[Bugfix] Fix DeepSeek V4 MTP HC state handling (#42320 )

Signed-off-by: Mohammad Miadh Angkad <176301910+mmangkad@users.noreply.github.com> (cherry picked from commit f1cc7aad3c)
[Bugfix] Add swiglu limits to deepgemm fp8 methods (#41986 )
2026-05-14 21:28:34 -07:00 · 2026-05-14 12:38:36 -07:00 · 2026-05-14 00:59:51 -07:00 · 2026-05-14 00:59:01 -07:00 · 2026-05-13 15:15:07 -07:00 · 2026-05-13 02:12:34 -07:00
2489 changed files with 65735 additions and 304501 deletions
@@ -1,23 +0,0 @@
-name: vllm_rocm_ci
-job_dirs:
-  - ".buildkite/hardware_tests"
-run_all_patterns:
-  - "docker/Dockerfile.rocm"
-  - "docker/Dockerfile.rocm_base"
-  - "docker/ci-rocm.hcl"
-  - "docker/docker-bake-rocm.hcl"
-  - ".buildkite/hardware_tests/amd.yaml"
-  - ".buildkite/scripts/ci-bake-rocm.sh"
-  - ".buildkite/scripts/hardware_ci/run-amd-test.py"
-  - ".buildkite/scripts/hardware_ci/run-amd-test.sh"
-  - "CMakeLists.txt"
-  - "requirements/common.txt"
-  - "requirements/rocm.txt"
-  - "requirements/build/rocm.txt"
-  - "requirements/test/rocm.txt"
-  - "setup.py"
-  - "csrc/"
-  - "cmake/"
-run_all_exclude_patterns:
-  - "csrc/cpu/"
-  - "cmake/cpu_extension.cmake"
@@ -1,73 +1,22 @@
-group: Hardware - AMD Build
+group: Hardware - AMD Build 
 steps:
-  # Ensure ci_base is up-to-date before building the test image.
-  # Compares a content hash of ci_base-affecting files against the remote
-  # image label. If hashes match the build is skipped (< 30 s); if they
-  # differ ci_base is rebuilt and pushed automatically.
-  - label: "AMD: :docker: ensure ci_base"
-    key: ensure-ci-base-amd
+  - label: "AMD: :docker: build image"
+    key: image-build-amd
    depends_on: []
    device: amd_cpu
    no_plugin: true
    commands:
-      - bash .buildkite/scripts/ci-bake-rocm.sh ci-base-rocm-ci-with-deps
+    - >
+      docker build
+      --build-arg max_jobs=16
+      --build-arg REMOTE_VLLM=1
+      --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
+      --target test
+      --no-cache
+      --progress plain .
+    - docker push "rocm/vllm-ci:${BUILDKITE_COMMIT}"
    env:
      DOCKER_BUILDKIT: "1"
-      VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
-      PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
-      REMOTE_VLLM: "1"
-      VLLM_BRANCH: "$BUILDKITE_COMMIT"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 1
-        - exit_status: -10  # Agent was lost
-          limit: 1
-
-  - label: "AMD: :docker: build test image and artifacts"
-    key: image-build-amd
-    depends_on:
-      - ensure-ci-base-amd
-    device: amd_cpu
-    no_plugin: true
-    commands:
-      - |
-        if [[ "${ROCM_CI_ARTIFACT_ONLY:-0}" == "1" ]]; then
-          echo "ROCM_CI_ARTIFACT_ONLY=1; building ROCm wheel artifact only"
-          IMAGE_TAG="" bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-artifacts
-        else
-          bash .buildkite/scripts/ci-bake-rocm.sh test-rocm-ci-with-wheel
-        fi
-      - |
-        docker run --rm --network=none --entrypoint /bin/bash "rocm/vllm-ci:${BUILDKITE_COMMIT}" -ec '
-          if [ ! -d /vllm-workspace ]; then echo Missing directory: /vllm-workspace >&2; exit 1; fi
-          if [ ! -d /vllm-workspace/tests ]; then echo Missing directory: /vllm-workspace/tests >&2; exit 1; fi
-          if [ ! -d /vllm-workspace/src/vllm ]; then echo Missing directory: /vllm-workspace/src/vllm >&2; exit 1; fi
-          if [ ! -x /vllm-workspace/src/vllm/vllm-rs ]; then echo Missing executable: /vllm-workspace/src/vllm/vllm-rs >&2; exit 1; fi
-          command -v python3
-          command -v uv
-          command -v pytest
-          if ! command -v amd-smi >/dev/null 2>&1 && ! command -v rocminfo >/dev/null 2>&1; then
-            echo No ROCm CLI found in image >&2
-            exit 1
-          fi
-          python3 - <<PY
-        import torch, vllm
-        print(torch.__version__)
-        print(vllm.__version__)
-        PY
-          echo AMD image smoke OK
-        '
-    env:
-      DOCKER_BUILDKIT: "1"
-      VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
-      PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
-      IMAGE_TAG: "rocm/vllm-ci:$BUILDKITE_COMMIT"
-      REMOTE_VLLM: "1"
-      VLLM_BRANCH: "$BUILDKITE_COMMIT"
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 1
-        - exit_status: -10  # Agent was lost
-          limit: 1
@@ -12,35 +12,32 @@ 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_quant_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
-  - tests/kernels/mamba/cpu/test_cpu_gdn_ops.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_quant_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
-      pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py"
+      pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py"

-# Note: SDE can't be downloaded from CI host because of AWS WAF
-# - 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 "
-#       bash .buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh"
+- 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 "
+      bash .buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh"

 - label: CPU-Language Generation and Pooling Model Tests
  depends_on: []
@@ -57,41 +54,23 @@ steps:
      pytest -x -v -s tests/models/language/generation -m cpu_model
      pytest -x -v -s tests/models/language/pooling -m cpu_model"

- label: CPU-ModelRunnerV2 Tests
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  soft_fail: true
-  source_file_dependencies:
-  - vllm/v1/worker/cpu/
-  - vllm/v1/worker/gpu/
-  - vllm/v1/sample/ops/topk_topp_triton.py
-  - vllm/v1/sample/ops/topk_topp_sampler.py
-  - tests/v1/sample/test_topk_topp_sampler.py
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
-      uv pip install git+https://github.com/triton-lang/triton-cpu.git@270e696d
-      VLLM_USE_V2_MODEL_RUNNER=1 pytest -x -v -s tests/models/language/generation/test_granite.py -m cpu_model
-      # TODO: move to CPU-Kernel Tests once triton-cpu has a pre-built wheel
-      pytest -x -v -s tests/v1/sample/test_topk_topp_sampler.py::TestTritonTopkTopp"
-
 - label: CPU-Quantization Model Tests
  depends_on: []
  device: intel_cpu
  no_plugin: true
  source_file_dependencies:
  - csrc/cpu/
-  - vllm/model_executor/layers/quantization/auto_gptq.py
+  - vllm/model_executor/layers/quantization/cpu_wna16.py
+  - vllm/model_executor/layers/quantization/gptq_marlin.py
  - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
-  - vllm/model_executor/kernels/linear/mixed_precision/cpu.py
-  - vllm/model_executor/kernels/linear/scaled_mm/cpu.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:
    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
      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"
      
@@ -8,3 +8,10 @@ steps:
    commands: 
    - bash .buildkite/scripts/hardware_ci/run-hpu-test.sh

+  - label: "Intel GPU Test"
+    depends_on: []
+    soft_fail: true
+    device: intel_gpu
+    no_plugin: true
+    commands: 
+    - bash .buildkite/scripts/hardware_ci/run-xpu-test.sh
@@ -13,60 +13,6 @@ steps:
        - exit_status: -10  # Agent was lost
          limit: 2

-  - label: ":docker: :smoking: Non-root smoke tests"
-    key: image-build-smoke-test
-    depends_on:
-      - image-build
-    commands:
-    # Smoke 1: the default (root) image must still be importable
-    # under a non-root UID via `--user 2000:0`. Validates the `vllm` passwd
-    # entry + group-0-writable /home/vllm + uv path cleanup from #31959.
-    # Uses `import vllm` rather than `vllm serve --help` because the latter
-    # instantiates `VllmConfig` which requires a GPU attached to the
-    # container.
-    - docker run --rm --user 2000:0 --entrypoint python3 "$IMAGE_TAG" -c "import vllm; print(vllm.__version__)"
-    # Smoke 2: assert the non-root enabling invariants are baked
-    # into the image. Runs as UID 2000:0 via a shell so we can verify
-    # filesystem perms + passwd/group file state + wrapper presence without
-    # triggering vLLM's GPU-requiring config-init path. The opt-in
-    # `vllm-openai-nonroot` target adds only `USER vllm`, `WORKDIR
-    # /home/vllm`, and an `ENTRYPOINT` override on top of these invariants;
-    # its build correctness is reviewed at the Dockerfile level. Wrapper
-    # logic is covered separately by the pre-commit hook
-    # `test-nonroot-entrypoint` (see .pre-commit-config.yaml).
-    - |
-      docker run --rm --user 2000:0 --entrypoint /bin/sh "$IMAGE_TAG" -ec '
-        if ! getent passwd 2000 | grep -q ^vllm:; then
-          echo FAIL: UID 2000 != vllm
-          exit 1
-        fi
-        if ! id -gn 2>/dev/null | grep -qx root; then
-          echo FAIL: GID 0 not root group
-          exit 1
-        fi
-        touch /home/vllm/.smoke && rm /home/vllm/.smoke
-        touch /opt/uv/cache/.smoke && rm /opt/uv/cache/.smoke
-        if ! test -x /usr/local/bin/vllm-nonroot-entrypoint.sh; then
-          echo FAIL: wrapper missing
-          exit 1
-        fi
-        if ! test -w /etc/passwd; then
-          echo FAIL: /etc/passwd not group-writable
-          exit 1
-        fi
-        if ! test -w /etc/group; then
-          echo FAIL: /etc/group not group-writable
-          exit 1
-        fi
-        echo non-root invariants OK
-      '
-    retry:
-      automatic:
-        - exit_status: -1  # Agent was lost
-          limit: 2
-        - exit_status: -10  # Agent was lost
-          limit: 2
-
  - label: ":docker: Build CPU image"
    key: image-build-cpu
    depends_on: []
@@ -110,21 +56,3 @@ steps:
          limit: 2
        - exit_status: -10  # Agent was lost
          limit: 2
-
-  - label: ":docker: Build arm64 image"
-    key: arm64-image-build
-    depends_on: []
-    source_file_dependencies:
-      - ".buildkite/image_build/image_build.yaml"
-      - ".buildkite/image_build/image_build_arm64.sh"
-      - "docker/Dockerfile"
-    commands:
-    - .buildkite/image_build/image_build_arm64.sh $REGISTRY $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
@@ -1,37 +0,0 @@
-#!/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" || true
-
-# skip build if image already exists
-if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64) ]]; then
-  echo "Image not found, proceeding with build..."
-else
-  echo "Image found"
-  exit 0
-fi
-
-# build (Grace/GH200 is the arm64 GPU target; sm_90)
-docker build --file docker/Dockerfile \
-  --platform linux/arm64 \
-  --build-arg max_jobs=16 \
-  --build-arg nvcc_threads=4 \
-  --build-arg torch_cuda_arch_list="9.0" \
-  --build-arg USE_SCCACHE=1 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64 \
-  --target test \
-  --progress plain .
-
-# push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64
@@ -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 "$REGISTRY" || true
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"

 # skip build if image already exists
 if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu) ]]; then
@@ -11,8 +11,8 @@ 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 "$REGISTRY" || true
-aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$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
@@ -1,22 +0,0 @@
-group: Basic Correctness
-depends_on:
-  - image-build-xpu
-steps:
- label: XPU Sleep Mode
-  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/basic_correctness/test_cumem.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      pytest -v -s basic_correctness/test_mem.py::test_end_to_end'
@@ -1,23 +0,0 @@
-group: Expert Parallelism
-depends_on: 
-  - image-build-xpu
-steps:
- label: EPLB Algorithm
-  key: eplb-algorithm
-  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/distributed/eplb
-  - tests/distributed/test_eplb_algo.py
-  - tests/distributed/test_eplb_utils.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'cd tests &&
-      pytest -v -s distributed/test_eplb_algo.py'
@@ -49,7 +49,7 @@ steps:
      '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 --deselect="tests/lora/test_moe_lora_align_sum.py::test_moe_lora_align_block_size_mixed_base_and_lora[1]"'
+      pytest -v -s lora/test_moe_lora_align_sum.py'

