-gpus
+  timeout_in_minutes: 20
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/distributed/
   - tests/v1/shutdown
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
-  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
-- label: Distributed Tests (4 GPUs)
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
+- label: Distributed Torchrun + Examples (4 GPUs)
+  key: distributed-torchrun-examples-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace"
   num_devices: 4
   source_file_dependencies:
   - vllm/distributed/
-  - tests/distributed/test_utils
-  - tests/distributed/test_pynccl
-  - tests/distributed/test_events
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - examples/offline_inference/rlhf.py
-  - examples/offline_inference/rlhf_colocate.py
-  - examples/offline_inference/new_weight_syncing/
-  - tests/examples/offline_inference/data_parallel.py
-  - tests/v1/distributed
-  - tests/v1/engine/test_engine_core_client.py
-  - tests/distributed/test_symm_mem_allreduce.py
+  - tests/distributed/test_torchrun_example.py
+  - tests/distributed/test_torchrun_example_moe.py
+  - examples/rl/
+  - tests/examples/features/data_parallel/data_parallel_offline.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and external_dp=2
-  - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  - torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
   # test with torchrun tp=2 and pp=2
-  - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  - PP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example.py
   # test with torchrun tp=4 and dp=1
-  - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - TP_SIZE=4 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=2, pp=2 and dp=1
-  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=1 and dp=4 with ep
-  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with torchrun tp=2 and dp=2 with ep
-  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with internal dp
-  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
+  - python3 examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  # rlhf examples
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
+  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
+
+- label: Distributed DP Tests (4 GPUs)
+  key: distributed-dp-tests-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/distributed/
+  - tests/v1/distributed
+  - tests/v1/engine/test_engine_core_client.py
+  - tests/distributed/test_utils
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
@@ -91,40 +136,52 @@ steps:
   - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
   - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
+
+- label: Distributed Compile + Comm (4 GPUs)
+  key: distributed-compile-comm-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/distributed/
+  - tests/distributed/test_pynccl
+  - tests/distributed/test_events
+  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/distributed/test_symm_mem_allreduce.py
+  - tests/distributed/test_multiproc_executor.py
+  commands:
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
   - pytest -v -s compile/fullgraph/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s distributed/test_events.py
   - pytest -v -s distributed/test_symm_mem_allreduce.py
-  # TODO: create a dedicated test section for multi-GPU example tests
-  # when we have multiple distributed example tests
-  # OLD rlhf examples
-  - cd ../examples/offline_inference
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
-  # NEW rlhf examples
-  - cd new_weight_syncing
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf.py
-  - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 rlhf_async_new_apis.py
+  # test multi-node TP with multiproc executor (simulated on single node)
+  - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
 - label: Distributed Tests (8 GPUs)(H100)
+  key: distributed-tests-8-gpus-h100
   timeout_in_minutes: 10
   device: h100
   num_devices: 8
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - examples/offline_inference/torchrun_dp_example.py
+  - examples/features/torchrun/torchrun_dp_example_offline.py
   - vllm/config/parallel.py
   - vllm/distributed/
   - vllm/v1/engine/llm_engine.py
   - vllm/v1/executor/uniproc_executor.py
   - vllm/v1/worker/gpu_worker.py
+  - tests/distributed/test_mnnvl_alltoall.py
+
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
+  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: Distributed Tests (4 GPUs)(A100)
+  key: distributed-tests-4-gpus-a100
   device: a100
   optional: true
   num_devices: 4
@@ -139,6 +196,7 @@ steps:
   - pytest -v -s -x lora/test_mixtral.py
 
 - label: Distributed Tests (2 GPUs)(H100)
+  key: distributed-tests-2-gpus-h100
   timeout_in_minutes: 15
   device: h100
   optional: true
@@ -146,11 +204,15 @@ steps:
   num_devices: 2
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
-    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
+    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
+    - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
+    - pytest -v -s tests/distributed/test_packed_tensor.py
 
 - label: Distributed Tests (2 GPUs)(B200)
-  device: b200
+  key: distributed-tests-2-gpus-b200
+  device: b200-k8s
   optional: true
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -158,8 +220,12 @@ steps:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
     - pytest -v -s tests/v1/distributed/test_dbo.py
+    - pytest -v -s tests/distributed/test_mnnvl_alltoall.py
+
+    
 
 - label: 2 Node Test (4 GPUs)
+  key: 2-node-test-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -172,44 +238,12 @@ steps:
   - vllm/executor/
   - vllm/model_executor/models/
   - tests/distributed/
-  - tests/examples/offline_inference/data_parallel.py
+  - tests/examples/features/data_parallel/data_parallel_offline.py
   commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
+    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
 
-- label: Distributed NixlConnector PD accuracy (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
-  timeout_in_minutes: 30
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 4
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
-
-- label: Pipeline + Context Parallelism (4 GPUs))
+- label: Pipeline + Context Parallelism (4 GPUs)
+  key: pipeline-context-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -222,3 +256,24 @@ steps:
   commands:
   - pytest -v -s distributed/test_pp_cudagraph.py
   - pytest -v -s distributed/test_pipeline_parallel.py
+
+- label: RayExecutorV2 (4 GPUs)
+  key: rayexecutorv2-4-gpus
+  timeout_in_minutes: 60
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/v1/executor/ray_executor_v2.py
+  - vllm/v1/executor/abstract.py
+  - vllm/v1/executor/multiproc_executor.py
+  - tests/distributed/test_ray_v2_executor.py
+  - tests/distributed/test_ray_v2_executor_e2e.py
+  - tests/distributed/test_pipeline_parallel.py
+  - tests/basic_correctness/test_basic_correctness.py
+  commands:
+  - export VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1
+  - export NCCL_CUMEM_HOST_ENABLE=0
+  - pytest -v -s distributed/test_ray_v2_executor.py
+  - pytest -v -s distributed/test_ray_v2_executor_e2e.py
+  - pytest -v -s distributed/test_pipeline_parallel.py -k "ray"
+  - TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -k "ray"

.buildkite/test_areas/docker.yaml

@@ -0,0 +1,16 @@
+group: Docker
+depends_on:
+  - image-build-cpu
+steps:
+- label: Docker Build Metadata
+  timeout_in_minutes: 10
+  device: cpu-small
+  source_file_dependencies:
+    - .buildkite/release-pipeline.yaml
+    - .buildkite/scripts/docker-build-metadata-args.sh
+    - docker/Dockerfile
+    - docker/Dockerfile.cpu
+    - docker/docker-bake.hcl
+    - tests/tools/test_docker_build_metadata_args.py
+  commands:
+    - pytest -v -s tools/test_docker_build_metadata_args.py

.buildkite/test_areas/e2e_integration.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: DeepSeek V2-Lite Accuracy
+  key: deepseek-v2-lite-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -12,6 +13,7 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy
+  key: qwen3-30b-a3b-fp8-block-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -21,23 +23,31 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
+  key: qwen3-30b-a3b-fp8-block-accuracy-b200
   timeout_in_minutes: 60
-  device: b200
+  device: b200-k8s
   optional: true
   num_devices: 2
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
-- label: Prime-RL Integration (2 GPUs)
-  timeout_in_minutes: 30
+- label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
+  key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
+  timeout_in_minutes: 60
+  device: h100
   optional: true
-  soft_fail: true
-  num_devices: 2
+  num_devices: 4
   working_dir: "/vllm-workspace"
-  source_file_dependencies:
-  - vllm/
-  - .buildkite/scripts/run-prime-rl-test.sh
   commands:
-    - nvidia-smi
-    - bash .buildkite/scripts/run-prime-rl-test.sh
+  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh 0.8 200 8050
+
+- label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
+  key: deepseek-v2-lite-prefetch-offload-accuracy-h100
+  timeout_in_minutes: 60
+  device: h100
+  optional: true
+  num_devices: 1
+  working_dir: "/vllm-workspace"
+  commands:
+  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh 0.25 200 8030

.buildkite/test_areas/engine.yaml

@@ -1,30 +1,126 @@
 group: Engine
-depends_on: 
+depends_on:
   - image-build
 steps:
 - label: Engine
+  key: engine
   timeout_in_minutes: 15
+  device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/compilation/
+  - vllm/config/
+  - vllm/engine/
+  - vllm/entrypoints/logger.py
+  - vllm/envs.py
+  - vllm/logger.py
+  - vllm/logging_utils/
+  - vllm/platforms/
+  - vllm/sequence.py
+  - vllm/triton_utils/
+  - vllm/utils/
   - tests/engine
   - tests/test_sequence
   - tests/test_config
   - tests/test_logger
   - tests/test_vllm_port
+  - tests/test_jit_monitor.py
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
 