 - label: LoRA Punica Kernels
  timeout_in_minutes: 45
@@ -38,17 +38,7 @@ steps:
    REPO: "vllm-ci-test-repo"
    VLLM_TEST_DEVICE: "xpu"
  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/
+    - vllm/
    - tests/v1/sample
    - tests/v1/logits_processors
    - tests/v1/test_oracle.py
@@ -62,126 +52,4 @@ steps:
      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 &&
-      pytest -v -s v1/sample/test_topk_topp_sampler.py'
-
- label: XPU CPU Offload
-  timeout_in_minutes: 60
-  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/
-    - vllm/v1/kv_offload/
-    - vllm/v1/kv_connector/
-    - tests/v1/kv_offload/
-    - tests/v1/kv_connector/unit/test_offloading_connector.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
-      cd tests &&
-      pytest -v -s v1/kv_offload &&
-      pytest -v -s v1/kv_connector/unit/test_offloading_connector.py'
-
- label: Regression
-  key: regression
-  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/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:
-  - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install modelscope &&
-      cd tests &&
-      pytest -v -s test_regression.py'
-
- label: Metrics, Tracing (2 GPUs)
-  key: metrics-tracing-2-gpus
-  timeout_in_minutes: 30
-  num_devices: 2
-  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/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:
-  - >-
-    bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-    'pip install opentelemetry-sdk\>=1.26.0 opentelemetry-api\>=1.26.0 opentelemetry-exporter-otlp\>=1.26.0 opentelemetry-semantic-conventions-ai\>=0.4.1 &&
-    cd tests &&
-    pytest -v -s v1/tracing'
-
- label: Async Engine, Inputs, Utils, Worker
-  key: async-engine-inputs-utils-worker
-  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/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_
-  commands:
-  - >-
-    bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-    'cd tests &&
-    pip install av &&
-    pytest -v -s detokenizer &&
-    pytest -v -s -m "not cpu_test" ./multimodal &&
-    pytest -v -s utils_ --ignore=utils_/test_mem_utils.py'
+      pytest -v -s v1/test_outputs.py'
@@ -1,111 +0,0 @@
-group: Models - Multimodal
-depends_on: 
-  - image-build-xpu
-steps:
- label: "Multi-Modal Models (Standard) 1: qwen2"
-  key: multi-modal-models-standard-1-qwen2
-  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/
-  - tests/models/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install av git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      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'
-
- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
-  key: multi-modal-models-standard-2-qwen3-gemma
-  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/
-  - tests/models/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model'
-
- label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
-  key: multi-modal-models-standard-3-llava-qwen2-vl
-  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/
-  - tests/models/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      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'
-
- label: "Multi-Modal Models (Standard) 4: other + whisper"
-  key: multi-modal-models-standard-4-other-whisper
-  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/
-  - tests/models/multimodal
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install av git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      cd tests &&
-      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'
-
- label: Multi-Modal Processor # 44min
-  key: multi-modal-processor
-  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/
-  - tests/models/multimodal
-  - tests/models/registry.py
-  commands:
-    - >-
-      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
-      'pip install av matplotlib ftfy git+https://github.com/TIGER-AI-Lab/Mantis.git &&
-      pip install open-clip-torch --no-deps &&
-      cd tests &&
-      pytest -v -s models/multimodal/processing/test_tensor_schema.py
-      --deselect "tests/models/multimodal/processing/test_tensor_schema.py::test_model_tensor_schema[mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4]"
-      --deselect "tests/models/multimodal/processing/test_tensor_schema.py::test_model_tensor_schema[Qwen/Qwen2.5-Omni-7B-AWQ]"
-      --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB'
-  parallelism: 4
@@ -36,14 +36,9 @@ steps:
        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 nvidia/Llama-3.1-8B-Instruct-FP8 --block-size 64 --enforce-eager --quantization modelopt --kv-cache-dtype fp8 --attention-backend TRITON_ATTN --max-model-len 4096 &&
        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 superjob/Qwen3-4B-Instruct-2507-GPTQ-Int4 --max-model-len 8192 &&
-        VLLM_XPU_FUSED_MOE_USE_REF=1 python3 examples/basic/offline_inference/generate.py --model Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 --enforce-eager -tp 2 --max-model-len 8192 &&
-        python3 examples/basic/offline_inference/generate.py --model INCModel/Qwen3-30B-A3B-Instruct-2507-MXFP4-LLMC --enforce-eager -tp 2 --max-model-len 8192
-        '
+        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
@@ -67,23 +62,4 @@ steps:
        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 --ignore=v1/kv_connector/unit/test_offloading_connector.py'
-  - label: "XPU server 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 av &&
-        cd tests &&
-        pytest -v -s entrypoints/multimodal/openai/chat_completion/test_audio_in_video.py &&
-        pytest -v -s benchmarks/test_serve_cli.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'
@@ -6,7 +6,9 @@ tasks:
    value: 0.7142
  - name: "exact_match,flexible-extract"
    value: 0.4579
-moe_backend: "flashinfer_cutlass"
+env_vars:
+  VLLM_USE_FLASHINFER_MOE_FP8: "1"
+  VLLM_FLASHINFER_MOE_BACKEND: "throughput"
 limit: 1319
 num_fewshot: 5
 max_model_len: 262144
@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on chartqa for vllm.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.11"

 usage() {
    echo``
@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.11"

 usage() {
    echo``
@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.11"

 usage() {
    echo``
@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install "lm-eval[api]>=0.4.12"
+#   pip install "lm-eval[api]>=0.4.11"

 usage() {
    echo``
@@ -68,10 +68,6 @@ def launch_lm_eval(eval_config, tp_size):
    if current_platform.is_rocm() and "Nemotron-3" in eval_config["model_name"]:
        model_args += "attention_backend=TRITON_ATTN"

-    moe_backend = eval_config.get("moe_backend", None)
-    if moe_backend is not None:
-        model_args += f"moe_backend={moe_backend},"
-
    env_vars = eval_config.get("env_vars", None)
    with scoped_env_vars(env_vars):
        results = lm_eval.simple_evaluate(
@@ -2,6 +2,7 @@
    {
        "test_name": "latency_llama8B_tp1",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
            "VLLM_CPU_KVCACHE_SPACE": 40
@@ -2,6 +2,7 @@
    {
        "test_name": "latency_llama8B_tp2",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
 	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
 	    "VLLM_CPU_SGL_KERNEL": 1,
@@ -13,6 +13,7 @@
      200
    ],
    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
      "VLLM_CPU_SGL_KERNEL": 1,
@@ -5,6 +5,7 @@
    ],
    "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": {
@@ -9,6 +9,7 @@
      128
    ],
    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
      "VLLM_CPU_SGL_KERNEL": 1,
@@ -5,6 +5,7 @@
    ],
    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
      "VLLM_CPU_SGL_KERNEL": 1,
@@ -30,9 +31,30 @@
    }
  },
  "tests": [
+    {
+      "test_name": "serving_llama8B_tp1_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "sharegpt",
+        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_sharegpt",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "sharegpt",
+        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
+      }
+    },
    {
      "test_name": "serving_llama8B_tp1_random_128_128",
      "server_parameters": {
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "dataset_name": "random",
@@ -40,10 +62,158 @@
        "random-output-len": 128
      }
    },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "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": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 2048
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_128_2048",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "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": {
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp2_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_tp4_random_2048_128",
+      "server_parameters": {
+        "tensor_parallel_size": 4
+      },
+      "client_parameters": {
+        "dataset_name": "random",
+        "random-input-len": 2048,
+        "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": {
-        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 1
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp2_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 2
+      },
+      "client_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "dataset_name": "random",
+        "random-input-len": 128,
+        "random-output-len": 128
+      }
+    },
+    {
+      "test_name": "serving_llama8B_int4_tp4_random_128_128",
+      "server_parameters": {
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
+        "tensor_parallel_size": 4
      },
      "client_parameters": {
        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
@@ -55,7 +225,8 @@
    {
      "test_name": "serving_llama8B_int8_tp1_random_128_128",
      "server_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
+        "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",
@@ -65,12 +236,26 @@
      }
    },
    {
-      "test_name": "serving_llama1B_tp1_random_128_128",
+      "test_name": "serving_llama8B_int8_tp2_random_128_128",
      "server_parameters": {
-        "model": "meta-llama/Llama-3.2-1B"
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "tensor_parallel_size": 2
      },
      "client_parameters": {
-        "model": "meta-llama/Llama-3.2-1B",
+        "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
@@ -79,7 +264,8 @@
    {
      "test_name": "serving_llama3B_tp1_random_128_128",
      "server_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct"
+        "model": "meta-llama/Llama-3.2-3B-Instruct",
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "model": "meta-llama/Llama-3.2-3B-Instruct",
@@ -88,22 +274,11 @@
        "random-output-len": 128
      }
    },
-    {
-      "test_name": "serving_llama70B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct"
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
    {
      "test_name": "serving_granite2B_tp1_random_128_128",
      "server_parameters": {
-        "model": "ibm-granite/granite-3.2-2b-instruct"
+        "model": "ibm-granite/granite-3.2-2b-instruct",
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "model": "ibm-granite/granite-3.2-2b-instruct",
@@ -115,7 +290,8 @@
    {
      "test_name": "serving_qwen1.7B_tp1_random_128_128",
      "server_parameters": {
-        "model": "Qwen/Qwen3-1.7B"
+        "model": "Qwen/Qwen3-1.7B",
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "model": "Qwen/Qwen3-1.7B",
@@ -127,7 +303,8 @@
    {
      "test_name": "serving_qwen4B_tp1_random_128_128",
      "server_parameters": {
-        "model": "Qwen/Qwen3-4B"
+        "model": "Qwen/Qwen3-4B",
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "model": "Qwen/Qwen3-4B",
@@ -139,7 +316,8 @@
    {
      "test_name": "serving_qwen8B_tp1_random_128_128",
      "server_parameters": {
-        "model": "Qwen/Qwen3-8B"
+        "model": "Qwen/Qwen3-8B",
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "model": "Qwen/Qwen3-8B",
@@ -148,34 +326,11 @@
        "random-output-len": 128
      }
    },
-    {
-      "test_name": "serving_qwen14B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-14B"
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-14B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_qwen30B_tp1_random_128_128",
-      "server_parameters": {
-        "model": "Qwen/Qwen3-30B-A3B"
-      },
-      "client_parameters": {
-        "model": "Qwen/Qwen3-30B-A3B",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
    {
      "test_name": "serving_glm9B_tp1_random_128_128",
      "server_parameters": {
-        "model": "zai-org/glm-4-9b-hf"
+        "model": "zai-org/glm-4-9b-hf",
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "model": "zai-org/glm-4-9b-hf",
@@ -187,7 +342,8 @@
    {
      "test_name": "serving_gemma7B_tp1_random_128_128",
      "server_parameters": {
-        "model": "google/gemma-7b"
+        "model": "google/gemma-7b",
+        "tensor_parallel_size": 1
      },
      "client_parameters": {
        "model": "google/gemma-7b",
@@ -195,94 +351,6 @@
        "random-input-len": 128,
        "random-output-len": 128
      }
-    },
-    {
-      "test_name": "serving_gemma3-4b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-3-4b-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-3-4b-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma3-12b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-3-12b-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-3-12b-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-4b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-4-E4B-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-E4B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-2b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-4-E2B-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-E2B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-26b_tp1_random_128_128",
-      "server_environment_variables": {
-	"VLLM_CPU_SGL_KERNEL": 0,
-        "VLLM_CPU_ATTN_SPLIT_KV": 0
-      },
-      "server_parameters": {
-        "model": "google/gemma-4-26B-A4B-it"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-26B-A4B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_phi4_tp1_random_128_128",
-      "server_parameters": {
-        "model": "microsoft/Phi-4-reasoning"
-      },
-      "client_parameters": {
-        "model": "microsoft/Phi-4-reasoning",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
    }
  ]
 }
@@ -5,6 +5,7 @@
    ],
    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
    "server_environment_variables": {
+      "VLLM_RPC_TIMEOUT": 100000,
      "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
      "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
      "VLLM_CPU_SGL_KERNEL": 1,
@@ -1,112 +1,77 @@
-{
-  "defaults": {
-    "qps_list": [
-      "inf"
-    ],
-    "max_concurrency_list": [12, 16, 24, 32, 64, 128, 200],
-    "server_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "tensor_parallel_size": 1,
-      "dtype": "bfloat16"
+[
+    {
+        "test_name": "serving_llama8B_tp1_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "tensor_parallel_size": 1,
+            "disable_log_stats": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
    },
-    "client_parameters": {
-      "model": "meta-llama/Llama-3.1-8B-Instruct",
-      "backend": "vllm",
-      "ignore-eos": "",
-      "temperature": 0,
-      "num_prompts": 200
+    {
+        "test_name": "serving_llama70B_tp4_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "tensor_parallel_size": 4,
+            "disable_log_stats": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_mixtral8x7B_tp2_sharegpt",
+        "qps_list": [1, 4, 16, "inf"],
+        "server_parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "tensor_parallel_size": 2,
+            "disable_log_stats": "",
+            "load_format": "dummy"
+        },
+        "client_parameters": {
+            "model": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200
+        }
+    },
+    {
+        "test_name": "serving_llama70B_tp4_sharegpt_specdecode",
+        "qps_list": [2],
+        "server_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct", 
+            "tensor_parallel_size": 4,
+            "speculative_config": {
+                "model": "turboderp/Qwama-0.5B-Instruct",
+                "num_speculative_tokens": 4,
+                "draft_tensor_parallel_size": 1
+            }
+        },
+        "client_parameters": {
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "backend": "vllm",
+            "dataset_name": "sharegpt",
+            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
+            "temperature": 0,
+            "num_prompts": 200 
+        }
    }
-  },
-  "tests": [
-    {
-      "test_name": "serving_llama8B_tp1_sharegpt",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-        "dataset_name": "sharegpt",
-        "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json"
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_128_2048",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 2048
-      }
-    },
-    {
-      "test_name": "serving_llama8B_tp1_random_2048_128",
-      "server_parameters": {
-        "tensor_parallel_size": 1
-      },
-      "client_parameters": {
-        "dataset_name": "random",
-        "random-input-len": 2048,
-        "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_llama70B_tp4_random_128_128",
-      "server_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct",
-        "async_scheduling": "",
-	"no_enable_prefix_caching": "",
-        "max_num_batched_tokens": 8192
-      },
-      "client_parameters": {
-        "model": "meta-llama/Llama-3.3-70B-Instruct",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_gemma4-e4b_tp1_random_128_128",
-      "server_parameters": {
-        "model": "google/gemma-4-E4B-it",
-	"enable_auto_tool_choice": "",
-	"tool_call_parser": "gemma4",
-	"chat_template": "examples/tool_chat_template_gemma4.jinja",
-	"reasoning_parser": "gemma4"
-      },
-      "client_parameters": {
-        "model": "google/gemma-4-E4B-it",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    }
-  ]
-}
+]
@@ -2,6 +2,7 @@
    {
        "test_name": "throughput_llama8B_tp1",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
            "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
            "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
            "VLLM_CPU_KVCACHE_SPACE": 40
@@ -2,6 +2,7 @@
    {
        "test_name": "throughput_llama8B_tp2",
        "environment_variables": {
+            "VLLM_RPC_TIMEOUT": 100000,
 	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
 	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
 	    "VLLM_CPU_SGL_KERNEL": 1,
@@ -1,28 +1,12 @@
 # CUDA architecture lists — following PyTorch RELEASE.md
 # (https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
 # SM86 included for broader Ampere coverage; SM89 for marlin fp8 support
-# These requested arches are filtered by CMake's CUDA_SUPPORTED_ARCHS before
-# per-kernel arch selection. Do not add +PTX here: top-level +PTX is stripped
-# during that filtering, so kernels that need PTX must request it locally.
 env:
-  # for CUDA >=13, sm_100+ targets have family specifiers (see CMakeLists.txt)
-  # so targets like 10.3 and 12.1 are automatically supported with this list
-  CUDA_ARCH_X86: "7.5 8.0 8.6 8.9 9.0 10.0 12.0"
-  # aarch64-only targets: Orin (8.7), Thor (11.0, CUDA 13+)
-  CUDA_ARCH_AARCH64: "8.0 8.7 8.9 9.0 10.0 11.0 12.0"
-
-  # for CUDA <13, we need to specify all needed targets
-  # some targets (10.3, 12.1) are skipped to limit the wheel size (< 500MB)
-  # please use CUDA 13 wheels or compile yourself on these new devices
+  CUDA_ARCH_X86: "7.5 8.0 8.6 8.9 9.0 10.0 12.0+PTX"
+  # aarch64 only architectures: 8.7 for Orin, 11.0 for Thor (since CUDA 13)
+  CUDA_ARCH_AARCH64: "8.0 8.7 8.9 9.0 10.0 11.0 12.0+PTX"
  CUDA_ARCH_X86_CU129: "7.5 8.0 8.6 8.9 9.0 10.0 12.0"
  CUDA_ARCH_AARCH64_CU129: "8.0 8.7 8.9 9.0 10.0 12.0"
-  
-  # pre-built mooncake wheels
-  # the manylinux_2_35 wheel has compatibility issue on Ubuntu 24.04
-  # so we use different wheels for the time being
-  MOONCAKE_WHEEL_AARCH64_2_35: "https://vllm-wheels.s3.amazonaws.com/mooncake/mooncake_transfer_engine-0.3.10.post2-0da9dfea3-cp312-cp312-manylinux_2_35_aarch64.whl"
-  MOONCAKE_WHEEL_AARCH64_2_39: "https://vllm-wheels.s3.amazonaws.com/mooncake/mooncake_transfer_engine-0.3.10.post2-0da9dfea3-cp312-cp312-manylinux_2_39_aarch64.whl"
-  MOONCAKE_WHEEL_X86_64: "https://vllm-wheels.s3.amazonaws.com/mooncake/mooncake_transfer_engine-0.3.10.post2-0da9dfea3-cp312-cp312-manylinux_2_35_x86_64.whl"