-- label: V1 e2e + engine
-  timeout_in_minutes: 45
+- label: Engine (1 GPU)
+  key: engine-1-gpu
+  timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/
-    - tests/v1
+    - vllm/v1/engine/
+    - tests/v1/engine/
   commands:
-    # TODO: accuracy does not match, whether setting
-    # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
-    - pytest -v -s v1/e2e
-    # Run this test standalone for now;
-    # need to untangle use (implicit) use of spawn/fork across the tests.
     - pytest -v -s v1/engine/test_preprocess_error_handling.py
-    # Run the rest of v1/engine tests
     - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
+
+- label: e2e Scheduling (1 GPU)
+  key: e2e-scheduling-1-gpu
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/v1/
+    - tests/v1/e2e/general/
+  commands:
+    - pytest -v -s v1/e2e/general/test_async_scheduling.py
+
+- label: e2e Core (1 GPU)
+  key: e2e-core-1-gpu
+  timeout_in_minutes: 30
+  source_file_dependencies:
+    - vllm/v1/
+    - tests/v1/e2e/general/
+  commands:
+    - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
+
+- label: V1 e2e (2 GPUs)
+  key: v1-e2e-2-gpus
+  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/logging_utils/
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/e2e/spec_decode
+  commands:
+    # Only run tests that need exactly 2 GPUs
+    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
+
+- label: V1 e2e (4 GPUs)
+  key: v1-e2e-4-gpus
+  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
+  optional: true
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/logging_utils/
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/e2e/spec_decode
+  commands:
+    # Only run tests that need 4 GPUs
+    - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle_correctness_heavy"
+
+- label: V1 e2e (4xH100)
+  key: v1-e2e-4xh100
+  timeout_in_minutes: 60
+  device: h100
+  num_devices: 4
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/attention/backends/utils.py
+    - vllm/v1/worker/gpu_model_runner.py
+    - tests/v1/e2e/test_hybrid_chunked_prefill.py
+  commands:
+    - pytest -v -s v1/e2e/test_hybrid_chunked_prefill.py

.buildkite/test_areas/entrypoints.yaml

@@ -2,7 +2,8 @@ group: Entrypoints
 depends_on: 
   - image-build
 steps:
-- label: Entrypoints Unit Tests  
+- label: Entrypoints Unit Tests
+  key: entrypoints-unit-tests
   timeout_in_minutes: 10
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -10,9 +11,10 @@ steps:
   - tests/entrypoints/
   commands:
   - pytest -v -s entrypoints/openai/tool_parsers
-  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
+  - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
 
 - label: Entrypoints Integration (LLM)
+  key: entrypoints-integration-llm
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -26,12 +28,13 @@ steps:
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
   mirror:
     amd:
-      device: mi325_1
+      device: mi300_1
       depends_on:
       - image-build-amd
 
-- label: Entrypoints Integration (API Server 1)
-  timeout_in_minutes: 130
+- label: Entrypoints Integration (API Server openai - Part 1)
+  key: entrypoints-integration-api-server-openai-part-1
+  timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -39,24 +42,52 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/  --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses
+  - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
+
+
+- label: Entrypoints Integration (API Server openai - Part 2)
+  key: entrypoints-integration-api-server-openai-part-2
+  timeout_in_minutes: 50
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
+  commands:
+  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - pytest -v -s entrypoints/openai/speech_to_text/
   - pytest -v -s entrypoints/test_chat_utils.py
 
+- label: Entrypoints Integration (API Server openai - Part 3)
+  key: entrypoints-integration-api-server-openai-part-3
+  timeout_in_minutes: 50
+  device: h200_18gb
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
+
 - label: Entrypoints Integration (API Server 2)
+  key: entrypoints-integration-api-server-2
   timeout_in_minutes: 130
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
   - tests/entrypoints/rpc
-  - tests/entrypoints/instrumentator
+  - tests/entrypoints/serve/instrumentator
   - tests/tool_use
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/instrumentator
+  - pytest -v -s entrypoints/serve/instrumentator
   - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
   - pytest -v -s tool_use
 
 - label: Entrypoints Integration (Pooling)
+  key: entrypoints-integration-pooling
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -67,6 +98,7 @@ steps:
   - pytest -v -s entrypoints/pooling
 
 - label: Entrypoints Integration (Responses API)
+  key: entrypoints-integration-responses-api
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -75,16 +107,10 @@ steps:
   commands:
   - pytest -v -s entrypoints/openai/responses
 
-- label: Entrypoints V1
-  timeout_in_minutes: 50
-  source_file_dependencies:
-    - vllm/
-    - tests/v1
-  commands:
-    - pytest -v -s v1/entrypoints
-
 - label: OpenAI API Correctness
+  key: openai-api-correctness
   timeout_in_minutes: 30
+  device: h200_18gb
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/

.buildkite/test_areas/expert_parallelism.yaml

@@ -3,16 +3,21 @@ depends_on:
   - image-build
 steps:
 - label: EPLB Algorithm
+  key: eplb-algorithm
   timeout_in_minutes: 15
+  device: h200_18gb
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/distributed/eplb
   - tests/distributed/test_eplb_algo.py
+  - tests/distributed/test_eplb_utils.py
   commands:
   - pytest -v -s distributed/test_eplb_algo.py
+  - pytest -v -s distributed/test_eplb_utils.py
 
-- label: EPLB Execution
-  timeout_in_minutes: 20
+- label: EPLB Execution # 17min
+  key: eplb-execution
+  timeout_in_minutes: 27
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -20,4 +25,19 @@ steps:
   - tests/distributed/test_eplb_execute.py
   commands:
   - pytest -v -s distributed/test_eplb_execute.py
-  - pytest -v -s distributed/test_eplb_spec_decode.py
+  - pytest -v -s distributed/test_eplb_spec_decode.py
+
+- label: Elastic EP Scaling Test
+  key: elastic-ep-scaling-test
+  timeout_in_minutes: 20
+  device: h100
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/compilation/
+  - tests/distributed/
+  commands:
+  - pytest -v -s distributed/test_elastic_ep.py

.buildkite/test_areas/kernels.yaml

@@ -2,28 +2,58 @@ group: Kernels
 depends_on: 
   - image-build
 steps:
+- label: vLLM IR Tests
+  key: vllm-ir-tests
+  timeout_in_minutes: 10
+  device: h200_18gb
+  working_dir: "/vllm-workspace/"
+  source_file_dependencies:
+    - vllm/ir
+    - vllm/kernels
+  commands:
+    - pytest -v -s tests/ir
+    - pytest -v -s tests/kernels/ir
+
 - label: Kernels Core Operation Test
+  key: kernels-core-operation-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
-  - tests/kernels/test_top_k_per_row.py
+  - tests/kernels/test_concat_mla_q.py
   commands:
-    - pytest -v -s kernels/core kernels/test_top_k_per_row.py
+    - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py
+
+- label: Kernels MiniMax Reduce RMS Test (2 GPUs)
+  key: kernels-minimax-reduce-rms-test-2-gpus
+  timeout_in_minutes: 15
+  num_devices: 2
+  device: h100
+  source_file_dependencies:
+  - csrc/minimax_reduce_rms_kernel.cu
+  - csrc/minimax_reduce_rms_kernel.h
+  - vllm/model_executor/layers/mamba/linear_attn.py
+  - vllm/model_executor/layers/mamba/lamport_workspace.py
+  - tests/kernels/core/test_minimax_reduce_rms.py
+  commands:
+    - pytest -v -s kernels/core/test_minimax_reduce_rms.py
 
 - label: Kernels Attention Test %N
+  key: kernels-attention-test
   timeout_in_minutes: 35
   source_file_dependencies:
   - csrc/attention/
   - vllm/v1/attention
     # TODO: remove this dependency (https://github.com/vllm-project/vllm/issues/32267)
   - vllm/model_executor/layers/attention
+  - vllm/utils/flashinfer.py
   - tests/kernels/attention
   commands:
     - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
 
 - label: Kernels Quantization Test %N
+  key: kernels-quantization-test
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/quantization/
@@ -34,7 +64,8 @@ steps:
   parallelism: 2
 
 - label: Kernels MoE Test %N
-  timeout_in_minutes: 60
+  key: kernels-moe-test
+  timeout_in_minutes: 25
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
   - csrc/moe/
@@ -44,10 +75,12 @@ steps:
   - vllm/envs.py
   - vllm/config
   commands:
-    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
+    - pytest -v -s kernels/moe --ignore=kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+    - pytest -v -s kernels/moe/test_modular_oai_triton_moe.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 5
 
 - label: Kernels Mamba Test
+  key: kernels-mamba-test
   timeout_in_minutes: 45
   source_file_dependencies:
   - csrc/mamba/
@@ -56,7 +89,18 @@ steps:
   commands:
     - pytest -v -s kernels/mamba
 