 steps:
  - input: "Provide Release version here"
@@ -40,7 +24,7 @@ steps:
        agents:
          queue: arm64_cpu_queue_release
        commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinuxaarch64-builder:cuda12.9 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
          - "mkdir artifacts"
          - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
@@ -82,7 +66,7 @@ steps:
        agents:
          queue: cpu_queue_release
        commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinux2_28-builder:cuda12.9 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
          - "mkdir artifacts"
          - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
@@ -152,8 +136,6 @@ steps:
              --build-arg CUDA_VERSION=13.0.2 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
              --target vllm-openai \
              --progress plain \
@@ -180,8 +162,6 @@ steps:
              --build-arg CUDA_VERSION=13.0.2 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
              --target vllm-openai \
              --progress plain \
@@ -205,8 +185,6 @@ steps:
              --build-arg CUDA_VERSION=12.9.1 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --target vllm-openai \
              --progress plain \
              -f docker/Dockerfile .
@@ -232,8 +210,6 @@ steps:
              --build-arg CUDA_VERSION=12.9.1 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_35}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --target vllm-openai \
              --progress plain \
              -f docker/Dockerfile .
@@ -258,8 +234,6 @@ steps:
              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
              --target vllm-openai \
              --progress plain \
@@ -287,8 +261,6 @@ steps:
              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
              --target vllm-openai \
              --progress plain \
@@ -314,8 +286,6 @@ steps:
              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --target vllm-openai \
              --progress plain \
              -f docker/Dockerfile .
@@ -342,8 +312,6 @@ steps:
              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg MOONCAKE_WHEEL_AARCH64="${MOONCAKE_WHEEL_AARCH64_2_39}" \
-              --build-arg MOONCAKE_WHEEL_X86_64="${MOONCAKE_WHEEL_X86_64}" \
              --target vllm-openai \
              --progress plain \
              -f docker/Dockerfile .
@@ -750,7 +718,7 @@ steps:
      - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
    env:
      S3_BUCKET: "vllm-wheels"
-      VARIANT: "rocm723"
+      VARIANT: "rocm722"

  # ROCm Job 6: Build ROCm Release Docker Image
  - label: ":docker: Build release image - x86_64 - ROCm"
@@ -859,6 +827,7 @@ steps:
        allow_failure: true
      - step: build-cpu-release-image-arm64
        allow_failure: true
+    if: build.env("NIGHTLY") != "1"

  - label: "Publish release images to DockerHub"
    depends_on:
@@ -13,8 +13,5 @@ INPUT_FILE="$1"
 # Strip timestamps
 sed -i 's/^\[[0-9]\{4\}-[0-9]\{2\}-[0-9]\{2\}T[0-9]\{2\}:[0-9]\{2\}:[0-9]\{2\}Z\] //' "$INPUT_FILE"

-# Strip Buildkite inline timestamp markers (ESC _bk;t=<ms> BEL)
-sed -i 's/\x1B_bk;t=[0-9]*\x07//g' "$INPUT_FILE"
-
 # Strip colorization
 sed -i -r 's/\x1B\[[0-9;]*[mK]//g' "$INPUT_FILE"
@@ -1,178 +1,55 @@
 #!/bin/bash
-# Fetch vLLM Buildkite CI logs (public; no login required).
+# Usage: ./ci-fetch-log.sh <buildkite_job_url> [output_file]
+#        ./ci-fetch-log.sh <build_number> <job_uuid> [output_file]
 #
-# Usage:
-#   ci-fetch-log.sh [--soft|--all] --pr [<PR>]  failed jobs in the PR's latest
-#                                               build (current branch if omitted)
-#   ci-fetch-log.sh [--soft|--all] <build_url>  failed jobs in that build
-#   ci-fetch-log.sh <job_url> [output]          one job; both #<job_uuid> and
-#                                               ?sid=<id> URL forms work
-#   ci-fetch-log.sh <build> <job_uuid> [output]
+# 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.
 #
-# --soft also fetches soft-failed jobs; --all fetches every finished job.
-# Saves each log as ci-<build>-<job-name>.log (ANSI/timestamps stripped) and
-# prints "<file>\t<job name>" per job. [output] is single-job only; "-"
-# streams to stdout. Existing files are kept; CI_FETCH_LOG_FORCE=1 refetches.
+# 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"
-UA="vllm-ci-fetch-log"
-UUID_RE='[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}'

 usage() {
-    sed -n '2,15p' "$0" | sed 's/^# \{0,1\}//'
+    echo "Usage: $0 <buildkite_job_url> [output_file]"
+    echo "       $0 <build_number> <job_uuid> [output_file]"
    exit 1
 }

-die() {
-    echo "$1" >&2
-    exit 1
-}
+if [ $# -lt 1 ]; then usage; fi

-BUILD="" JOB="" SID="" OUT=""
-SCOPE="failed"
-
-while :; do
-    case "${1:-}" in
-    --soft) SCOPE="soft" ;;
-    --all) SCOPE="all" ;;
-    *) break ;;
-    esac
-    shift
-done
-
-case "${1:-}" in
--pr)
-    PR="${2:-}"
-    # gh pr checks exits non-zero when checks are failing; that is the
-    # expected case here.
-    URL=$(gh pr checks ${PR:+"$PR"} --repo vllm-project/vllm 2>/dev/null |
-        grep -oE "https://buildkite.com/${ORG}/${PIPELINE}/builds/[0-9]+" |
-        sort -t/ -k7 -n | tail -1 || true)
-    [ -n "$URL" ] || die "No Buildkite build found via: gh pr checks ${PR:-<current branch>}"
-    BUILD="${URL##*/}"
-    ;;
-https://*)
+if [[ "$1" == https://* ]]; then
    BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
-    JOB=$(echo "$1" | grep -oE "#${UUID_RE}" | head -n 1 | cut -c2- || true)
-    SID=$(echo "$1" | grep -oE "[?&]sid=${UUID_RE}" | head -n 1 | sed 's/.*sid=//' || true)
-    OUT="${2:-}"
-    [ -n "$BUILD" ] || die "Could not parse build number from: $1"
-    ;;
-[0-9]*)
-    [ $# -ge 2 ] || usage
+    JOB=$(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:-}"
-    ;;
-*)
+    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
-    ;;
-esac
+fi

 COOKIES=$(mktemp)
-JOBS_TSV=$(mktemp)
-trap 'rm -f "$COOKIES" "$JOBS_TSV"' EXIT
+trap 'rm -f "$COOKIES"' EXIT

-# Buildkite issues a session cookie on first hit; later requests need it.
-curl -fsSL -c "$COOKIES" -A "$UA" \
+# 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

-# The build's job list (id, step uuid, state, name) is served as JSON from
-# the user-facing /data/jobs endpoint. Flatten it to TSV for easy filtering:
-#   job_id  step_uuid  failed  soft_failed  finished  slug  name
-curl -fsSL -b "$COOKIES" -A "$UA" \
-    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}/data/jobs" |
-    python3 -c '
-import json, re, sys
+curl -fsSL -b "$COOKIES" -A "vllm-ci-fetch-log" \
+    "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/${JOB}/download" \
+    -o "$OUT"

-data = json.load(sys.stdin)
-if data.get("has_next_page"):
-    print("warning: job list is paginated; some jobs not shown", file=sys.stderr)
-for r in data["records"]:
-    if r.get("type") != "script":
-        continue
-    name = (r.get("name") or "").replace("\t", " ").replace("\n", " ")
-    slug = re.sub(r"[^a-z0-9]+", "-", name.lower()).strip("-")[:60]
-    print("\t".join([
-        r["id"],
-        r.get("step_uuid") or "",
-        str(r.get("passed") is False),
-        str(bool(r.get("soft_failed"))),
-        str(bool(r.get("finished_at"))),
-        slug,
-        name,
-    ]))
-' >"$JOBS_TSV" || die "Could not list jobs for build ${BUILD}"
+bash "$(dirname "$0")/ci-clean-log.sh" "$OUT"

-if [ -n "$SID" ] && [ -z "$JOB" ]; then
-    # The ?sid= in builds/<N>/list URLs is the *step* uuid, not the job uuid.
-    JOB=$(awk -F'\t' -v s="$SID" '$1 == s || $2 == s {print $1; exit}' "$JOBS_TSV")
-    [ -n "$JOB" ] || die "No job matching sid=${SID} in build ${BUILD}"
-fi
-
-fetch_job() { # <job_uuid> <output_file>
-    curl -fsSL -b "$COOKIES" -A "$UA" \
-        "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/$1/download" \
-        -o "$2"
-    bash "$(dirname "$0")/ci-clean-log.sh" "$2"
-}
-
-if [ -n "$JOB" ]; then
-    # Single-job mode.
-    NAME=$(awk -F'\t' -v j="$JOB" '$1 == j {print $7; exit}' "$JOBS_TSV")
-    SLUG=$(awk -F'\t' -v j="$JOB" '$1 == j {print $6; exit}' "$JOBS_TSV")
-    [ -n "$OUT" ] || OUT="ci-${BUILD}-${SLUG:-${JOB:0:13}}.log"
-    if [ "$OUT" = "-" ]; then
-        TMP=$(mktemp)
-        fetch_job "$JOB" "$TMP"
-        cat "$TMP"
-        rm -f "$TMP"
-        exit 0
-    fi
-    if [ -e "$OUT" ] && [ -z "${CI_FETCH_LOG_FORCE:-}" ]; then
-        die "Refusing to overwrite existing ${OUT} (set CI_FETCH_LOG_FORCE=1 or pass an output path)."
-    fi
-    fetch_job "$JOB" "$OUT"
-    printf '%s\t%s\n' "$OUT" "${NAME:-$JOB}"
-    exit 0
-fi
-
-# Build-wide mode: fetch finished jobs matching $SCOPE.
-[ -z "$OUT" ] || die "[output_file] is only valid when fetching a single job."
-
-case "$SCOPE" in
-failed) FILTER='$3 == "True" && $4 == "False" && $5 == "True"' ;;
-soft) FILTER='$3 == "True" && $5 == "True"' ;;
-all) FILTER='$5 == "True"' ;;
-esac
-
-if [ "$SCOPE" = "failed" ]; then
-    SOFT=$(awk -F'\t' '$3 == "True" && $4 == "True"' "$JOBS_TSV" | wc -l)
-    [ "$SOFT" -eq 0 ] || echo "Skipping ${SOFT} soft-failed job(s); use --soft to include them." >&2
-fi
-
-FOUND=0
-EMITTED=" "
-while IFS=$'\t' read -r job_id _ _ _ _ slug name; do
-    FOUND=$((FOUND + 1))
-    out="ci-${BUILD}-${slug:-${job_id:0:13}}.log"
-    # Retries share a name with the original job; disambiguate by uuid.
-    case "$EMITTED" in
-    *" $out "*) out="ci-${BUILD}-${slug:-job}-${job_id:0:13}.log" ;;
-    esac
-    EMITTED="${EMITTED}${out} "
-    if [ -e "$out" ] && [ -z "${CI_FETCH_LOG_FORCE:-}" ]; then
-        echo "Keeping existing ${out} (set CI_FETCH_LOG_FORCE=1 to refetch)." >&2
-    elif ! fetch_job "$job_id" "$out"; then
-        echo "Failed to download log for job ${job_id} (${name})." >&2
-        continue
-    fi
-    printf '%s\t%s\n' "$out" "$name"
-done < <(awk -F'\t' "$FILTER" "$JOBS_TSV")
-
-if [ "$FOUND" -eq 0 ]; then
-    echo "No matching jobs in build ${BUILD} (scope: ${SCOPE})." >&2
-fi
+echo "$OUT"
@@ -28,18 +28,32 @@
 ###############################################################################
 set -o pipefail

-# Export Python path for commands that run directly on the host. Containerized
-# tests set this to /vllm-workspace below so spawned Python processes do not
-# depend on their current working directory.
-export PYTHONPATH="${PYTHONPATH:-..}"
+# Export Python path
+export PYTHONPATH=".."