+- label: Kernels KDA Test
+  timeout_in_minutes: 20
+  source_file_dependencies:
+  - vllm/model_executor/layers/fla/ops/kda.py
+  - vllm/model_executor/layers/fla/ops/chunk_delta_h.py
+  - vllm/model_executor/layers/fla/ops/l2norm.py
+  - tests/kernels/test_kda.py
+  commands:
+    - pytest -v -s kernels/test_kda.py
+
 - label: Kernels DeepGEMM Test (H100)
+  key: kernels-deepgemm-test-h100
   timeout_in_minutes: 45
   device: h100
   num_devices: 1
@@ -69,16 +113,19 @@ steps:
   - tests/kernels/moe/test_deepgemm.py
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
+  - tests/quantization/test_cutlass_w4a16.py
   commands:
-    - pytest -v -s kernels/quantization/test_block_fp8.py -k deep_gemm
+    - pytest -v -s kernels/quantization/test_block_fp8.py
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
+    - pytest -v -s quantization/test_cutlass_w4a16.py
 
 - label: Kernels (B200)
+  key: kernels-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   # optional: true
   source_file_dependencies:
   - csrc/quantization/fp4/
@@ -93,9 +140,10 @@ steps:
   - vllm/v1/attention/backends/mla/flashinfer_mla.py
   - vllm/v1/attention/selector.py
   - vllm/platforms/cuda.py
+  - tests/kernels/test_top_k_per_row.py
   commands:
     - nvidia-smi
-    - python3 examples/offline_inference/basic/chat.py
+    - python3 examples/basic/offline_inference/chat.py
     # Attention
     # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
     - pytest -v -s tests/kernels/attention/test_attention_selector.py
@@ -103,6 +151,7 @@ steps:
     - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_attention.py
     - pytest -v -s tests/kernels/attention/test_cutlass_mla_decode.py
     - pytest -v -s tests/kernels/attention/test_flashinfer_mla_decode.py
+    - pytest -v -s tests/kernels/test_top_k_per_row.py
     # Quantization
     - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
     - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
@@ -113,24 +162,28 @@ steps:
     - pytest -v -s tests/kernels/quantization/test_nvfp4_qutlass.py
     - pytest -v -s tests/kernels/quantization/test_mxfp4_qutlass.py
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
+    - pytest -v -s tests/kernels/moe/test_mxfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
+    - pytest -v -s tests/kernels/moe/test_flashinfer_moe.py
     - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
     # e2e
     - pytest -v -s tests/models/quantization/test_nvfp4.py
 
 - label: Kernels Helion Test
+  key: kernels-helion-test
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
   - vllm/utils/import_utils.py
   - tests/kernels/helion/
   commands:
-    - pip install helion
+    - pip install helion==1.0.0
     - pytest -v -s kernels/helion/
 
  
 - label: Kernels FP8 MoE Test (1 H100)
+  key: kernels-fp8-moe-test-1-h100
   timeout_in_minutes: 90
   device: h100
   num_devices: 1
@@ -147,6 +200,7 @@ steps:
     - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
 
 - label: Kernels FP8 MoE Test (2 H100s)
+  key: kernels-fp8-moe-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -154,12 +208,11 @@ steps:
   commands:
     - pytest -v -s kernels/moe/test_deepep_deepgemm_moe.py
     - pytest -v -s kernels/moe/test_deepep_moe.py
-    - pytest -v -s kernels/moe/test_pplx_cutlass_moe.py
-    # - pytest -v -s kernels/moe/test_pplx_moe.py - failing on main
-  
+
 - label: Kernels Fp4 MoE Test (B200)
+  key: kernels-fp4-moe-test-b200
   timeout_in_minutes: 60
-  device: b200
+  device: b200-k8s
   num_devices: 1
   optional: true
   commands:
@@ -167,3 +220,37 @@ steps:
     - pytest -v -s kernels/moe/test_flashinfer_moe.py
     - pytest -v -s kernels/moe/test_nvfp4_moe.py
     - pytest -v -s kernels/moe/test_ocp_mx_moe.py
+
+
+- label: Kernels FusedMoE Layer Test (2 H100s)
+  key: kernels-fusedmoe-layer-test-2-h100s
+  timeout_in_minutes: 90
+  device: h100
+  num_devices: 2
+  source_file_dependencies:
+  - csrc/quantization/cutlass_w8a8/moe/
+  - csrc/moe/
+  - tests/kernels/moe
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
+  - vllm/distributed/device_communicators/
+  - vllm/config
+  commands:
+    - pytest -v -s kernels/moe/test_moe_layer.py
+
+
+- label: Kernels FusedMoE Layer Test (2 B200s)
+  key: kernels-fusedmoe-layer-test-2-b200s
+  timeout_in_minutes: 90
+  device: b200
+  num_devices: 2
+  source_file_dependencies:
+  - csrc/quantization/cutlass_w8a8/moe/
+  - csrc/moe/
+  - tests/kernels/moe
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
+  - vllm/distributed/device_communicators/
+  - vllm/config
+  commands:
+    - pytest -v -s kernels/moe/test_moe_layer.py

.buildkite/test_areas/lm_eval.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: LM Eval Small Models
+  key: lm-eval-small-models
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -11,19 +12,20 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
 
-- label: LM Eval Large Models (4 GPUs)(A100)
-  device: a100
-  optional: true
-  num_devices: 4
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
+# - label: LM Eval Large Models (4 GPUs)(A100)
+#   device: a100
+#   optional: true
+#   num_devices: 4
+#   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+#   source_file_dependencies:
+#   - csrc/
+#   - vllm/model_executor/layers/quantization
+#   commands:
+#   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 - label: LM Eval Large Models (4 GPUs)(H100)
+  key: lm-eval-large-models-4-gpus-h100
   device: h100
   optional: true
   num_devices: 4
@@ -36,6 +38,7 @@ steps:
     - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
 
 - label: LM Eval Small Models (B200)
+  key: lm-eval-small-models-b200
   timeout_in_minutes: 120
   device: b200
   optional: true
@@ -45,7 +48,25 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
 
+- label: LM Eval Qwen3.5 Models (B200)
+  key: lm-eval-qwen3-5-models-b200
+  timeout_in_minutes: 120
+  device: b200-k8s
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/model_executor/models/qwen3_5.py
+  - vllm/model_executor/models/qwen3_5_mtp.py
+  - vllm/transformers_utils/configs/qwen3_5.py
+  - vllm/transformers_utils/configs/qwen3_5_moe.py
+  - vllm/model_executor/models/qwen3_next.py
+  - vllm/model_executor/models/qwen3_next_mtp.py
+  - vllm/model_executor/layers/fla/ops/
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-blackwell.txt
+
 - label: LM Eval Large Models (H200)
+  key: lm-eval-large-models-h200
   timeout_in_minutes: 60
   device: h200
   optional: true
@@ -54,6 +75,7 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
 
 - label: MoE Refactor Integration Test (H100 - TEMPORARY)
+  key: moe-refactor-integration-test-h100-temporary
   device: h100
   optional: true
   num_devices: 2
@@ -61,15 +83,64 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
   
 - label: MoE Refactor Integration Test (B200 - TEMPORARY)
-  device: b200
+  key: moe-refactor-integration-test-b200-temporary
+  device: b200-k8s
   optional: true
   num_devices: 2
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
 
 - label: MoE Refactor Integration Test (B200 DP - TEMPORARY)
+  key: moe-refactor-integration-test-b200-dp-temporary
   device: b200
   optional: true
   num_devices: 2
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor-dp-ep/config-b200.txt
+
+
+- label: LM Eval TurboQuant KV Cache
+  key: lm-eval-turboquant-kv-cache
+  timeout_in_minutes: 75
+  source_file_dependencies:
+  - vllm/model_executor/layers/quantization/turboquant/
+  - vllm/v1/attention/backends/turboquant_attn.py
+  - vllm/v1/attention/ops/triton_turboquant_decode.py
+  - vllm/v1/attention/ops/triton_turboquant_store.py
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/models-turboquant.txt
+
+- label: GPQA Eval (GPT-OSS) (H100)
+  key: gpqa-eval-gpt-oss-h100
+  timeout_in_minutes: 120
+  device: h100
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - tests/evals/gpt_oss/
+  commands:
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-h100.txt
+
+- label: GPQA Eval (GPT-OSS) (B200)
+  key: gpqa-eval-gpt-oss-b200
+  timeout_in_minutes: 120
+  device: b200
+  optional: true
+  num_devices: 2
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - tests/evals/gpt_oss/
+  commands:
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt
+
+- label: MRCR Eval Small Models
+  timeout_in_minutes: 30
+  source_file_dependencies:
+  - tests/evals/mrcr/
+  commands:
+    - pytest -s -v evals/mrcr/test_mrcr_correctness.py --config-list-file=evals/mrcr/configs/models-small.txt

.buildkite/test_areas/lora.yaml

@@ -3,20 +3,23 @@ depends_on:
   - image-build
 steps:
 - label: LoRA %N
+  key: lora
   timeout_in_minutes: 30
   source_file_dependencies:
   - vllm/lora
   - tests/lora
   commands:
-    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py
+    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py 
   parallelism: 4
 
 
 - label: LoRA TP (Distributed)
+  key: lora-tp-distributed
   timeout_in_minutes: 30
   num_devices: 4
   source_file_dependencies:
   - vllm/lora
+  - vllm/model_executor/layers/fused_moe/
   - tests/lora
   commands:
     # FIXIT: find out which code initialize cuda before running the test
@@ -30,4 +33,5 @@ steps:
     - pytest -v -s -x lora/test_llama_tp.py
     - pytest -v -s -x lora/test_llm_with_multi_loras.py
     - pytest -v -s -x lora/test_olmoe_tp.py
-    - pytest -v -s -x lora/test_gptoss_tp.py
+    - pytest -v -s -x lora/test_gptoss_tp.py
+    - pytest -v -s -x lora/test_qwen35_densemodel_lora.py

.buildkite/test_areas/misc.yaml

@@ -2,37 +2,123 @@ group: Miscellaneous
 depends_on: 
   - image-build
 steps:
-- label: V1 Others
-  timeout_in_minutes: 60
+- label: V1 Spec Decode
+  key: v1-spec-decode
+  timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/
-    - tests/v1
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/inputs/
+    - vllm/model_executor/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/spec_decode
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    # TODO: create another `optional` test group for slow tests
+    - pytest -v -s -m 'not slow_test' v1/spec_decode
+
+- label: V1 Sample + Logits
+  key: v1-sample-logits
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/sample
+    - tests/v1/logits_processors
+    - tests/v1/test_oracle.py
+    - tests/v1/test_request.py
+    - tests/v1/test_outputs.py
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pytest -v -s v1/sample
+    - pytest -v -s v1/logits_processors
+    - pytest -v -s v1/test_oracle.py
+    - pytest -v -s v1/test_request.py
+    - pytest -v -s v1/test_outputs.py
+  mirror:
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
+
+- label: V1 Core + KV + Metrics
+  key: v1-core-kv-metrics
+  timeout_in_minutes: 30
+  source_file_dependencies:
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/entrypoints/pooling/
+    - vllm/inputs/
+    - vllm/lora/
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/outputs.py
+    - vllm/platforms/
+    - vllm/pooling_params.py
+    - vllm/profiler/
+    - vllm/sampling_params.py
+    - vllm/tokenizers/
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/core
+    - tests/v1/executor
+    - tests/v1/kv_offload
+    - tests/v1/worker
+    - tests/v1/kv_connector/unit
+    - tests/v1/metrics
+    - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # split the test to avoid interference
     - pytest -v -s -m 'not cpu_test' v1/core
     - pytest -v -s v1/executor
     - pytest -v -s v1/kv_offload
-    - pytest -v -s v1/sample
-    - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker
-    - pytest -v -s -m 'not slow_test' v1/spec_decode
     - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
     - pytest -v -s -m 'not cpu_test' v1/metrics
-    - pytest -v -s v1/test_oracle.py
-    - pytest -v -s v1/test_request.py
-    - pytest -v -s v1/test_outputs.py
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
 - label: V1 Others (CPU)
+  key: v1-others-cpu
   depends_on:
     - image-build-cpu
   source_file_dependencies:
-    - vllm/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/inputs/
+    - vllm/lora/
+    - vllm/multimodal/
+    - vllm/outputs.py
+    - vllm/platforms/
+    - vllm/pooling_params.py
+    - vllm/profiler/
+    - vllm/sampling_params.py
+    - vllm/tokenizers/
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/v1
-  device: cpu
+  device: cpu-small
   commands:
     # split the test to avoid interference
     - pytest -v -s -m 'cpu_test' v1/core
@@ -42,9 +128,21 @@ steps:
     - pytest -v -s -m 'cpu_test' v1/metrics
 
 - label: Regression
+  key: regression
   timeout_in_minutes: 20
+  device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/inputs/
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/sampling_params.py
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/test_regression
   commands:
   - pip install modelscope
@@ -52,6 +150,7 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Examples
+  key: examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
@@ -60,32 +159,45 @@ steps:
   - examples/
   commands:
     - pip install tensorizer # for tensorizer test
-    - python3 offline_inference/basic/chat.py # for basic
-    - python3 offline_inference/basic/generate.py --model facebook/opt-125m
-    - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
-    - python3 offline_inference/basic/classify.py
-    - python3 offline_inference/basic/embed.py
-    - python3 offline_inference/basic/score.py
+     # for basic
+    - python3 basic/offline_inference/chat.py
+    - python3 basic/offline_inference/generate.py --model facebook/opt-125m
+    - python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
+    - python3 basic/offline_inference/classify.py
+    - python3 basic/offline_inference/embed.py
+    - python3 basic/offline_inference/score.py
     # for multi-modal models
-    - python3 offline_inference/audio_language.py --seed 0
-    - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
      # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 deployment/llm_engine_example.py
+    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Metrics, Tracing (2 GPUs)
+  key: metrics-tracing-2-gpus
   timeout_in_minutes: 20
   num_devices: 2
   source_file_dependencies:
-  - vllm/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/inputs/
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/sampling_params.py
+  - vllm/tracing/
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/v1/tracing
   commands:
   - "pip install \
@@ -96,6 +208,7 @@ steps:
   - pytest -v -s v1/tracing
 
 - label: Python-only Installation
+  key: python-only-installation
   depends_on: ~
   timeout_in_minutes: 20
   source_file_dependencies:
@@ -105,9 +218,22 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Async Engine, Inputs, Utils, Worker
+  key: async-engine-inputs-utils-worker
   timeout_in_minutes: 50
   source_file_dependencies:
-  - vllm/
+  - vllm/assets/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/inputs/
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/sampling_params.py
+  - vllm/tokenizers/
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/detokenizer
   - tests/multimodal
   - tests/utils_
@@ -117,11 +243,35 @@ steps:
   - pytest -v -s utils_
 
 - label: Async Engine, Inputs, Utils, Worker, Config (CPU)
-  depends_on: 
+  key: async-engine-inputs-utils-worker-config-cpu
+  depends_on:
   - image-build-cpu
   timeout_in_minutes: 30
   source_file_dependencies:
-  - vllm/
+  - vllm/assets/
+  - vllm/config/
+  - vllm/engine/arg_utils.py
+  - vllm/entrypoints/chat_utils.py
+  - vllm/entrypoints/mcp/
+  - vllm/entrypoints/openai/chat_completion/protocol.py
+  - vllm/entrypoints/openai/engine/protocol.py
+  - vllm/envs.py
+  - vllm/exceptions.py
+  - vllm/inputs/
+  - vllm/model_executor/layers/quantization/quark/
+  - vllm/multimodal/
+  - vllm/outputs.py
+  - vllm/platforms/
+  - vllm/pooling_params.py
+  - vllm/ray/
+  - vllm/reasoning/
+  - vllm/renderers/
+  - vllm/sampling_params.py
+  - vllm/tokenizers/
+  - vllm/tool_parsers/
+  - vllm/transformers_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/test_pooling_params.py
@@ -130,10 +280,11 @@ steps:
   - tests/renderers
   - tests/standalone_tests/lazy_imports.py
   - tests/tokenizers_
+  - tests/reasoning
   - tests/tool_parsers
   - tests/transformers_utils
   - tests/config
-  device: cpu
+  device: cpu-small
   commands:
   - python3 standalone_tests/lazy_imports.py
   - pytest -v -s test_inputs.py
@@ -143,26 +294,14 @@ steps:
   - pytest -v -s -m 'cpu_test' multimodal
   - pytest -v -s renderers
   - pytest -v -s tokenizers_
+  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
-- label: GPT-OSS Eval (B200)
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/"
-  device: b200
-  optional: true
-  source_file_dependencies:
-  - tests/evals/gpt_oss
-  - vllm/model_executor/models/gpt_oss.py
-  - vllm/model_executor/layers/quantization/mxfp4.py
-  - vllm/v1/attention/backends/flashinfer.py
-  commands:
-    - uv pip install --system 'gpt-oss[eval]==0.0.5'
-    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
-
 - label: Batch Invariance (H100)
-  timeout_in_minutes: 25
+  key: batch-invariance-h100
+  timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
     - vllm/v1/attention
@@ -173,8 +312,28 @@ steps:
     - pip install pytest-timeout pytest-forked
     - pytest -v -s v1/determinism/test_batch_invariance.py
     - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
+    - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
+
+- label: Batch Invariance (B200)
+  key: batch-invariance-b200
+  timeout_in_minutes: 30
+  device: b200-k8s
+  source_file_dependencies:
+    - vllm/v1/attention
+    - vllm/model_executor/layers
+    - tests/v1/determinism/
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - pip install pytest-timeout pytest-forked
+    - pytest -v -s v1/determinism/test_batch_invariance.py
+    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
+    - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
+    - pytest -v -s v1/determinism/test_nvfp4_batch_invariant.py
   
 - label: Acceptance Length Test (Large Models) # optional
+  key: acceptance-length-test-large-models
   timeout_in_minutes: 25
   gpu: h100
   optional: true