 ###############################################################################
 # Helper Functions
 ###############################################################################

-report_docker_usage() {
-  echo "--- Docker usage"
-  docker system df || true
+cleanup_docker() {
+  # Get Docker's root directory
+  docker_root=$(docker info -f '{{.DockerRootDir}}')
+  if [ -z "$docker_root" ]; then
+    echo "Failed to determine Docker root directory."
+    exit 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%. Cleaning up Docker images and volumes..."
+    docker image prune -f
+    docker volume prune -f && docker system prune --force --filter "until=72h" --all
+    echo "Docker images and volumes cleanup completed."
+  else
+    echo "Disk usage is below $threshold%. No cleanup needed."
+  fi
 }

 cleanup_network() {
@@ -54,108 +68,6 @@ cleanup_network() {
  fi
 }

-prepare_artifact_image() {
-  if [[ "${VLLM_CI_USE_ARTIFACTS:-0}" != "1" ]]; then
-    return 1
-  fi
-  if ! command -v buildkite-agent >/dev/null 2>&1; then
-    echo "buildkite-agent not found; cannot download ROCm wheel artifact"
-    return 1
-  fi
-
-  local artifact_glob="${VLLM_CI_ARTIFACT_GLOB:-artifacts/vllm-rocm-install/vllm-rocm-install.tar.gz}"
-  local archive=""
-  local metadata_file=""
-  local base_image="${VLLM_CI_BASE_IMAGE:-rocm/vllm-dev:ci_base}"
-  local artifact_image=""
-  local artifact_key=""
-  local base_digest=""
-  local wheel_dir=""
-  local context_dir=""
-  local workspace_dir=""
-
-  artifact_work_dir=$(mktemp -d -t vllm-rocm-artifact.XXXXXX)
-  wheel_dir="${artifact_work_dir}/wheels"
-  context_dir="${artifact_work_dir}/context"
-  workspace_dir="${context_dir}/workspace"
-  mkdir -p "${wheel_dir}" "${context_dir}/wheels" "${workspace_dir}"
-
-  echo "--- Downloading ROCm wheel artifact"
-  if ! buildkite-agent artifact download "${artifact_glob}" "${artifact_work_dir}"; then
-    echo "Failed to download ${artifact_glob}"
-    return 1
-  fi
-  buildkite-agent artifact download \
-    "artifacts/vllm-rocm-install/ci-base-image.txt" \
-    "${artifact_work_dir}" >/dev/null 2>&1 || true
-
-  archive=$(find "${artifact_work_dir}" -name "vllm-rocm-install.tar.gz" -type f | head -1)
-  if [[ -z "${archive}" || ! -f "${archive}" ]]; then
-    echo "ROCm wheel artifact archive was not found"
-    return 1
-  fi
-
-  metadata_file=$(find "${artifact_work_dir}" -name "ci-base-image.txt" -type f | head -1)
-  if [[ -n "${metadata_file}" && -s "${metadata_file}" ]]; then
-    base_image=$(tr -d '[:space:]' < "${metadata_file}")
-  fi
-
-  echo "--- Preparing local ROCm test image"
-  echo "Base image: ${base_image}"
-  docker pull "${base_image}" || return 1
-  base_digest=$(
-    docker image inspect \
-      --format='{{if .RepoDigests}}{{index .RepoDigests 0}}{{else}}{{.Id}}{{end}}' \
-      "${base_image}" 2>/dev/null || printf '%s' "${base_image}"
-  )
-
-  artifact_key=$(
-    {
-      printf 'base-image:%s\n' "${base_digest}"
-      sha256sum "${archive}"
-    } | sha256sum | cut -c1-24
-  )
-  artifact_image="rocm/vllm-ci-artifact:${artifact_key}"
-
-  if docker image inspect "${artifact_image}" >/dev/null 2>&1; then
-    echo "Using existing local ROCm artifact image: ${artifact_image}"
-    image_name="${artifact_image}"
-    return 0
-  fi
-
-  tar -xzf "${archive}" -C "${wheel_dir}" || return 1
-  if ! ls "${wheel_dir}"/*.whl >/dev/null 2>&1; then
-    echo "ROCm wheel artifact did not contain a wheel"
-    return 1
-  fi
-  if [[ ! -d "${wheel_dir}/tests" ]]; then
-    echo "ROCm wheel artifact did not contain the test workspace"
-    return 1
-  fi
-
-  cp "${wheel_dir}"/*.whl "${context_dir}/wheels/" || return 1
-  tar -C "${wheel_dir}" --exclude='*.whl' -cf - . \
-    | tar -C "${workspace_dir}" -xf - || return 1
-  cat > "${context_dir}/Dockerfile" <<'EOF'
-ARG BASE_IMAGE
-FROM ${BASE_IMAGE}
-COPY wheels/ /tmp/vllm-wheels/
-COPY workspace/ /vllm-workspace/
-RUN python3 -m pip install --no-deps --force-reinstall /tmp/vllm-wheels/*.whl \
-    && rm -rf /tmp/vllm-wheels
-WORKDIR /vllm-workspace
-EOF
-
-  echo "--- Building local ROCm test image"
-  docker build \
-    --pull=false \
-    --build-arg "BASE_IMAGE=${base_image}" \
-    -t "${artifact_image}" \
-    "${context_dir}" || return 1
-  image_name="${artifact_image}"
-  return 0
-}
-
 is_multi_node() {
  local cmds="$1"
  # Primary signal: NUM_NODES environment variable set by the pipeline
@@ -202,7 +114,8 @@ handle_pytest_exit() {
 # unquoted since they have no spaces and work fine.
 #
 # Already-quoted expressions (containing literal single quotes) are passed
-# through untouched to avoid double-quoting well-formed shell fragments.
+# through untouched to avoid double-quoting values injected by
+# apply_rocm_test_overrides.
 #
 # NOTE: This ONLY fixes -m/-k flags. It cannot recover arbitrary inner
 # double-quotes stripped by the calling shell (see header comment).
@@ -334,6 +247,102 @@ re_quote_pytest_markers() {
  echo "${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
 ###############################################################################
@@ -342,36 +351,20 @@ re_quote_pytest_markers() {
 echo "--- ROCm info"
 rocminfo

-# --- Docker status ---
-report_docker_usage
+# --- Docker housekeeping ---
+cleanup_docker

 # --- Pull test image ---
 echo "--- Pulling container"
-image_name="${VLLM_CI_FALLBACK_IMAGE:-rocm/vllm-ci:${BUILDKITE_COMMIT:-local}}"
-artifact_work_dir=""
+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() {
-  if docker container inspect "${container_name}" >/dev/null 2>&1; then
-    docker rm -f "${container_name}" || true
-  fi
-  if [[ "${VLLM_CI_REMOVE_TEST_IMAGE:-0}" == "1" ]]; then
-    docker image rm -f "${image_name}" || true
-  else
-    # Keep images by default so later jobs on the same AMD node can reuse layers.
-    echo "Keeping ROCm test image locally: ${image_name}"
-  fi
-  if [[ -n "${artifact_work_dir}" ]]; then
-    rm -rf "${artifact_work_dir}"
-  fi
+  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
 trap remove_docker_container EXIT

-if ! prepare_artifact_image; then
-  echo "Using full ROCm CI image: ${image_name}"
-  docker pull "${image_name}" || exit 1
-fi
-
 # --- Prepare commands ---
 echo "--- Running container"

@@ -379,14 +372,6 @@ HF_CACHE="$(realpath ~)/huggingface"
 mkdir -p "${HF_CACHE}"
 HF_MOUNT="/root/.cache/huggingface"

-# Hugging Face Hub defaults to 10s request/download timeouts, while the ROCm
-# CI image currently raises downloads to 60s. AMD model-test jobs routinely
-# start from a cold or partially-populated shared cache, and the 60s read cap
-# has still timed out before pytest reached the vLLM behavior under test.
-# Keep the CI default explicit and overridable from the Buildkite environment.
-: "${HF_HUB_DOWNLOAD_TIMEOUT:=300}"
-: "${HF_HUB_ETAG_TIMEOUT:=60}"
-
 # ---- Command source selection ----
 # Prefer VLLM_TEST_COMMANDS (preserves all inner quoting intact).
 # Fall back to $* for backward compatibility, but warn that inner
@@ -424,16 +409,10 @@ else
  echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
 fi

+commands=$(apply_rocm_test_overrides "$commands")
 echo "Final commands: $commands"

-MYPYTHONPATH="/vllm-workspace"
-
-container_job_id="${BUILDKITE_JOB_ID:-${BUILDKITE_PARALLEL_JOB:-0}}"
-container_job_id="${container_job_id//[^A-Za-z0-9_.-]/_}"
-container_job_id_short="${container_job_id:0:8}"
-CONTAINER_TMPDIR="/tmp/vllm-${container_job_id_short}"
-CONTAINER_CACHE_ROOT="/tmp/vllm-buildkite-${container_job_id}/cache"
-CONTAINER_PREFLIGHT="mkdir -p \"\$TMPDIR\" \"\$TORCHINDUCTOR_CACHE_DIR\" \"\$TRITON_CACHE_DIR\" \"\$VLLM_CACHE_ROOT\" \"\$XDG_CACHE_HOME\" && python -c \"import encodings, importlib.metadata as im, importlib.util as iu; [im.version(d) for d in ('transformers', 'torch', 'ray', 'sympy', 'markupsafe', 'vllm')]; missing=[m for m in ('torch.utils.model_zoo', 'transformers.models.nomic_bert', 'ray.dag', 'sympy.physics', 'markupsafe._speedups') if iu.find_spec(m) is None]; assert not missing, missing\""
+MYPYTHONPATH=".."

 # Verify GPU access
 render_gid=$(getent group render | cut -d: -f3)
@@ -510,8 +489,6 @@ else
    --group-add "$render_gid" \
    --rm \
    -e HF_TOKEN \
-    -e "HF_HUB_DOWNLOAD_TIMEOUT=${HF_HUB_DOWNLOAD_TIMEOUT}" \
-    -e "HF_HUB_ETAG_TIMEOUT=${HF_HUB_ETAG_TIMEOUT}" \
    -e AWS_ACCESS_KEY_ID \
    -e AWS_SECRET_ACCESS_KEY \
    -e BUILDKITE_PARALLEL_JOB \
@@ -519,15 +496,10 @@ else
    -v "${HF_CACHE}:${HF_MOUNT}" \
    -e "HF_HOME=${HF_MOUNT}" \
    -e "PYTHONPATH=${MYPYTHONPATH}" \
-    -e "TMPDIR=${CONTAINER_TMPDIR}/tmp" \
-    -e "TORCHINDUCTOR_CACHE_DIR=${CONTAINER_CACHE_ROOT}/torchinductor" \
-    -e "TRITON_CACHE_DIR=${CONTAINER_CACHE_ROOT}/triton" \
-    -e "VLLM_CACHE_ROOT=${CONTAINER_CACHE_ROOT}/vllm" \
-    -e "XDG_CACHE_HOME=${CONTAINER_CACHE_ROOT}/xdg" \
    -e "PYTORCH_ROCM_ARCH=" \
    --name "${container_name}" \
    "${image_name}" \
-    /bin/bash -c "${CONTAINER_PREFLIGHT} && ${commands}"
+    /bin/bash -c "${commands}"

  exit_code=$?
  handle_pytest_exit "$exit_code"
@@ -37,8 +37,7 @@ function cpu_tests() {
    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
-    pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.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
@@ -324,6 +324,23 @@ 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
 }
@@ -335,36 +352,17 @@ if [[ -z "${ZE_AFFINITY_MASK:-}" ]]; then
  echo "Warning: ZE_AFFINITY_MASK is not set. Proceeding without device affinity." >&2
 fi

-export CMDS="${commands}"
-export HF_TOKEN ZE_AFFINITY_MASK
-
-{
-  flock 9
-  if docker image inspect "${IMAGE}" >/dev/null 2>&1; then
-    echo "Image already exists locally, skipping pull"
-  else
-    echo "Image not found locally, pulling image..."
-    timeout 900 docker pull "${IMAGE}"
-    echo "Pull step completed"
-  fi
-
-  docker create \
+docker run \
    --device /dev/dri:/dev/dri \
    --net=host \
    --ipc=host \
    --privileged \
    -v /dev/dri/by-path:/dev/dri/by-path \
-    -v "${HOME}/.cache/huggingface:/root/.cache/huggingface" \
-    --entrypoint='' \
-    -e HF_TOKEN \
-    -e ZE_AFFINITY_MASK \
-    -e BUILDKITE_PARALLEL_JOB \
-    -e BUILDKITE_PARALLEL_JOB_COUNT \
-    -e CMDS \
+    -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}" \
-    bash -c 'set -e; echo "ZE_AFFINITY_MASK is ${ZE_AFFINITY_MASK:-}"; eval "$CMDS"' \
-    >/dev/null
-} 9>/tmp/docker-pull.lock
-
-docker start -a "${container_name}"
+    "${image_name}" \
+    bash -c 'set -e; echo "ZE_AFFINITY_MASK is ${ZE_AFFINITY_MASK:-}"; eval "$CMDS"'
@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.12" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.11" \
    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"

@@ -61,7 +61,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.12" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.11" \
    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"