.buildkite/test_areas/model_executor.yaml

@@ -3,15 +3,16 @@ depends_on:
   - image-build
 steps:
 - label: Model Executor
+  key: model-executor
   timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/engine/arg_utils.py
   - vllm/config/model.py
   - vllm/model_executor
   - tests/model_executor
-  - tests/entrypoints/openai/test_tensorizer_entrypoint.py
+  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
   commands:
     - apt-get update && apt-get install -y curl libsodium23
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s model_executor
-    - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py
+    - pytest -v -s model_executor -m '(not slow_test)'
+    - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py

.buildkite/test_areas/model_runner_v2.yaml

@@ -0,0 +1,118 @@
+group: Model Runner V2
+depends_on:
+  - image-build
+steps:
+- label: Model Runner V2 Core Tests
+  key: model-runner-v2-core-tests
+  timeout_in_minutes: 45
+  source_file_dependencies:
+  - vllm/v1/worker/gpu/
+  - vllm/v1/worker/gpu_worker.py
+  - vllm/v1/core/sched/
+  - vllm/v1/attention/
+  - tests/v1/engine/test_llm_engine.py
+  - tests/v1/e2e/
+  - tests/entrypoints/llm/test_struct_output_generate.py
+  commands:
+  - set -x
+  - export VLLM_USE_V2_MODEL_RUNNER=1
+  - pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics"
+  # This requires eager until we sort out CG correctness issues.
+  # TODO: remove ENFORCE_EAGER here after https://github.com/vllm-project/vllm/pull/32936 is merged.
+  - ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
+  - pytest -v -s v1/e2e/general/test_context_length.py
+  - pytest -v -s v1/e2e/general/test_min_tokens.py
+  # Temporary hack filter to exclude ngram spec decoding based tests.
+  - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
+
+- label: Model Runner V2 Examples
+  key: model-runner-v2-examples
+  timeout_in_minutes: 45
+  working_dir: "/vllm-workspace/examples"
+  source_file_dependencies:
+    - vllm/v1/worker/gpu/
+    - vllm/v1/core/sched/
+    - vllm/v1/worker/gpu_worker.py
+    - examples/basic/offline_inference/
+    - examples/generate/multimodal/
+    - examples/features/
+    - examples/pooling/embed/vision_embedding_offline.py
+    - examples/features/tensorize_vllm_model.py
+  commands:
+    - set -x
+    - export VLLM_USE_V2_MODEL_RUNNER=1
+    - pip install tensorizer # for tensorizer test
+    - python3 basic/offline_inference/chat.py # for basic
+    - python3 basic/offline_inference/generate.py --model facebook/opt-125m
+    #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
+    #- python3 basic/offline_inference/embed.py   # TODO
+    # for multi-modal models
+    - python3 generate/multimodal/audio_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_offline.py --seed 0
+    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
+    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    # for pooling models
+    - python3 pooling/embed/vision_embedding_offline.py --seed 0
+    # for features demo
+    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
+    - python3 deployment/llm_engine_example.py
+    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
+    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
+
+- label: Model Runner V2 Distributed (2 GPUs)
+  key: model-runner-v2-distributed-2-gpus
+  timeout_in_minutes: 45
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 2
+  source_file_dependencies:
+    - vllm/v1/worker/gpu/
+    - vllm/v1/worker/gpu_worker.py
+    - tests/basic_correctness/test_basic_correctness.py
+    - tests/v1/distributed/test_async_llm_dp.py
+    - tests/v1/distributed/test_eagle_dp.py
+  commands:
+    - set -x
+    - export VLLM_USE_V2_MODEL_RUNNER=1
+    # The "and not True" here is a hacky way to exclude the prompt_embeds cases which aren't yet supported.
+    - TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -m 'distributed(num_gpus=2)' -k "not ray and not True"
+    # https://github.com/NVIDIA/nccl/issues/1838
+    - export NCCL_CUMEM_HOST_ENABLE=0
+    - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py -k "not ray"
+    - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
+
+- label: Model Runner V2 Pipeline Parallelism (4 GPUs)
+  key: model-runner-v2-pipeline-parallelism-4-gpus
+  timeout_in_minutes: 60
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/v1/worker/gpu/
+    - vllm/v1/worker/gpu_worker.py
+    - tests/distributed/test_pipeline_parallel.py
+    - tests/distributed/test_pp_cudagraph.py
+  commands:
+    - set -x
+    - export VLLM_USE_V2_MODEL_RUNNER=1
+    - pytest -v -s distributed/test_pipeline_parallel.py -k "not ray and not Jamba"
+    - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
+
+- label: Model Runner V2 Spec Decode
+  key: model-runner-v2-spec-decode
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/v1/worker/gpu/
+  - vllm/v1/worker/gpu_worker.py
+  - tests/v1/spec_decode/test_max_len.py
+  - tests/v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
+  - tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
+  - tests/v1/e2e/spec_decode/test_spec_decode.py
+  commands:
+  - set -x
+  - export VLLM_USE_V2_MODEL_RUNNER=1
+  - pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
+  - pytest -v -s v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
+  - pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
+  - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"

.buildkite/test_areas/models_basic.yaml

@@ -1,8 +1,9 @@
 group: Models - Basic
-depends_on: 
+depends_on:
   - image-build
 steps:
 - label: Basic Models Tests (Initialization)
+  key: basic-models-tests-initialization
   timeout_in_minutes: 45
   torch_nightly: true
   source_file_dependencies:
@@ -12,10 +13,12 @@ steps:
   commands:
     # Run a subset of model initialization tests
     - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset
+  mirror:
+    torch_nightly: {}
 
 - label: Basic Models Tests (Extra Initialization) %N
+  key: basic-models-tests-extra-initialization
   timeout_in_minutes: 45
-  torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
   - tests/models/test_initialization.py
@@ -26,8 +29,11 @@ steps:
     # test.) Also run if model initialization test file is modified
     - pytest -v -s models/test_initialization.py -k 'not test_can_initialize_small_subset' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
   parallelism: 2
+  mirror:
+    torch_nightly: {}
 
 - label: Basic Models Tests (Other)
+  key: basic-models-tests-other
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -36,26 +42,22 @@ steps:
   - tests/models/test_registry.py
   commands:
     - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-    
 
 - label: Basic Models Test (Other CPU) # 5min
-  depends_on: 
+  key: basic-models-test-other-cpu
+  depends_on:
   - image-build-cpu
   timeout_in_minutes: 10
   source_file_dependencies:
   - vllm/
   - tests/models/test_utils.py
   - tests/models/test_vision.py
-  device: cpu
+  device: cpu-small
   commands:
     - pytest -v -s models/test_utils.py models/test_vision.py
 
 - label: Transformers Nightly Models
+  key: transformers-nightly-models
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -65,7 +67,23 @@ steps:
     - pytest -v -s tests/models/test_transformers.py
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    - python3 examples/basic/offline_inference/chat.py
+    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+
+- label: Transformers Backward Compatibility Models Test
+  key: transformers-backward-compatibility-models-test
+  working_dir: "/vllm-workspace/"
+  optional: true
+  soft_fail: true
+  commands:
+    - pip install transformers==4.57.5
+    - pytest -v -s tests/models/test_initialization.py
+    - pytest -v -s tests/models/test_transformers.py
+    - pytest -v -s tests/models/multimodal/processing/
+    - pytest -v -s tests/models/multimodal/test_mapping.py
+    - python3 examples/basic/offline_inference/chat.py
+    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    # Whisper needs spawn method to avoid deadlock
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper

.buildkite/test_areas/models_distributed.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Model Tests (2 GPUs)
+  key: distributed-model-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -14,9 +15,10 @@ steps:
   - tests/models/
   commands:
   - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
   # Avoid importing model tests that cause CUDA reinitialization error
   - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/language -v -s -m 'distributed(num_gpus=2)'
-  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
+  - pytest models/multimodal/generation/test_phi4siglip.py -v -s -m 'distributed(num_gpus=2)'
+  - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/generation/test_phi4siglip.py
   - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'

.buildkite/test_areas/models_language.yaml

@@ -1,10 +1,10 @@
 group: Models - Language
-depends_on: 
+depends_on:
   - image-build
 steps:
 - label: Language Models Tests (Standard)
+  key: language-models-tests-standard
   timeout_in_minutes: 25
-  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models/language
@@ -12,10 +12,12 @@ steps:
     # Test standard language models, excluding a subset of slow tests
     - pip freeze | grep -E 'torch'
     - pytest -v -s models/language -m 'core_model and (not slow_test)'
+  mirror:
+    torch_nightly: {}
 
 - label: Language Models Tests (Extra Standard) %N
+  key: language-models-tests-extra-standard
   timeout_in_minutes: 45
-  torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
   - tests/models/language/pooling/test_embedding.py
@@ -27,10 +29,12 @@ steps:
     - pip freeze | grep -E 'torch'
     - pytest -v -s models/language -m 'core_model and slow_test' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
   parallelism: 2
+  mirror:
+    torch_nightly: {}
 
 - label: Language Models Tests (Hybrid) %N
+  key: language-models-tests-hybrid
   timeout_in_minutes: 75
-  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/models/language/generation
@@ -38,12 +42,23 @@ steps:
     # Install fast path packages for testing against transformers
     # Note: also needed to run plamo2 model in vLLM
     - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     # Shard hybrid language model tests
     - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
   parallelism: 2
+  mirror:
+    torch_nightly: {}
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
+      commands:
+      - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+      - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+      - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
 - label: Language Models Test (Extended Generation) # 80min
+  key: language-models-test-extended-generation
   timeout_in_minutes: 110
   optional: true
   source_file_dependencies:
@@ -53,11 +68,13 @@ steps:
     # Install fast path packages for testing against transformers
     # Note: also needed to run plamo2 model in vLLM
     - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
+    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
 
 - label: Language Models Test (PPL)
+  key: language-models-test-ppl
   timeout_in_minutes: 110
+  device: h200_18gb
   optional: true
   source_file_dependencies:
   - vllm/
@@ -66,6 +83,7 @@ steps:
     - pytest -v -s models/language/generation_ppl_test
 
 - label: Language Models Test (Extended Pooling)  # 36min
+  key: language-models-test-extended-pooling
   timeout_in_minutes: 50
   optional: true
   source_file_dependencies:
@@ -75,7 +93,9 @@ steps:
     - pytest -v -s models/language/pooling -m 'not core_model'
 
 - label: Language Models Test (MTEB)
+  key: language-models-test-mteb
   timeout_in_minutes: 110
+  device: h200_18gb
   optional: true
   source_file_dependencies:
   - vllm/

.buildkite/test_areas/models_multimodal.yaml

@@ -2,39 +2,97 @@ group: Models - Multimodal
 depends_on: 
   - image-build
 steps:
-- label: Multi-Modal Models (Standard) # 60min
-  timeout_in_minutes: 80
+- label: "Multi-Modal Models (Standard) 1: qwen2"
+  key: multi-modal-models-standard-1-qwen2
+  timeout_in_minutes: 45
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pip freeze | grep -E 'torch'
-    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
+    - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen2"
+    - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
+  mirror:
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
+
+- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
+  key: multi-modal-models-standard-2-qwen3-gemma
+  timeout_in_minutes: 45
+  device: h200_18gb
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  commands:
+    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
+    - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
+    - pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
+  mirror:
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
+
+- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
+  key: multi-modal-models-standard-3-llava-qwen2-vl
+  timeout_in_minutes: 45
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  commands:
+    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma"
+    - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
+  mirror:
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
+
+- label: "Multi-Modal Models (Standard) 4: other + whisper"
+  key: multi-modal-models-standard-4-other-whisper
+  timeout_in_minutes: 45
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  commands:
+    - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+    - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
+    - pytest models/multimodal/generation/test_memory_leak.py -m core_model
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
-- label: Multi-Modal Processor Test (CPU)
-  depends_on: 
+- label: Multi-Modal Processor (CPU)
+  key: multi-modal-processor-cpu
+  depends_on:
   - image-build-cpu
   timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
-  device: cpu
+  - tests/models/registry.py
+  device: cpu-medium
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Processor # 44min
+  key: multi-modal-processor
   timeout_in_minutes: 60
+  device: h200_18gb
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
+  - tests/models/registry.py
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
+  key: multi-modal-accuracy-eval-small-models
   timeout_in_minutes: 70
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
@@ -44,38 +102,49 @@ steps:
   commands:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
 
-- label: Multi-Modal Models (Extended) 1
+- label: Multi-Modal Models (Extended Generation 1)
+  key: multi-modal-models-extended-generation-1
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal
+  - tests/models/multimodal/generation
+  - tests/models/multimodal/test_mapping.py
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-    - pytest -v -s models/multimodal -m 'not core_model' --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing
+    - pytest -v -s models/multimodal/generation -m 'not core_model' --ignore models/multimodal/generation/test_common.py
+    - pytest -v -s models/multimodal/test_mapping.py
+  mirror:
+    amd:
+      device: mi300_1
+      depends_on:
+      - image-build-amd
 
-- label: Multi-Modal Models (Extended) 2
+- label: Multi-Modal Models (Extended Generation 2)
+  key: multi-modal-models-extended-generation-2
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal
+  - tests/models/multimodal/generation
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
-- label: Multi-Modal Models (Extended) 3
+- label: Multi-Modal Models (Extended Generation 3)
+  key: multi-modal-models-extended-generation-3
   optional: true
   source_file_dependencies:
   - vllm/
-  - tests/models/multimodal
+  - tests/models/multimodal/generation
   commands:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
-# This test is used only in PR development phase to test individual models and should never run on main
-- label: Custom Models
+- label: Multi-Modal Models (Extended Pooling)
+  key: multi-modal-models-extended-pooling
   optional: true
+  device: h200_18gb
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal/pooling
   commands:
-    - echo 'Testing custom models...'
-    # PR authors can temporarily add commands below to test individual models
-    # e.g. pytest -v -s models/encoder_decoder/vision_language/test_mllama.py
-    # *To avoid merge conflicts, remember to REMOVE (not just comment out) them before merging the PR*
+    - pytest -v -s models/multimodal/pooling -m 'not core_model'

.buildkite/test_areas/plugins.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Plugin Tests (2 GPUs)
+  key: plugin-tests-2-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -15,10 +16,17 @@ steps:
   - pytest -v -s plugins_tests/test_platform_plugins.py
   - pip uninstall vllm_add_dummy_platform -y
   # end platform plugin tests
-  # begin io_processor plugins test, all the code in between uses the prithvi_io_processor plugin
+  # begin io_processor plugins test
+  # test generic io_processor plugins functions
+  - pytest -v -s ./plugins_tests/test_io_processor_plugins.py
+  # test Terratorch io_processor plugins
   - pip install -e ./plugins/prithvi_io_processor_plugin
-  - pytest -v -s plugins_tests/test_io_processor_plugins.py
+  - pytest -v -s plugins_tests/test_terratorch_io_processor_plugins.py
   - pip uninstall prithvi_io_processor_plugin -y
+  # test bge_m3_sparse io_processor plugin
+  - pip install -e ./plugins/bge_m3_sparse_plugin
+  - pytest -v -s plugins_tests/test_bge_m3_sparse_io_processor_plugins.py
+  - pip uninstall bge_m3_sparse_plugin -y
   # end io_processor plugins test
   # begin stat_logger plugins test
   - pip install -e ./plugins/vllm_add_dummy_stat_logger
@@ -29,6 +37,6 @@ steps:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
   - pytest -v -s distributed/test_distributed_oot.py
-  - pytest -v -s entrypoints/openai/test_oot_registration.py # it needs a clean process
+  - pytest -v -s entrypoints/openai/chat_completion/test_oot_registration.py # it needs a clean process
   - pytest -v -s models/test_oot_registration.py # it needs a clean process
   - pytest -v -s plugins/lora_resolvers # unit tests for in-tree lora resolver plugins

.buildkite/test_areas/pytorch.yaml

@@ -3,9 +3,33 @@ depends_on:
   - image-build
 steps:
 - label: PyTorch Compilation Unit Tests
+  key: pytorch-compilation-unit-tests
   timeout_in_minutes: 10
   source_file_dependencies:
-    - vllm/
+    - vllm/__init__.py
+    - vllm/_aiter_ops.py
+    - vllm/_custom_ops.py
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/env_override.py
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/ir/
+    - vllm/kernels/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/plugins/
+    - vllm/sampling_params.py
+    - vllm/sequence.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/compile
   commands:
   # Run unit tests defined directly under compile/,
@@ -17,41 +41,150 @@ steps:
   # (using -0 for proper path handling)
   - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
+- label: PyTorch Compilation Unit Tests (H100)
+  key: pytorch-compilation-unit-tests-h100
+  timeout_in_minutes: 30
+  device: h100
+  num_devices: 1
+  source_file_dependencies:
+    - vllm/__init__.py
+    - vllm/_aiter_ops.py
+    - vllm/_custom_ops.py
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/env_override.py
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/ir/
+    - vllm/kernels/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/plugins/
+    - vllm/sampling_params.py
+    - vllm/sequence.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/compile/h100/
+  commands:
+  - "find compile/h100/ -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
+
 - label: PyTorch Compilation Passes Unit Tests
+  key: pytorch-compilation-passes-unit-tests
   timeout_in_minutes: 20
   source_file_dependencies:
-    - vllm/
+    - vllm/__init__.py
+    - vllm/_aiter_ops.py
+    - vllm/_custom_ops.py
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/env_override.py
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/ir/
+    - vllm/kernels/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/plugins/
+    - vllm/sampling_params.py
+    - vllm/sequence.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/compile/passes
   commands:
   - pytest -s -v compile/passes --ignore compile/passes/distributed
 