@@ -0,0 +1,53 @@
+#!/bin/bash
+
+# This script build the CPU docker image and run the offline inference inside the container.
+# It serves a sanity check for compilation and basic model usage.
+set -ex
+
+image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
+container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+
+# Try building the docker image
+docker build -t "${image_name}" -f docker/Dockerfile.xpu .
+
+# Setup cleanup
+remove_docker_container() {
+  docker rm -f "${container_name}" || true
+}
+trap remove_docker_container EXIT
+
+# Run the image and test offline inference/tensor parallel
+docker run \
+    --device /dev/dri:/dev/dri \
+    --net=host \
+    --ipc=host \
+    --privileged \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    --entrypoint="" \
+    -e "HF_TOKEN=${HF_TOKEN}" \
+    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK}" \
+    --name "${container_name}" \
+    "${image_name}" \
+    bash -c '
+    set -e
+    echo $ZE_AFFINITY_MASK
+    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 --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 --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_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
+'
@@ -1,39 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-set -euo pipefail
-
-REQUIREMENTS_FILE="${KV_CONNECTORS_REQUIREMENTS:-/vllm-workspace/requirements/kv_connectors.txt}"
-
-uv pip install --system -r "${REQUIREMENTS_FILE}"
-
-NIXL_METADATA=$(python3 - <<'PY'
-import importlib.metadata as metadata
-
-import torch
-
-cuda_version = torch.version.cuda
-if cuda_version is None:
-    raise SystemExit("torch.version.cuda is not set")
-
-print(cuda_version.split(".", 1)[0], metadata.version("nixl"))
-PY
-)
-read -r CUDA_MAJOR NIXL_VERSION <<<"${NIXL_METADATA}"
-
-# nixl>=1.1.0 can install multiple CUDA wheel variants. Keep only the variant
-# matching this CI image so nixl_ep_cpp links against the available libcudart.
-uv pip uninstall --system nixl-cu12 nixl-cu13 2>/dev/null || true
-uv pip install --system --no-deps "nixl-cu${CUDA_MAJOR}==${NIXL_VERSION}"
-
-python3 - <<'PY'
-import importlib.metadata as metadata
-
-for package_name in ("nixl", "nixl-cu12", "nixl-cu13"):
-    try:
-        version = metadata.version(package_name)
-    except metadata.PackageNotFoundError:
-        version = "not installed"
-    print(f"{package_name}: {version}")
-PY
@@ -1,187 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-MODE="${1:-}"
-
-if [[ "$MODE" != "style-clippy" && "$MODE" != "test" ]]; then
-  echo "Usage: $0 {style-clippy|test}" >&2
-  exit 2
-fi
-
-ROOT_DIR="$(git rev-parse --show-toplevel)"
-cd "$ROOT_DIR"
-
-export CARGO_TERM_COLOR="${CARGO_TERM_COLOR:-always}"
-export CARGO_HOME="${CARGO_HOME:-$HOME/.cargo}"
-export RUSTUP_HOME="${RUSTUP_HOME:-$HOME/.rustup}"
-export PATH="$CARGO_HOME/bin:$PATH"
-
-log_section() {
-  echo "--- $*"
-}
-
-install_protoc() {
-  if command -v protoc >/dev/null 2>&1; then
-    return
-  fi
-
-  local version="${PROTOC_VERSION:-31.1}"
-  local arch
-  case "$(uname -m)" in
-    x86_64)
-      arch="x86_64"
-      ;;
-    aarch64|arm64)
-      arch="aarch_64"
-      ;;
-    *)
-      echo "Unsupported protoc architecture: $(uname -m)" >&2
-      return 1
-      ;;
-  esac
-
-  local url="https://github.com/protocolbuffers/protobuf/releases/download/v${version}/protoc-${version}-linux-${arch}.zip"
-  local tmp_dir
-  tmp_dir="$(mktemp -d)"
-
-  log_section "Installing protoc ${version}"
-  curl -L --proto '=https' --tlsv1.2 -sSf "$url" -o "$tmp_dir/protoc.zip"
-  mkdir -p "$CARGO_HOME/bin"
-  unzip -q "$tmp_dir/protoc.zip" bin/protoc 'include/*' -d "$CARGO_HOME"
-  chmod +x "$CARGO_HOME/bin/protoc"
-  rm -rf "$tmp_dir"
-}
-
-rust_toolchain() {
-  awk -F '"' '/channel[[:space:]]*=/ { print $2; exit }' rust-toolchain.toml
-}
-
-install_rust_toolchain() {
-  log_section "Installing Rust toolchain"
-  if ! command -v rustup >/dev/null 2>&1; then
-    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs \
-      | sh -s -- -y --profile minimal --default-toolchain none
-  fi
-
-  local toolchain
-  toolchain="$(rust_toolchain)"
-  rustup toolchain install "$toolchain" --profile minimal --component rustfmt,clippy
-  rustup component add --toolchain "$toolchain" rustfmt clippy
-}
-
-install_cargo_binstall() {
-  if command -v cargo-binstall >/dev/null 2>&1; then
-    return
-  fi
-
-  log_section "Installing cargo-binstall"
-  curl -L --proto '=https' --tlsv1.2 -sSf \
-    https://raw.githubusercontent.com/cargo-bins/cargo-binstall/main/install-from-binstall-release.sh \
-    | bash
-}
-
-install_cargo_sort() {
-  if command -v cargo-sort >/dev/null 2>&1; then
-    return
-  fi
-
-  log_section "Installing cargo-sort"
-  install_cargo_binstall
-  cargo binstall --no-confirm cargo-sort
-}
-
-install_cargo_nextest() {
-  if command -v cargo-nextest >/dev/null 2>&1; then
-    return
-  fi
-
-  log_section "Installing cargo-nextest"
-  install_cargo_binstall
-  cargo binstall --no-confirm --secure cargo-nextest
-}
-
-install_uv() {
-  if command -v uv >/dev/null 2>&1; then
-    return
-  fi
-
-  log_section "Installing uv"
-  curl -LsSf --proto '=https' --tlsv1.2 https://astral.sh/uv/install.sh \
-    | env UV_INSTALL_DIR="$CARGO_HOME/bin" sh
-}
-
-setup_pyo3_python() {
-  local python_version="${PYO3_PYTHON_VERSION:-3.12}"
-
-  log_section "Installing Python ${python_version} for PyO3 tests"
-  uv python install "$python_version"
-  PYO3_PYTHON="$(uv python find \
-    --managed-python \
-    --no-project \
-    --resolve-links \
-    "$python_version")"
-  export PYO3_PYTHON
-
-  local python_libdir
-  python_libdir="$("$PYO3_PYTHON" - <<'PY'
-import pathlib
-import sysconfig
-
-libdir = pathlib.Path(sysconfig.get_config_var("LIBDIR"))
-ldlibrary = sysconfig.get_config_var("LDLIBRARY")
-assert sysconfig.get_config_var("Py_ENABLE_SHARED") == 1
-assert ldlibrary
-assert (libdir / ldlibrary).exists(), libdir / ldlibrary
-print(libdir)
-PY
-)"
-
-  export LD_LIBRARY_PATH="${python_libdir}:${LD_LIBRARY_PATH:-}"
-  export LIBRARY_PATH="${python_libdir}:${LIBRARY_PATH:-}"
-}
-
-run_style_clippy() {
-  install_cargo_sort
-
-  log_section "Checking Rust formatting"
-  cargo fmt --manifest-path rust/Cargo.toml --all -- --check
-
-  log_section "Checking Cargo.toml ordering"
-  cargo sort --workspace --check rust
-
-  log_section "Running clippy"
-  cargo clippy \
-    --manifest-path rust/Cargo.toml \
-    --workspace \
-    --all-targets \
-    --all-features \
-    --locked \
-    -- \
-    -D warnings
-}
-
-run_tests() {
-  install_uv
-  setup_pyo3_python
-  install_cargo_nextest
-
-  log_section "Running cargo nextest"
-  cargo nextest run \
-    --manifest-path rust/Cargo.toml \
-    --workspace \
-    --all-features \
-    --locked \
-    --no-fail-fast
-}
-
-install_protoc
-install_rust_toolchain
-
-case "$MODE" in
-  style-clippy)
-    run_style_clippy
-    ;;
-  test)
-    run_tests
-    ;;
-esac
@@ -49,7 +49,6 @@ for BACK in "${BACKENDS[@]}"; do
    --data-parallel-size 2 \
    --enable-expert-parallel \
    --enable-eplb \
-    --eplb-config '{"use_async": false}' \
    --trust-remote-code \
    --max-model-len 2048 \
    --all2all-backend "$BACK" \
@@ -48,7 +48,7 @@ for BACK in "${BACKENDS[@]}"; do
    --enforce-eager \
    --enable-eplb \
    --all2all-backend "$BACK" \
-    --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true, "use_async":false}' \
+    --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true}' \
    --tensor-parallel-size "${TENSOR_PARALLEL_SIZE}" \
    --data-parallel-size "${DATA_PARALLEL_SIZE}" \
    --enable-expert-parallel \
@@ -70,7 +70,7 @@ echo "============================================"
 # ---- Install bfcl-eval if missing ----
 if ! python3 -c "import bfcl_eval" 2>/dev/null; then
    echo "Installing bfcl-eval..."
-    uv pip install "bfcl-eval>=2025.10.20.1,<2026"
+    pip install "bfcl-eval>=2025.10.20.1,<2026"
 fi

 # ---- Cleanup handler ----
@@ -100,7 +100,7 @@ SERVE_ARGS=(
    --tensor-parallel-size "$TP_SIZE"
    --max-model-len "$MAX_MODEL_LEN"
    --enforce-eager
-    --enable-prefix-caching
+    --no-enable-prefix-caching
 )

 # Append reasoning parser if specified
@@ -2,8 +2,8 @@ group: Attention
 depends_on: 
  - image-build
 steps:
- label: V1 attention (H100-MI300)
-  key: v1-attention-h100-mi300
+- label: V1 attention (H100)
+  key: v1-attention-h100
  timeout_in_minutes: 30
  device: h100
  source_file_dependencies:
@@ -13,20 +13,6 @@ steps:
    - tests/v1/attention
  commands:
    - pytest -v -s v1/attention
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 70
-      depends_on:
-      - image-build-amd
-      source_file_dependencies:
-      - vllm/config/attention.py
-      - vllm/model_executor/layers/attention
-      - vllm/v1/attention
-      - tests/v1/attention
-      - vllm/_aiter_ops.py
-      - vllm/envs.py
-      - vllm/platforms/rocm.py

 - label: V1 attention (B200)
  key: v1-attention-b200
@@ -10,9 +10,9 @@ steps:
  - vllm/
  - tests/basic_correctness/test_basic_correctness
  - tests/basic_correctness/test_cpu_offload
-  - tests/basic_correctness/test_mem.py
+  - tests/basic_correctness/test_cumem.py
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s basic_correctness/test_mem.py
+  - 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
@@ -27,5 +27,4 @@ steps:
  - vllm/compilation
  commands:
    - pytest -v -s v1/cudagraph/test_cudagraph_dispatch.py
-    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
-    - pytest -v -s v1/cudagraph/test_breakable_cudagraph.py
+    - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
@@ -11,7 +11,7 @@ steps:
    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
    - tests/v1/kv_connector/nixl_integration/
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - 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
@@ -22,7 +22,7 @@ steps:
    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
    - tests/v1/kv_connector/nixl_integration/
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - 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)
@@ -34,7 +34,7 @@ steps:
    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
    - tests/v1/kv_connector/nixl_integration/
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - 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)
@@ -46,35 +46,21 @@ steps:
    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
    - tests/v1/kv_connector/nixl_integration/
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - 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: 25
+  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:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - 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: Hybrid SSM NixlConnector PD prefix cache test (2 GPUs)
-  key: hybrid-ssm-nixlconnector-pd-prefix-cache-2-gpus
-  timeout_in_minutes: 25
-  working_dir: "/vllm-workspace/tests"
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
-    - vllm/v1/core/sched/
-    - vllm/v1/core/kv_cache_coordinator.py
-    - tests/v1/kv_connector/nixl_integration/
-  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
-    - bash v1/kv_connector/nixl_integration/run_mamba_prefix_cache_test.sh
-
 - label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
  key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
  timeout_in_minutes: 30
@@ -87,7 +73,7 @@ steps:
    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
    - tests/v1/kv_connector/nixl_integration/
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - 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)
@@ -101,8 +87,8 @@ steps:
    - vllm/v1/worker/kv_connector_model_runner_mixin.py
    - tests/v1/kv_connector/nixl_integration/
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
-    - bash v1/kv_connector/nixl_integration/config_sweep_spec_decode_test.sh
+    - 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
@@ -116,5 +102,5 @@ steps:
    - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
    - tests/v1/kv_connector/nixl_integration/
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
-    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
+    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
@@ -2,8 +2,8 @@ group: E2E Integration
 depends_on: 
  - image-build
 steps:
- label: DeepSeek V2-Lite Sync EPLB Accuracy
-  key: deepseek-v2-lite-sync-eplb-accuracy
+- label: DeepSeek V2-Lite Accuracy
+  key: deepseek-v2-lite-accuracy
  timeout_in_minutes: 60
  device: h100
  optional: true
@@ -12,8 +12,8 @@ steps:
  commands:
  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010

- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy
-  key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy
+- label: Qwen3-30B-A3B-FP8-block Accuracy
+  key: qwen3-30b-a3b-fp8-block-accuracy
  timeout_in_minutes: 60
  device: h100
  optional: true
@@ -22,8 +22,8 @@ steps:
  commands:
  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020

- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (B200)
-  key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-b200
+- label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
+  key: qwen3-30b-a3b-fp8-block-accuracy-b200
  timeout_in_minutes: 60
  device: b200-k8s
  optional: true
@@ -26,12 +26,6 @@ steps:
  - tests/test_jit_monitor.py
  commands:
  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 60
-      depends_on:
-      - image-build-amd

 - label: Engine (1 GPU)
  key: engine-1-gpu
@@ -42,12 +36,6 @@ steps:
  commands:
    - pytest -v -s v1/engine/test_preprocess_error_handling.py
    - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 40
-      depends_on:
-      - image-build-amd

 - label: e2e Scheduling (1 GPU)
  key: e2e-scheduling-1-gpu
@@ -58,15 +46,8 @@ steps:
    - tests/v1/e2e/general/
  commands:
    - pytest -v -s v1/e2e/general/test_async_scheduling.py
-  mirror:
-    amd:
-      device: mi250_1
-      timeout_in_minutes: 60
-      depends_on:
-      - image-build-amd

 - label: e2e Core (1 GPU)
-  device: h200_35gb
  key: e2e-core-1-gpu
  timeout_in_minutes: 30
  source_file_dependencies:
@@ -8,11 +8,10 @@ steps:
  working_dir: "/vllm-workspace/tests"
  source_file_dependencies:
  - vllm/entrypoints
-  - tests/entrypoints/unit_tests
-  - tests/entrypoints/weight_transfer
+  - tests/entrypoints/
  commands:
-  - pytest -v -s entrypoints/unit_tests
-  - pytest -v -s entrypoints/weight_transfer
+  - pytest -v -s entrypoints/openai/tool_parsers
+  - 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
@@ -21,36 +20,19 @@ steps:
  source_file_dependencies:
  - vllm/
  - tests/entrypoints/llm
+  - tests/entrypoints/offline_mode
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py --ignore=entrypoints/llm/offline_mode
+  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
  - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/llm/offline_mode # Needs to avoid interference with other tests
+  - 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)
-  key: entrypoints-integration-api-server
-  device: h200_35gb
-  timeout_in_minutes: 130
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/serve
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/serve --ignore=entrypoints/serve/dev/rpc
-  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-
- label: Entrypoints Integration (API Server OpenAI - Part 1)
+- 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"
@@ -60,15 +42,10 @@ steps:
  - tests/entrypoints/test_chat_utils
  commands:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/correctness
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 80
-      depends_on:
-      - image-build-amd
+  - 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)
+
+- 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"
@@ -77,71 +54,37 @@ steps:
  - tests/entrypoints/openai
  - tests/entrypoints/test_chat_utils
  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai/chat_completion
  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 80
-      depends_on:
-      - image-build-amd
+  - pytest -v -s entrypoints/openai/speech_to_text/
+  - pytest -v -s entrypoints/test_chat_utils.py

- label: Entrypoints Integration (API Server Generate)
-  key: entrypoints-integration-api-server-generate
+- 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/serve/instrumentator
  - tests/tool_use
-  - tests/entrypoints/tool_parsers
-  - tests/entrypoints/anthropic
-  - tests/entrypoints/generate
  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/serve/instrumentator
+  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/rpc
  - pytest -v -s tool_use
-  - pytest -v -s entrypoints/tool_parsers
-  - pytest -v -s entrypoints/generate
-  - pytest -v -s entrypoints/anthropic
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 60
-      depends_on:
-      - image-build-amd
-
- label: Entrypoints Integration (Responses API)
-  key: entrypoints-integration-responses-api
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai/responses
-  commands:
-  - pytest -v -s entrypoints/openai/responses
-
- label: Entrypoints Integration (Speech to Text)
-  device: h200_35gb
-  key: entrypoints-integration-speech_to_text
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/speech_to_text
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/speech_to_text
-
- label: Entrypoints Integration (Multimodal)
-  device: h200_35gb
-  key: entrypoints-integration-multimodal
-  timeout_in_minutes: 50
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/multimodal
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/multimodal