 - label: PyTorch Fullgraph Smoke Test
+  key: pytorch-fullgraph-smoke-test
   timeout_in_minutes: 35
   source_file_dependencies:
-  - vllm/
+  - vllm/__init__.py
+  - vllm/_aiter_ops.py
+  - vllm/_custom_ops.py
+  - vllm/compilation/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/env_override.py
+  - vllm/envs.py
+  - vllm/forward_context.py
+  - vllm/inputs/
+  - vllm/ir/
+  - vllm/kernels/
+  - vllm/logger.py
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/plugins/
+  - vllm/sampling_params.py
+  - vllm/sequence.py
+  - vllm/transformers_utils/
+  - vllm/triton_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/compile
   commands:
   # Run smoke tests under fullgraph directory, except test_full_graph.py
   # as it is a heavy test that is covered in other steps.
   # Use `find` to launch multiple instances of pytest so that
   # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
-  - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -exec pytest -s -v {} \\;"
+  - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Fullgraph
+  key: pytorch-fullgraph
   timeout_in_minutes: 30
+  device: h200_18gb
   source_file_dependencies:
-  - vllm/
+  - vllm/__init__.py
+  - vllm/_aiter_ops.py
+  - vllm/_custom_ops.py
+  - vllm/compilation/
+  - vllm/config/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/env_override.py
+  - vllm/envs.py
+  - vllm/forward_context.py
+  - vllm/inputs/
+  - vllm/ir/
+  - vllm/kernels/
+  - vllm/logger.py
+  - vllm/model_executor/
+  - vllm/multimodal/
+  - vllm/platforms/
+  - vllm/plugins/
+  - vllm/sampling_params.py
+  - vllm/sequence.py
+  - vllm/transformers_utils/
+  - vllm/triton_utils/
+  - vllm/utils/
+  - vllm/v1/
   - tests/compile
   commands:
     # fp8 kv scales not supported on sm89, tested on Blackwell instead
   - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
 
 - label: Pytorch Nightly Dependency Override Check # 2min
+  key: pytorch-nightly-dependency-override-check
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
   # in /vllm/tools/pre_commit/generate_nightly_torch_test.py
+  device: h200_18gb
   soft_fail: true
   source_file_dependencies:
-  - requirements/nightly_torch_test.txt
+  - requirements/test/nightly-torch.txt
   commands:
-  - bash standalone_tests/pytorch_nightly_dependency.sh
+  - bash standalone_tests/pytorch_nightly_dependency.sh

.buildkite/test_areas/quantization.yaml

@@ -1,29 +1,31 @@
 group: Quantization
-depends_on: 
+depends_on:
   - image-build
 steps:
 - label: Quantization
+  key: quantization
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
   - tests/quantization
   commands:
-  # temporary install here since we need nightly, will move to requirements/test.in
+  # temporary install here since we need nightly, will move to requirements/test/cuda.in
   # after torchao 0.12 release, and pin a working version of torchao nightly here
 
   # since torchao nightly is only compatible with torch nightly currently
   # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
   # we can only upgrade after this is resolved
   # TODO(jerryzh168): resolve the above comment
-  - uv pip install --system torchao==0.14.1 --index-url https://download.pytorch.org/whl/cu129
+  - uv pip install --system torchao==0.17.0 --index-url https://download.pytorch.org/whl/cu130
   - uv pip install --system conch-triton-kernels
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
 - label: Quantized MoE Test (B200)
+  key: quantized-moe-test-b200
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
-  device: b200
+  device: b200-k8s
   source_file_dependencies:
   - tests/quantization/test_blackwell_moe.py
   - vllm/model_executor/models/deepseek_v2.py
@@ -38,6 +40,7 @@ steps:
     - pytest -s -v tests/quantization/test_blackwell_moe.py
 
 - label: Quantized Models Test
+  key: quantized-models-test
   timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/model_executor/layers/quantization

.buildkite/test_areas/ray_compat.yaml

@@ -0,0 +1,18 @@
+group: Ray Compatibility
+depends_on:
+  - image-build
+steps:
+- label: Ray Dependency Compatibility Check
+  key: ray-dependency-compatibility-check
+  # Informational only — does not block the pipeline.
+  # If this fails, it means the PR introduces a dependency that
+  # conflicts with Ray's dependency constraints.
+  # See https://github.com/vllm-project/vllm/issues/33599
+  device: h200_18gb
+  soft_fail: true
+  timeout_in_minutes: 10
+  source_file_dependencies:
+  - requirements/
+  - setup.py
+  commands:
+  - bash /vllm-workspace/.buildkite/scripts/check-ray-compatibility.sh

.buildkite/test_areas/samplers.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Samplers Test
+  key: samplers-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/model_executor/layers
@@ -10,12 +11,14 @@ steps:
   - tests/samplers
   - tests/conftest.py
   commands:
-    - pytest -v -s samplers
+    # VLLM_USE_FLASHINFER_SAMPLER defaults to 1 now, so we need to pin both
+    # values explicitly to still cover the PyTorch-native (Triton) path.
+    - VLLM_USE_FLASHINFER_SAMPLER=0 pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
   mirror:
     amd:
-      device: mi325_1
+      device: mi250_1
       depends_on:
       - image-build-amd
       commands:
-      - pytest -v -s -m samplers
+      - pytest -v -s samplers

.buildkite/test_areas/spec_decode.yaml

@@ -0,0 +1,112 @@
+group: Spec Decode
+depends_on:
+  - image-build
+steps:
+- label: Spec Decode Eagle
+  key: spec-decode-eagle
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
+
+- label: Spec Decode Eagle Nightly B200
+  key: spec-decode-eagle-nightly-b200
+  timeout_in_minutes: 30
+  device: b200
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
+
+- label: Spec Decode Speculators + MTP
+  key: spec-decode-speculators-mtp
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - vllm/transformers_utils/configs/speculators/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
+
+- label: Spec Decode Speculators + MTP Nightly B200
+  key: spec-decode-speculators-mtp-nightly-b200
+  timeout_in_minutes: 30
+  device: b200
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - vllm/transformers_utils/configs/speculators/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
+  
+- label: Spec Decode Ngram + Suffix
+  key: spec-decode-ngram-suffix
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
+
+- label: Spec Decode Draft Model
+  key: spec-decode-draft-model
+  timeout_in_minutes: 30
+  device: h200_18gb
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
+
+- label: Spec Decode Draft Model Nightly B200
+  key: spec-decode-draft-model-nightly-b200
+  timeout_in_minutes: 30
+  device: b200-k8s
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
+
+- label: DFlash Speculators Correctness
+  key: dflash-speculators-correctness
+  timeout_in_minutes: 30
+  device: h100
+  optional: true
+  num_devices: 1
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/model_executor/models/qwen3_dflash.py
+    - tests/v1/spec_decode/test_speculators_dflash.py
+  commands:
+    - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
+    - pytest -v -s v1/spec_decode/test_speculators_dflash.py -m slow_test
+
+- label: Spec Decode MTP hybrid (B200)
+  timeout_in_minutes: 30
+  device: b200
+  optional: true
+  source_file_dependencies:
+    - vllm/v1/spec_decode/
+    - vllm/v1/worker/gpu/spec_decode/
+    - vllm/model_executor/models/qwen3_5.py
+    - vllm/model_executor/models/qwen3_5_mtp.py
+    - tests/v1/e2e/spec_decode/
+  commands:
+    - pytest -v -s v1/e2e/spec_decode -k "qwen3_5-hybrid"

.buildkite/test_areas/weight_loading.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Weight Loading Multiple GPU  # 33min
+  key: weight-loading-multiple-gpu
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -13,13 +14,13 @@ steps:
   commands:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt
 
-- label: Weight Loading Multiple GPU - Large Models # optional
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  device: a100
-  optional: true
-  source_file_dependencies:
-  - vllm/
-  - tests/weight_loading
-  commands:
-    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
+# - label: Weight Loading Multiple GPU - Large Models # optional
+#   working_dir: "/vllm-workspace/tests"
+#   num_devices: 2
+#   device: a100
+#   optional: true
+#   source_file_dependencies:
+#   - vllm/
+#   - tests/weight_loading
+#   commands:
+#     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt

.github/.bc-linter.yml

@@ -1,24 +0,0 @@
-# doc: https://github.com/pytorch/test-infra/blob/main/tools/stronghold/docs/bc_linter_config.md
-version: 1
-paths:
-# We temporarily disable globally, and will only enable with `annotations.include`
-# include:
-#   - "vllm/v1/attetion/*.py"
-#   - "vllm/v1/core/*.py"
-exclude:
-  - "**/*.py"
-
-scan:
-  functions: true        # check free functions and methods
-  classes: true          # check classes/dataclasses
-  public_only: true      # ignore names starting with "_" at any level
-
-annotations:
-  include:               # decorators that force‑include a symbol
-    - name: "bc_linter_include"  # matched by simple name or dotted suffix
-      propagate_to_members: false # for classes, include methods/inner classes
-  exclude:               # decorators that force‑exclude a symbol
-    - name: "bc_linter_skip"     # matched by simple name or dotted suffix
-      propagate_to_members: true  # for classes, exclude methods/inner classes
-
-excluded_violations: []  # e.g. ["ParameterRenamed", "FieldTypeChanged"]

.github/CODEOWNERS

@@ -2,18 +2,24 @@
 # for more info about CODEOWNERS file
 
 # This lists cover the "core" components of vLLM that require careful review
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg
-/vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery
+/vllm/compilation @zou3519 @youkaichao @ProExpertProg @BoyuanFeng @vadiklyutiy
+/vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery @xuechendi
 /vllm/lora @jeejeelee
-/vllm/model_executor/layers/attention @LucasWilkinson
-/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
-/vllm/model_executor/layers/mamba @tdoublep
+/vllm/model_executor/layers/attention @LucasWilkinson @MatthewBonanni
+/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety @zyongye
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety @zyongye
+/vllm/model_executor/layers/mamba @tdoublep @tomeras91
+/vllm/model_executor/layers/mamba/gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
+/vllm/model_executor/layers/rotary_embedding.py @vadiklyutiy
 /vllm/model_executor/model_loader @22quinn
 /vllm/model_executor/layers/batch_invariant.py @yewentao256 
+/vllm/ir @ProExpertProg
+/vllm/kernels/ @ProExpertProg @tjtanaa
+/vllm/kernels/helion @ProExpertProg @zou3519
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
-/vllm/vllm_flash_attn @LucasWilkinson
-CMakeLists.txt @tlrmchlsmth @LucasWilkinson
+/vllm/vllm_flash_attn @LucasWilkinson @MatthewBonanni
+/CMakeLists.txt @tlrmchlsmth @LucasWilkinson @Harry-Chen
+/cmake @tlrmchlsmth @LucasWilkinson @Harry-Chen
 
 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
@@ -39,26 +45,35 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /vllm/pooling_params.py @noooop @DarkLight1337
 /vllm/tokenizers @DarkLight1337 @njhill
 /vllm/renderers @DarkLight1337 @njhill
-/vllm/reasoning @aarnphm @chaunceyjiang
-/vllm/tool_parsers @aarnphm @chaunceyjiang
+/vllm/reasoning @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/vllm/tool_parsers @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/vllm/parser @aarnphm @chaunceyjiang @sfeng33 @bbrowning
 
 # vLLM V1
-/vllm/v1/attention @LucasWilkinson
+/vllm/v1/attention @LucasWilkinson @MatthewBonanni
 /vllm/v1/attention/backend.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @njhill
 /vllm/v1/attention/backends/mla @pavanimajety
-/vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety
+/vllm/v1/attention/backends/flashinfer.py @mgoin @pavanimajety @vadiklyutiy
 /vllm/v1/attention/backends/triton_attn.py @tdoublep
+/vllm/v1/attention/backends/gdn_attn.py @ZJY0516 @vadiklyutiy
 /vllm/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @alexm-redhat @heheda12345 @ApostaC @orozery
 /vllm/v1/sample @22quinn @houseroad @njhill
-/vllm/v1/spec_decode @benchislett @luccafong
+/vllm/v1/spec_decode @benchislett @luccafong @MatthewBonanni
 /vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett
 /vllm/v1/kv_cache_interface.py @heheda12345
 /vllm/v1/kv_offload @ApostaC @orozery
-/vllm/v1/worker/gpu/kv_connector.py @orozery
-/vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery
+/vllm/v1/engine @njhill
+/vllm/v1/executor @njhill
+/vllm/v1/worker @njhill
+/vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery @NickLucche
 
 # Model runner V2
-/vllm/v1/worker/gpu @WoosukKwon
+/vllm/v1/worker/gpu @WoosukKwon @njhill
+/vllm/v1/worker/gpu/kv_connector.py @orozery
+
+# CI & building
+/.buildkite @Harry-Chen
+/docker/Dockerfile @Harry-Chen
 
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin 
@@ -66,22 +81,26 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @aarnphm @NickLucche
-/tests/evals @mgoin
-/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256
+/tests/evals @mgoin @vadiklyutiy
+/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye
+/tests/kernels/ir @ProExpertProg @tjtanaa
 /tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye
 /tests/test_inputs.py @DarkLight1337 @ywang96
-/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
+/tests/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
 /tests/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @alexm-redhat @heheda12345 @ApostaC @orozery
 /tests/weight_loading @mgoin @youkaichao @yewentao256
 /tests/lora @jeejeelee
-/tests/models/language/generation/test_hybrid.py @tdoublep
+/tests/models/language/generation/test_hybrid.py @tdoublep @tomeras91
 /tests/v1/kv_connector/nixl_integration @NickLucche
 /tests/v1/kv_connector @ApostaC @orozery
 /tests/v1/kv_offload @ApostaC @orozery
-/tests/v1/determinism @yewentao256 
+/tests/v1/determinism @yewentao256
+/tests/reasoning @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/tests/tool_parsers @aarnphm @chaunceyjiang @sfeng33 @bbrowning
+/tests/tool_use @aarnphm @chaunceyjiang @sfeng33 @bbrowning
 
 # Transformers modeling backend
 /vllm/model_executor/models/transformers @hmellor
@@ -110,24 +129,35 @@ mkdocs.yaml @hmellor
 /tools/pre_commit @hmellor
 
 # CPU
-/vllm/v1/worker/cpu* @bigPYJ1151
+/vllm/v1/worker/cpu* @bigPYJ1151 @xuechendi
 /csrc/cpu @bigPYJ1151
-/vllm/platforms/cpu.py @bigPYJ1151
+/vllm/platforms/cpu.py @bigPYJ1151 @xuechendi
 /cmake/cpu_extension.cmake @bigPYJ1151
-/docker/Dockerfile.cpu @bigPYJ1151
+/docker/Dockerfile.cpu @bigPYJ1151 @xuechendi
 
 # Intel GPU
-/vllm/v1/worker/xpu* @jikunshang
-/vllm/platforms/xpu.py @jikunshang
-/docker/Dockerfile.xpu @jikunshang
+/vllm/v1/worker/xpu* @jikunshang @xuechendi
+/vllm/platforms/xpu.py @jikunshang @xuechendi
+/docker/Dockerfile.xpu @jikunshang @xuechendi
+
+# Nemotron-specific files
+/vllm/model_executor/models/*nemotron* @tomeras91
+/vllm/transformers_utils/configs/*nemotron* @tomeras91
+/tests/**/*nemotron* @tomeras91
 
 # Qwen-specific files
-/vllm/attention/backends/dual_chunk_flash_attn.py @sighingnow
-/vllm/model_executor/models/qwen* @sighingnow
+/vllm/model_executor/models/qwen* @sighingnow @vadiklyutiy
+/vllm/transformers_utils/configs/qwen* @sighingnow @vadiklyutiy
 
 # MTP-specific files
 /vllm/model_executor/models/deepseek_mtp.py @luccafong
 
+# DeepseekV4-specific files
+/vllm/v1/attention/ops/deepseek_v4_ops @zyongye
+/vllm/model_executor/layers/deepseek_compressor.py @zyongye
+/vllm/model_executor/layers/deepseek_v4_attention.py @zyongye
+/vllm/model_executor/layers/sparse_attn_indexer.py @zyongye
+
 # Mistral-specific files
 /vllm/model_executor/models/mistral*.py @patrickvonplaten
 /vllm/model_executor/models/mixtral*.py @patrickvonplaten
@@ -139,6 +169,7 @@ mkdocs.yaml @hmellor
 # Kernels
 /vllm/v1/attention/ops/chunked_prefill_paged_decode.py @tdoublep
 /vllm/v1/attention/ops/triton_unified_attention.py @tdoublep
+/vllm/model_executor/layers/fla @ZJY0516 @vadiklyutiy
 
 # ROCm related: specify owner with write access to notify AMD folks for careful code review
 /vllm/**/*rocm* @tjtanaa
@@ -168,6 +199,7 @@ mkdocs.yaml @hmellor
 
 # Pooling models
 /examples/pooling @noooop
+/docs/models/pooling_models @noooop
 /tests/models/*/pooling* @noooop
 /tests/entrypoints/pooling @noooop
 /vllm/config/pooler.py @noooop

.github/PULL_REQUEST_TEMPLATE.md

@@ -15,7 +15,6 @@ PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTT
 - [ ] The test plan, such as providing test command.
 - [ ] The test results, such as pasting the results comparison before and after, or e2e results
 - [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
-- [ ] (Optional) Release notes update. If your change is user facing, please update the release notes draft in the [Google Doc](https://docs.google.com/document/d/1YyVqrgX4gHTtrstbq8oWUImOyPCKSGnJ7xtTpmXzlRs/edit?tab=t.0).
 </details>
 
 **BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)