 - label: Entrypoints Integration (Pooling)
  key: entrypoints-integration-pooling
@@ -154,6 +97,16 @@ steps:
  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
  - 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:
+  - vllm/
+  - tests/entrypoints/openai/responses
+  commands:
+  - pytest -v -s entrypoints/openai/responses
+
 - label: OpenAI API Correctness
  key: openai-api-correctness
  timeout_in_minutes: 30
@@ -161,22 +114,6 @@ steps:
  source_file_dependencies:
  - csrc/
  - vllm/entrypoints/openai/
-  commands: # LMEval
+  - vllm/model_executor/models/whisper.py
+  commands: # LMEval+Transcription WER check
  - pytest -s entrypoints/openai/correctness/
-  mirror:
-    amd:
-      device: mi325_1
-      depends_on:
-      - image-build-amd
-      source_file_dependencies:
-      - csrc/
-      - vllm/entrypoints/openai/
-      - vllm/model_executor/layers/
-      - vllm/v1/attention/backends/
-      - vllm/v1/attention/selector.py
-      - vllm/_aiter_ops.py
-      - vllm/platforms/rocm.py
-      - vllm/model_executor/model_loader/
-      commands:
-      - bash ../tools/install_torchcodec_rocm.sh || exit 1
-      - pytest -s entrypoints/openai/correctness/
@@ -21,9 +21,8 @@ steps:
  - csrc/
  - tests/kernels/core
  - tests/kernels/test_concat_mla_q.py
-  - tests/kernels/test_fused_qk_norm_rope_gate.py
  commands:
-    - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_fused_qk_norm_rope_gate.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
@@ -39,28 +38,6 @@ steps:
  commands:
    - pytest -v -s kernels/core/test_minimax_reduce_rms.py

- label: Deepseek V4 Kernel Test (H100)
-  key: deepseek-v4-kernel-test-h100
-  timeout_in_minutes: 15
-  device: h100
-  source_file_dependencies:
-  - csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
-  - vllm/models/deepseek_v4/common/ops/
-  - tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py
-  commands:
-    - pytest -v -s kernels/test_fused_deepseek_v4_*.py
-
- label: Deepseek V4 Kernel Test (B200)
-  key: deepseek-v4-kernel-test-b200
-  timeout_in_minutes: 15
-  device: b200-k8s
-  source_file_dependencies:
-  - csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
-  - vllm/models/deepseek_v4/common/ops/
-  - tests/kernels/test_fused_deepseek_v4_qnorm_rope_kv_insert.py
-  commands:
-    - pytest -v -s kernels/test_fused_deepseek_v4_*.py
-
 - label: Kernels Attention Test %N
  key: kernels-attention-test
  timeout_in_minutes: 35
@@ -75,19 +52,6 @@ steps:
    - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2

- label: Kernels Attention DiffKV Test (H100)
-  key: kernels-attention-diffkv-test-h100
-  timeout_in_minutes: 20
-  device: h100
-  num_devices: 1
-  source_file_dependencies:
-  - vllm/v1/attention/ops/triton_unified_attention_diffkv.py
-  - vllm/v1/attention/backends/triton_attn_diffkv.py
-  - vllm/v1/attention/backends/flash_attn_diffkv.py
-  - tests/kernels/attention/test_triton_unified_attention_diffkv.py
-  commands:
-    - pytest -v -s kernels/attention/test_triton_unified_attention_diffkv.py
-
 - label: Kernels Quantization Test %N
  key: kernels-quantization-test
  timeout_in_minutes: 90
@@ -98,19 +62,6 @@ steps:
  commands:
    - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
  parallelism: 2
-  mirror:
-    amd:
-      device: mi325_1
-      source_file_dependencies:
-      - csrc/quantization/
-      - vllm/model_executor/layers/quantization
-      - tests/kernels/quantization
-      - tests/kernels/quantization/test_rocm_skinny_gemms.py
-      - vllm/_aiter_ops.py
-      - vllm/platforms/rocm.py
-      - vllm/model_executor/kernels/
-      depends_on:
-      - image-build-amd

 - label: Kernels MoE Test %N
  key: kernels-moe-test
@@ -140,7 +91,6 @@ steps:

 - label: Kernels KDA Test
  timeout_in_minutes: 20
-  device: h200_18gb
  source_file_dependencies:
  - vllm/model_executor/layers/fla/ops/kda.py
  - vllm/model_executor/layers/fla/ops/chunk_delta_h.py
@@ -186,12 +136,9 @@ steps:
  - csrc/quantization/fp4/
  - csrc/attention/mla/
  - csrc/quantization/cutlass_w8a8/moe/
-  - vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/experts/flashinfer_cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py
-  - vllm/model_executor/layers/fused_moe/oracle/nvfp4.py
-  - vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_one_sided.py
-  - vllm/model_executor/layers/fused_moe/prepare_finalize/flashinfer_nvlink_two_sided.py
+  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
+  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
+  - vllm/model_executor/layers/fused_moe/flashinfer_a2a_prepare_finalize.py
  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
  - vllm/v1/attention/backends/flashinfer.py
  - vllm/v1/attention/backends/mla/cutlass_mla.py
@@ -224,7 +171,6 @@ steps:
    - 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_trtllm_nvfp4_moe.py
    - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
    # e2e
    - pytest -v -s tests/models/quantization/test_nvfp4.py
@@ -237,7 +183,7 @@ steps:
  - vllm/utils/import_utils.py
  - tests/kernels/helion/
  commands:
-    - pip install helion==1.1.0
+    - pip install helion==1.0.0
    - pytest -v -s kernels/helion/

 
@@ -301,7 +247,7 @@ steps:
 - label: Kernels FusedMoE Layer Test (2 B200s)
  key: kernels-fusedmoe-layer-test-2-b200s
  timeout_in_minutes: 90
-  device: b200-k8s
+  device: b200
  num_devices: 2
  source_file_dependencies:
  - csrc/quantization/cutlass_w8a8/moe/
@@ -313,4 +259,3 @@ steps:
  - vllm/config
  commands:
    - pytest -v -s kernels/moe/test_moe_layer.py
-    - pytest -v -s kernels/moe/test_deepep_v2_moe.py
@@ -3,7 +3,6 @@ depends_on:
  - image-build
 steps:
 - label: LM Eval Small Models
-  device: h200_35gb
  key: lm-eval-small-models
  timeout_in_minutes: 75
  source_file_dependencies:
@@ -12,21 +11,6 @@ steps:
  autorun_on_main: true
  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 55
-      depends_on:
-      - image-build-amd
-      source_file_dependencies:
-      - csrc/
-      - vllm/model_executor/layers/quantization
-      - vllm/model_executor/models/
-      - vllm/model_executor/model_loader/
-      - vllm/v1/attention/backends/
-      - vllm/v1/attention/selector.py
-      - vllm/_aiter_ops.py
-      - vllm/platforms/rocm.py

 # - label: LM Eval Large Models (4 GPUs)(A100)
 #   device: a100
@@ -56,7 +40,7 @@ steps:
 - label: LM Eval Small Models (B200)
  key: lm-eval-small-models-b200
  timeout_in_minutes: 120
-  device: b200-k8s
+  device: b200
  optional: true
  source_file_dependencies:
  - csrc/
@@ -64,18 +48,6 @@ steps:
  commands:
  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt

- label: LM Eval Large Models (B200, EP)
-  key: lm-eval-large-models-b200-ep
-  timeout_in_minutes: 120
-  device: b200-k8s
-  optional: true
-  num_devices: 2
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell-ep.txt
-
 - label: LM Eval Qwen3.5 Models (B200)
  key: lm-eval-qwen3-5-models-b200
  timeout_in_minutes: 120
@@ -120,7 +92,7 @@ steps:

 - label: MoE Refactor Integration Test (B200 DP - TEMPORARY)
  key: moe-refactor-integration-test-b200-dp-temporary
-  device: b200-k8s
+  device: b200
  optional: true
  num_devices: 2
  commands:
@@ -130,7 +102,6 @@ steps:
 - label: LM Eval TurboQuant KV Cache
  key: lm-eval-turboquant-kv-cache
  timeout_in_minutes: 75
-  device: h200_18gb
  source_file_dependencies:
  - vllm/model_executor/layers/quantization/turboquant/
  - vllm/v1/attention/backends/turboquant_attn.py
@@ -156,7 +127,7 @@ steps:
 - label: GPQA Eval (GPT-OSS) (B200)
  key: gpqa-eval-gpt-oss-b200
  timeout_in_minutes: 120
-  device: b200-k8s
+  device: b200
  optional: true
  num_devices: 2
  source_file_dependencies:
@@ -168,7 +139,6 @@ steps:
    - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt

 - label: MRCR Eval Small Models
-  device: h200_35gb
  timeout_in_minutes: 30
  source_file_dependencies:
  - tests/evals/mrcr/
@@ -3,14 +3,13 @@ depends_on:
  - image-build
 steps:
 - label: LoRA %N
-  device: h200_35gb
  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_qwen3_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 
+    - 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


@@ -32,7 +31,7 @@ steps:
    # requires multi-GPU testing for validation.
    - pytest -v -s -x lora/test_chatglm3_tp.py
    - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_qwen3_with_multi_loras.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_qwen35_densemodel_lora.py
@@ -3,7 +3,6 @@ depends_on:
  - image-build
 steps:
 - label: V1 Spec Decode
-  device: h200_35gb
  key: v1-spec-decode
  timeout_in_minutes: 30
  source_file_dependencies:
@@ -52,7 +51,7 @@ steps:
    - pytest -v -s v1/test_outputs.py
  mirror:
    amd:
-      device: mi325_1
+      device: mi300_1
      depends_on:
      - image-build-amd

@@ -80,19 +79,17 @@ steps:
    - tests/v1/core
    - tests/v1/executor
    - tests/v1/kv_offload
-    - tests/v1/simple_kv_offload
    - tests/v1/worker
    - tests/v1/kv_connector/unit
    - tests/v1/metrics
    - tests/entrypoints/openai/correctness/test_lmeval.py
  commands:
-    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - 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/simple_kv_offload
    - pytest -v -s v1/worker
    - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'not cpu_test' v1/metrics
@@ -130,34 +127,6 @@ steps:
    - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
    - pytest -v -s -m 'cpu_test' v1/metrics

- label: Extract Hidden States Integration
-  key: extract-hidden-states-integration
-  timeout_in_minutes: 20
-  device: h200_18gb
-  source_file_dependencies:
-    - vllm/v1/spec_decode/extract_hidden_states.py
-    - vllm/model_executor/models/extract_hidden_states.py
-    - vllm/transformers_utils/configs/extract_hidden_states.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py
-    - tests/v1/kv_connector/extract_hidden_states_integration
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s v1/kv_connector/extract_hidden_states_integration
-
- label: Extract Hidden States Integration (2 GPUs)
-  key: extract-hidden-states-integration-2-gpus
-  timeout_in_minutes: 20
-  num_devices: 2
-  source_file_dependencies:
-    - vllm/v1/spec_decode/extract_hidden_states.py
-    - vllm/model_executor/models/extract_hidden_states.py
-    - vllm/transformers_utils/configs/extract_hidden_states.py
-    - vllm/distributed/kv_transfer/kv_connector/v1/example_hidden_states_connector.py
-    - tests/v1/kv_connector/extract_hidden_states_integration
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s -m 'distributed' v1/kv_connector/extract_hidden_states_integration
-
 - label: Regression
  key: regression
  timeout_in_minutes: 20
@@ -181,7 +150,6 @@ steps:
  working_dir: "/vllm-workspace/tests" # optional

 - label: Examples
-  device: h200_35gb
  key: examples
  timeout_in_minutes: 45
  working_dir: "/vllm-workspace/examples"
@@ -242,9 +210,7 @@ steps:
 - label: Python-only Installation
  key: python-only-installation
  depends_on: ~
-  optional: true
  timeout_in_minutes: 20
-  device: h200_18gb
  source_file_dependencies:
  - tests/standalone_tests/python_only_compile.sh
  - setup.py
@@ -252,7 +218,6 @@ steps:
  - bash standalone_tests/python_only_compile.sh

 - label: Async Engine, Inputs, Utils, Worker
-  device: h200_35gb
  key: async-engine-inputs-utils-worker
  timeout_in_minutes: 50
  source_file_dependencies:
@@ -296,7 +261,6 @@ steps:
  - vllm/model_executor/layers/quantization/quark/
  - vllm/multimodal/
  - vllm/outputs.py
-  - vllm/parser/
  - vllm/platforms/
  - vllm/pooling_params.py
  - vllm/ray/
@@ -308,7 +272,6 @@ steps:
  - vllm/transformers_utils/
  - vllm/utils/
  - vllm/v1/
-  - tests/test_envs.py
  - tests/test_inputs.py
  - tests/test_outputs.py
  - tests/test_pooling_params.py
@@ -316,43 +279,26 @@ steps:
  - tests/multimodal
  - tests/renderers
  - tests/standalone_tests/lazy_imports.py
+  - tests/tokenizers_
  - tests/reasoning
  - tests/tool_parsers
-  - tests/tokenizers_
-  - tests/parser
  - tests/transformers_utils
  - tests/config
  device: cpu-small
  commands:
  - python3 standalone_tests/lazy_imports.py
-  - pytest -v -s test_envs.py
  - pytest -v -s test_inputs.py
  - pytest -v -s test_outputs.py
  - pytest -v -s test_pooling_params.py
  - pytest -v -s test_ray_env.py
  - 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 tokenizers_
-  - pytest -v -s parser
  - pytest -v -s transformers_utils
  - pytest -v -s config

- label: Batch Invariance (A100)
-  key: batch-invariance-a100
-  timeout_in_minutes: 30
-  device: a100
-  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
-    - 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]
-
 - label: Batch Invariance (H100)
  key: batch-invariance-h100
  timeout_in_minutes: 30
@@ -385,10 +331,8 @@ steps:
    - 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
-    - pytest -v -s v1/determinism/test_nvfp4_batch_invariant_scaled_mm.py
  
 - label: Acceptance Length Test (Large Models) # optional
-  device: h200_35gb
  key: acceptance-length-test-large-models
  timeout_in_minutes: 25
  gpu: h100
@@ -14,12 +14,5 @@ steps:
  commands:
    - apt-get update && apt-get install -y curl libsodium23
    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    # Dump tracebacks of all threads if a test hangs, so a wedged GPU/CUDA
-    # init surfaces a stack instead of silently stalling.
-    - export PYTHONFAULTHANDLER=1
-    # Per-test watchdog: a single hung test (e.g. stuck during engine/CUDA
-    # init) fails fast with a traceback instead of running until the global
-    # build timeout. The `thread` method also handles hangs inside C/CUDA
-    # calls that the signal method cannot interrupt.
-    - pytest -v -s model_executor -m '(not slow_test)' --timeout=900 --timeout-method=thread
-    - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py --timeout=900 --timeout-method=thread
+    - pytest -v -s model_executor -m '(not slow_test)'
+    - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py
@@ -3,7 +3,6 @@ depends_on:
  - image-build
 steps:
 - label: Model Runner V2 Core Tests
-  device: h200_35gb
  key: model-runner-v2-core-tests
  timeout_in_minutes: 45
  source_file_dependencies:
@@ -27,7 +26,6 @@ steps:
  - 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
-  device: h200_35gb
  key: model-runner-v2-examples
  timeout_in_minutes: 45
  working_dir: "/vllm-workspace/examples"
@@ -94,16 +92,13 @@ steps:
    - vllm/v1/worker/gpu_worker.py
    - tests/distributed/test_pipeline_parallel.py
    - tests/distributed/test_pp_cudagraph.py
-    - tests/v1/distributed/test_pp_dp_v2.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"
-    - pytest -v -s v1/distributed/test_pp_dp_v2.py

 - label: Model Runner V2 Spec Decode
-  device: h200_35gb
  key: model-runner-v2-spec-decode
  timeout_in_minutes: 30
  working_dir: "/vllm-workspace/tests"
@@ -111,12 +106,13 @@ steps:
  - vllm/v1/worker/gpu/
  - vllm/v1/worker/gpu_worker.py
  - tests/v1/spec_decode/test_max_len.py
-  - tests/v1/spec_decode/test_rejection_sampler_utils.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_rejection_sampler_utils.py
+  - 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"
@@ -5,7 +5,6 @@ steps:
 - label: Basic Models Tests (Initialization)
  key: basic-models-tests-initialization
  timeout_in_minutes: 45
-  device: h200_18gb
  torch_nightly: true
  source_file_dependencies:
  - vllm/
@@ -18,7 +17,6 @@ steps:
    torch_nightly: {}

 - label: Basic Models Tests (Extra Initialization) %N
-  device: h200_35gb
  key: basic-models-tests-extra-initialization
  timeout_in_minutes: 45
  source_file_dependencies:
@@ -35,7 +33,6 @@ steps:
    torch_nightly: {}

 - label: Basic Models Tests (Other)
-  device: h200_35gb
  key: basic-models-tests-other
  timeout_in_minutes: 45
  source_file_dependencies:
@@ -58,3 +55,35 @@ steps:
  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
+  commands:
+    - pip install --upgrade git+https://github.com/huggingface/transformers
+    - 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
+
+- 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
@@ -5,7 +5,6 @@ steps:
 - label: Language Models Tests (Standard)
  key: language-models-tests-standard
  timeout_in_minutes: 25
-  device: h200_18gb
  source_file_dependencies:
  - vllm/
  - tests/models/language
@@ -50,8 +49,7 @@ steps:
  mirror:
    torch_nightly: {}
    amd:
-      device: mi325_1
-      timeout_in_minutes: 90
+      device: mi300_1
      depends_on:
      - image-build-amd
      commands:
@@ -60,7 +58,6 @@ steps:
      - 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
-  device: h200_35gb
  key: language-models-test-extended-generation
  timeout_in_minutes: 110
  optional: true
@@ -86,7 +83,6 @@ steps:
    - pytest -v -s models/language/generation_ppl_test

 - label: Language Models Test (Extended Pooling)  # 36min
-  device: h200_35gb
  key: language-models-test-extended-pooling
  timeout_in_minutes: 50
  optional: true
@@ -95,12 +91,6 @@ steps:
  - tests/models/language/pooling
  commands:
    - pytest -v -s models/language/pooling -m 'not core_model'
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 100
-      depends_on:
-      - image-build-amd

 - label: Language Models Test (MTEB)
  key: language-models-test-mteb
@@ -15,7 +15,7 @@ steps:
    - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
  mirror:
    amd:
-      device: mi325_1
+      device: mi300_1
      depends_on:
      - image-build-amd

@@ -33,12 +33,11 @@ steps:
    - pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
  mirror:
    amd:
-      device: mi325_1
+      device: mi300_1
      depends_on:
      - image-build-amd

 - label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
-  device: h200_35gb
  key: multi-modal-models-standard-3-llava-qwen2-vl
  timeout_in_minutes: 45
  source_file_dependencies:
@@ -50,12 +49,11 @@ steps:
    - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
  mirror:
    amd:
-      device: mi325_1
+      device: mi300_1
      depends_on:
      - image-build-amd

 - label: "Multi-Modal Models (Standard) 4: other + whisper"
-  device: h200_35gb
  key: multi-modal-models-standard-4-other-whisper
  timeout_in_minutes: 45
  source_file_dependencies:
@@ -94,7 +92,6 @@ steps:
    - pytest -v -s models/multimodal/processing/test_tensor_schema.py

 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
-  device: h200_35gb
  key: multi-modal-accuracy-eval-small-models
  timeout_in_minutes: 70
  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
@@ -118,12 +115,11 @@ steps:
    - pytest -v -s models/multimodal/test_mapping.py
  mirror:
    amd:
-      device: mi325_1
+      device: mi300_1
      depends_on:
      - image-build-amd

 - label: Multi-Modal Models (Extended Generation 2)
-  device: h200_35gb
  key: multi-modal-models-extended-generation-2
  optional: true
  source_file_dependencies:
@@ -134,7 +130,6 @@ steps:
    - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'

 - label: Multi-Modal Models (Extended Generation 3)
-  device: h200_35gb
  key: multi-modal-models-extended-generation-3
  optional: true
  source_file_dependencies:
@@ -153,12 +148,3 @@ steps:
  - tests/models/multimodal/pooling
  commands:
    - pytest -v -s models/multimodal/pooling -m 'not core_model'
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 60
-      depends_on:
-      - image-build-amd
-      source_file_dependencies:
-      - vllm/
-      - tests/models/multimodal/pooling
@@ -37,20 +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 plugins_tests/test_oot_registration_online.py # it needs a clean process
-  - pytest -v -s plugins_tests/test_oot_registration_offline.py # it needs a clean process
-  - pytest -v -s plugins_tests/lora_resolvers # unit tests for in-tree lora resolver plugins
-
-
- label: GGUF Plugin
-  key: gguf-plugin
-  device: h200_18gb
-  timeout_in_minutes: 30
-  soft_fail: true
-  optional: true
-  source_file_dependencies:
-  - vllm/model_executor/layers/quantization
-  - tests/plugins_tests/test_gguf_plugin.py
-  commands:
-    - pip install "vllm-gguf-plugin >= 0.0.2"
-    - pytest -v -s plugins_tests/gguf
+  - 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
@@ -3,7 +3,6 @@ depends_on:
  - image-build
 steps:
 - label: PyTorch Compilation Unit Tests
-  device: h200_35gb
  key: pytorch-compilation-unit-tests
  timeout_in_minutes: 10
  source_file_dependencies:
@@ -21,18 +21,6 @@ steps:
  - 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 Fusions
-  key: quantized-fusions
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - tests/fusion
-  - vllm/model_executor/layers/fusion
-  - vllm/model_executor/kernels/linear
-  - vllm/model_executor/layers/quantization/compressed_tensors
-  - vllm/model_executor/layers/quantization/modelopt.py
-  commands:
-    - pytest -v -s fusion/
-
 - label: Quantized MoE Test (B200)
  key: quantized-moe-test-b200
  timeout_in_minutes: 60
@@ -1,107 +0,0 @@
-group: Rust Frontend E2E
-depends_on:
-  - image-build
-steps:
- label: Rust Frontend OpenAI Coverage
-  timeout_in_minutes: 90
-  device: h200_18gb
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/benchmarks/
-  - vllm/entrypoints/openai/
-  - vllm/entrypoints/serve/
-  - vllm/v1/sample/
-  - tests/utils.py
-  - tests/benchmarks/test_serve_cli.py
-  - tests/entrypoints/openai/chat_completion/test_chat_completion.py
-  # - tests/entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py
-
-  # - tests/entrypoints/openai/completion/test_prompt_validation.py
-  - tests/entrypoints/openai/completion/test_shutdown.py
-  # - tests/entrypoints/openai/test_return_token_ids.py
-  # - tests/entrypoints/openai/test_uds.py
-  - tests/v1/sample/test_logprobs_e2e.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s benchmarks/test_serve_cli.py -k "not insecure and not (test_bench_serve and not test_bench_serve_chat)"
-  - pytest -v -s entrypoints/openai/chat_completion/test_chat_completion.py
-  # - pytest -v -s entrypoints/openai/chat_completion/test_chat_logit_bias_validation.py -k "not invalid"
-
-  # - pytest -v -s entrypoints/openai/completion/test_prompt_validation.py -k "not prompt_embeds"
-  - pytest -v -s entrypoints/openai/completion/test_shutdown.py -k "not engine_failure and not test_abort_timeout_exits_quickly"
-  # - pytest -v -s entrypoints/openai/test_return_token_ids.py
-  # - pytest -v -s entrypoints/openai/test_uds.py
-  - pytest -v -s v1/sample/test_logprobs_e2e.py -k "test_prompt_logprobs_e2e_server"
-
- label: Rust Frontend Serve/Admin Coverage
-  timeout_in_minutes: 60
-  device: h200_18gb
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - vllm/entrypoints/serve/
-  - vllm/v1/engine/
-  - tests/utils.py
-  # - tests/entrypoints/serve/dev/rpc/test_collective_rpc.py
-  - tests/entrypoints/serve/disagg/test_serving_tokens.py
-  - tests/entrypoints/serve/instrumentator/test_basic.py
-  - tests/entrypoints/serve/instrumentator/test_metrics.py
-  # - tests/entrypoints/serve/dev/test_sleep.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  # - pytest -v -s entrypoints/serve/dev/rpc/test_collective_rpc.py
-  - pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
-  - pytest -v -s entrypoints/serve/disagg/test_serving_tokens.py -k "not stream and not lora and not test_generate_logprobs and not stop_string_workflow"
-  - pytest -v -s entrypoints/serve/instrumentator/test_metrics.py -k "text and not show and not run_batch and not test_metrics_counts and not test_metrics_exist"
-  # - pytest -v -s entrypoints/serve/dev/test_sleep.py
-
- label: Rust Frontend Core Correctness
-  timeout_in_minutes: 30
-  device: h200_18gb
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - tests/utils.py
-  - tests/entrypoints/openai/correctness/test_lmeval.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
- label: Rust Frontend Tool Use
-  timeout_in_minutes: 60
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/entrypoints/openai/
-  - vllm/tool_parsers/
-  - tests/utils.py
-  - tests/tool_use/
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s tool_use --ignore=tool_use/mistral --models llama3.2 -k "not test_response_format_with_tool_choice_required and not test_parallel_tool_calls_false and not test_tool_call_and_choice"
-
- label: Rust Frontend Distributed
-  timeout_in_minutes: 30
-  num_devices: 4
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - rust/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/utils.py
-  - tests/v1/distributed/test_internal_lb_dp.py
-  commands:
-  - export VLLM_USE_RUST_FRONTEND=1
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - export NCCL_CUMEM_HOST_ENABLE=0
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py -k "not 4 and not server_info"
@@ -1,30 +0,0 @@
-group: Rust Frontend Cargo
-depends_on: []
-steps:
- label: Rust Frontend Cargo Style + Clippy
-  key: rust-frontend-cargo-style-clippy
-  depends_on: []
-  timeout_in_minutes: 30
-  device: cpu-medium
-  no_plugin: true
-  source_file_dependencies:
-  - rust/
-  - rust-toolchain.toml
-  - .buildkite/test_areas/rust_frontend_cargo.yaml
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh
-  commands:
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh style-clippy
-
- label: Rust Frontend Cargo Tests
-  key: rust-frontend-cargo-tests
-  depends_on: []
-  timeout_in_minutes: 30
-  device: cpu-medium
-  no_plugin: true
-  source_file_dependencies:
-  - rust/
-  - rust-toolchain.toml
-  - .buildkite/test_areas/rust_frontend_cargo.yaml
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh
-  commands:
-  - .buildkite/scripts/run-rust-frontend-cargo-ci.sh test
@@ -3,7 +3,6 @@ depends_on:
  - image-build
 steps:
 - label: Samplers Test
-  device: h200_35gb
  key: samplers-test
  timeout_in_minutes: 75
  source_file_dependencies:
@@ -11,7 +10,6 @@ steps:
  - vllm/sampling_metadata.py
  - tests/samplers
  - tests/conftest.py
-  - vllm/entrypoints/generate/beam_search
  commands:
    # VLLM_USE_FLASHINFER_SAMPLER defaults to 1 now, so we need to pin both
    # values explicitly to still cover the PyTorch-native (Triton) path.
@@ -16,7 +16,7 @@ steps:
 - label: Spec Decode Eagle Nightly B200
  key: spec-decode-eagle-nightly-b200
  timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
  optional: true
  source_file_dependencies:
    - vllm/v1/spec_decode/
@@ -32,31 +32,15 @@ steps:
  source_file_dependencies:
    - vllm/v1/spec_decode/
    - vllm/v1/worker/gpu/spec_decode/
-    - vllm/v1/attention/backends/
    - vllm/transformers_utils/configs/speculators/
    - tests/v1/e2e/spec_decode/
  commands:
    - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 65
-      depends_on:
-      - image-build-amd
-      source_file_dependencies:
-      - vllm/v1/spec_decode/
-      - vllm/v1/worker/gpu/spec_decode/
-      - vllm/model_executor/model_loader/
-      - vllm/v1/sample/
-      - vllm/model_executor/layers/
-      - vllm/transformers_utils/configs/speculators/
-      - tests/v1/e2e/spec_decode/
-      - vllm/platforms/rocm.py

 - label: Spec Decode Speculators + MTP Nightly B200
  key: spec-decode-speculators-mtp-nightly-b200
  timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
  optional: true
  source_file_dependencies:
    - vllm/v1/spec_decode/
@@ -76,20 +60,6 @@ steps:
    - tests/v1/e2e/spec_decode/
  commands:
    - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 65
-      depends_on:
-      - image-build-amd
-      source_file_dependencies:
-      - vllm/v1/spec_decode/
-      - vllm/v1/worker/gpu/spec_decode/
-      - vllm/model_executor/model_loader/
-      - vllm/v1/sample/
-      - vllm/model_executor/layers/
-      - tests/v1/e2e/spec_decode/
-      - vllm/platforms/rocm.py

 - label: Spec Decode Draft Model
  key: spec-decode-draft-model
@@ -101,20 +71,6 @@ steps:
    - tests/v1/e2e/spec_decode/
  commands:
    - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
-  mirror:
-    amd:
-      device: mi325_1
-      timeout_in_minutes: 50
-      depends_on:
-      - image-build-amd
-      source_file_dependencies:
-      - vllm/v1/spec_decode/
-      - vllm/v1/worker/gpu/spec_decode/
-      - vllm/model_executor/model_loader/
-      - vllm/v1/sample/
-      - vllm/model_executor/layers/
-      - tests/v1/e2e/spec_decode/
-      - vllm/platforms/rocm.py

 - label: Spec Decode Draft Model Nightly B200
  key: spec-decode-draft-model-nightly-b200
@@ -128,23 +84,23 @@ steps:
  commands:
    - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"

- label: Speculators Correctness
-  key: speculators-correctness
-  timeout_in_minutes: 60
+- 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_correctness.py
+    - tests/v1/spec_decode/test_speculators_dflash.py
  commands:
    - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
-    - pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test
+    - 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-k8s
+  device: b200
  optional: true
  source_file_dependencies:
    - vllm/v1/spec_decode/
@@ -2,7 +2,6 @@
 /build
 dist
 vllm/*.so
-vllm/vllm-rs

 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -32,4 +31,3 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
-rust/target/
@@ -23,35 +23,23 @@

 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
-/vllm/config @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @yewentao256 @ProExpertProg
+/vllm/config @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor @yewentao256 @ProExpertProg
 /vllm/config/cache.py @heheda12345

-# Config utils
-/vllm/config/utils.py @hmellor
-/vllm/engine/arg_utils.py @hmellor
-/vllm/utils/argparse_utils.py
-
 # Entrypoints
 /vllm/entrypoints/anthropic @mgoin @DarkLight1337
 /vllm/entrypoints/cli @hmellor @mgoin @DarkLight1337 @russellb
 /vllm/entrypoints/mcp @heheda12345
 /vllm/entrypoints/openai @aarnphm @chaunceyjiang @DarkLight1337 @russellb
-/vllm/entrypoints/speech_to_text/realtime @njhill
-/vllm/entrypoints/speech_to_text @NickLucche
+/vllm/entrypoints/openai/realtime @njhill
+/vllm/entrypoints/openai/speech_to_text @NickLucche
 /vllm/entrypoints/pooling @noooop
-/vllm/entrypoints/serve/sagemaker @DarkLight1337
+/vllm/entrypoints/sagemaker @DarkLight1337
 /vllm/entrypoints/serve @njhill
 /vllm/entrypoints/*.py @njhill
 /vllm/entrypoints/chat_utils.py @DarkLight1337
-/vllm/entrypoints/offline_utils.py @DarkLight1337
 /vllm/entrypoints/llm.py @DarkLight1337

-# Rust Frontend
-/rust/ @BugenZhao @njhill
-/build_rust.sh @BugenZhao @njhill
-/rust-toolchain.toml @BugenZhao @njhill
-/.buildkite/test_areas/rust* @BugenZhao @njhill
-
 # Input/Output Processing
 /vllm/sampling_params.py @njhill @NickLucche
 /vllm/pooling_params.py @noooop @DarkLight1337
@@ -80,27 +68,25 @@
 /vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery @NickLucche

 # Model runner V2
-/vllm/v1/worker/gpu @WoosukKwon @njhill @yewentao256
+/vllm/v1/worker/gpu @WoosukKwon @njhill
 /vllm/v1/worker/gpu/kv_connector.py @orozery

 # CI & building
-/.buildkite @Harry-Chen @khluu
-/docker/Dockerfile @Harry-Chen @khluu
-/pyproject.toml @khluu
-/setup.py @khluu
+/.buildkite @Harry-Chen
+/docker/Dockerfile @Harry-Chen

 # Test ownership
-/.buildkite/lm-eval-harness @mgoin
+/.buildkite/lm-eval-harness @mgoin 
 /tests/distributed/test_multi_node_assignment.py @youkaichao
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
-/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @aarnphm @NickLucche @AndreasKaratzas
-/tests/evals @mgoin @vadiklyutiy @AndreasKaratzas
-/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye @AndreasKaratzas
+/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @aarnphm @NickLucche
+/tests/evals @mgoin @vadiklyutiy
+/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye
 /tests/kernels/ir @ProExpertProg @tjtanaa
-/tests/models @DarkLight1337 @ywang96 @AndreasKaratzas
+/tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye @AndreasKaratzas
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -167,7 +153,9 @@ mkdocs.yaml @hmellor
 /vllm/model_executor/models/deepseek_mtp.py @luccafong

 # DeepseekV4-specific files
-/vllm/models/deepseek_v4 @zyongye
+/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
@@ -184,21 +172,21 @@ mkdocs.yaml @hmellor
 /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 @dllehr-amd
-/docker/Dockerfile.rocm* @tjtanaa @dllehr-amd @AndreasKaratzas
-/vllm/v1/attention/backends/rocm*.py @tjtanaa @dllehr-amd
-/vllm/v1/attention/backends/mla/rocm*.py @tjtanaa @dllehr-amd
-/vllm/v1/attention/ops/rocm*.py @tjtanaa @dllehr-amd
-/vllm/model_executor/layers/fused_moe/rocm*.py @tjtanaa @dllehr-amd
-/csrc/rocm @tjtanaa @dllehr-amd
-/requirements/*rocm* @tjtanaa @AndreasKaratzas
-/tests/**/*rocm* @tjtanaa @AndreasKaratzas
+/vllm/**/*rocm* @tjtanaa
+/docker/Dockerfile.rocm* @gshtras @tjtanaa
+/vllm/v1/attention/backends/rocm*.py @gshtras @tjtanaa
+/vllm/v1/attention/backends/mla/rocm*.py @gshtras @tjtanaa
+/vllm/v1/attention/ops/rocm*.py @gshtras @tjtanaa
+/vllm/model_executor/layers/fused_moe/rocm*.py @gshtras @tjtanaa
+/csrc/rocm @gshtras @tjtanaa
+/requirements/*rocm* @tjtanaa
+/tests/**/*rocm* @tjtanaa
 /docs/**/*rocm* @tjtanaa
 /vllm/**/*quark* @tjtanaa
-/tests/**/*quark* @tjtanaa @AndreasKaratzas
+/tests/**/*quark* @tjtanaa
 /docs/**/*quark* @tjtanaa
-/vllm/**/*aiter* @tjtanaa @AndreasKaratzas
-/tests/**/*aiter* @tjtanaa @AndreasKaratzas
+/vllm/**/*aiter* @tjtanaa
+/tests/**/*aiter* @tjtanaa

 # TPU
 /vllm/v1/worker/tpu* @NickLucche
@@ -21,6 +21,7 @@ updates:
      - dependency-name: "torchvision"
      - dependency-name: "xformers"
      - dependency-name: "lm-format-enforcer"
+      - dependency-name: "gguf"
      - dependency-name: "compressed-tensors"
      - dependency-name: "ray[cgraph]" # Ray Compiled Graph
      - dependency-name: "lm-eval"
@@ -21,9 +21,6 @@ pull_request_rules:
    - check-failure=pre-commit
    - -closed
    - -draft
-    - or:
-      - label=ready
-      - label=verified
  actions:
    comment:
      message: |
@@ -39,6 +36,18 @@ pull_request_rules:

        For future commits, `pre-commit` will run automatically on changed files before each commit.

+        > [!TIP]
+        > <details>
+        > <summary>Is <code>mypy</code> failing?</summary>
+        > <br/>
+        > <code>mypy</code> is run differently in CI. If the failure is related to this check, please use the following command to run it locally:
+        >
+        > ```bash
+        > # For mypy (substitute "3.10" with the failing version if needed)
+        > pre-commit run --hook-stage manual mypy-3.10
+        > ```
+        > </details>
+
 - name: comment-dco-failure
  description: Comment on PR when DCO check fails
  conditions:
@@ -94,19 +103,6 @@ pull_request_rules:
      add:
        - frontend

- name: label-rust
-  description: Automatically apply rust label
-  conditions:
-    - label != stale
-    - or:
-      - files~=(?i)rust
-      - title~=(?i)rust
-      - title~=(?i)vllm-rs
-  actions:
-    label:
-      add:
-        - rust
-
 - name: label-llama
  description: Automatically apply llama label
  conditions:
@@ -144,12 +140,12 @@ pull_request_rules:
    - label != stale
    - or:
      - files~=^examples/.*mistral.*\.py
-      - files~=^tests/.*(?:mistral|voxtral|mixtral|pixtral).*\.py
-      - files~=^vllm/model_executor/models/.*(?:mistral|voxtral|mixtral|pixtral).*\.py
+      - files~=^tests/.*mistral.*\.py
+      - files~=^vllm/model_executor/models/.*mistral.*\.py
      - files~=^vllm/reasoning/.*mistral.*\.py
      - files~=^vllm/tool_parsers/.*mistral.*\.py
-      - files~=^vllm/transformers_utils/.*(?:mistral|voxtral|pixtral).*\.py
-      - title~=(?i)(?:mistral|ministral|voxtral|mixtral|pixtral)
+      - files~=^vllm/transformers_utils/.*mistral.*\.py
+      - title~=(?i)Mistral
  actions:
    label:
      add:
@@ -10,7 +10,7 @@ jobs:
        runs-on: ubuntu-latest
        steps:
            -   name: Add label
-                uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
+                uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
                with:
                    script: |
                        github.rest.issues.addLabels({
@@ -14,7 +14,7 @@ jobs:
    steps:
      - name: Label issues based on keywords
        id: label-step
-        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3  # v9.0.0
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
        with:
          script: |
            // Configuration: Add new labels and keywords here
@@ -315,7 +315,7 @@ jobs:

      - name: CC users for labeled issues
        if: steps.label-step.outputs.labels_added != '[]'
-        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3  # v9.0.0
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
        with:
          script: |
            // Configuration: Map labels to GitHub users to CC
@@ -392,7 +392,7 @@ jobs:

      - name: Request missing ROCm info from issue author
        if: contains(steps.label-step.outputs.labels_added, 'rocm') && contains(toJSON(github.event.issue.labels.*.name), 'bug')
-        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3  # v9.0.0
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
        with:
          script: |
            const body = (context.payload.issue.body || '').toLowerCase();
@@ -12,7 +12,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Update PR description
-        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
        with:
          script: |
            const { owner, repo } = context.repo;
@@ -55,7 +55,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Post welcome comment for first-time contributors
-        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
+        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
        with:
          script: |
            const { owner, repo } = context.repo;
@@ -20,7 +20,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
    - name: Check PR label and author merge count
-      uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
+      uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
      with:
        script: |
          const { data: pr } = await github.rest.pulls.get({
@@ -9,7 +9,7 @@ PATH=${cuda_home}/bin:$PATH
 LD_LIBRARY_PATH=${cuda_home}/lib64:$LD_LIBRARY_PATH

 # Install requirements
-if [ "$(echo "$2" | cut -d. -f1)" = "12" ]; then
+if [ "$(echo $2 | cut -d. -f1)" = "12" ]; then
    sed -i 's/^nvidia-cutlass-dsl\[cu13\]>=/nvidia-cutlass-dsl>=/' requirements/cuda.txt
 fi
 $python_executable -m pip install -r requirements/build/cuda.txt -r requirements/cuda.txt
@@ -17,10 +17,7 @@ $python_executable -m pip install -r requirements/build/cuda.txt -r requirements
 # Limit the number of parallel jobs to avoid OOM
 export MAX_JOBS=1
 # Make sure release wheels are built for the following architectures
-# Do not add +PTX here: vLLM filters torch's top-level PTX flag when it
-# converts global gencode flags into per-kernel arch lists. If a specific
-# kernel needs PTX, add +PTX to that kernel's CMake arch list instead.
-export TORCH_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9 9.0 10.0 12.0"
+export TORCH_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9 9.0 10.0 12.0+PTX"

 bash tools/check_repo.sh

@@ -15,7 +15,7 @@ jobs:
      actions: write
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/stale@eb5cf3af3ac0a1aa4c9c45633dd1ae542a27a899 # v10.3.0
+      - uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
        with:
          # Increasing this value ensures that changes to this workflow
          # propagate to all issues and PRs in days rather than months
@@ -15,9 +15,6 @@ vllm/third_party/flashmla/flash_mla_interface.py
 # DeepGEMM vendored package built from source
 vllm/third_party/deep_gemm/

-# fmha_sm100 vendored package built from source
-vllm/third_party/fmha_sm100/
-
 # triton jit
 .triton

@@ -29,9 +26,6 @@ __pycache__/
 # C extensions
 *.so

-# Rust binaries
-vllm/vllm-rs
-
 # Distribution / packaging
 .Python
 build/
@@ -236,7 +230,7 @@ actionlint
 shellcheck*/

 # Ignore moe/marlin_moe gen code
-csrc/libtorch_stable/moe/marlin_moe_wna16/kernel_*
+csrc/moe/marlin_moe_wna16/kernel_*

 # Ignore ep_kernels_workspace folder
 ep_kernels_workspace/
@@ -21,7 +21,7 @@ repos:
  rev: v21.1.2
  hooks:
  - id: clang-format
-    exclude: 'csrc/libtorch_stable/moe/topk_softmax_kernels.cu|vllm/third_party/.*'
+    exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
    types_or: [c++, cuda]
    args: [--style=file, --verbose]
 - repo: https://github.com/DavidAnson/markdownlint-cli2
@@ -148,27 +148,33 @@ repos:
    language: python
    entry: python tools/pre_commit/generate_nightly_torch_test.py
    files: ^requirements/test/cuda\.(in|txt)$
-  - id: mypy-3.10 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
-    name: Run mypy for Python 3.10
-    entry: python tools/pre_commit/mypy.py "3.10"
+  - id: mypy-local
+    name: Run mypy locally for lowest supported Python version
+    entry: python tools/pre_commit/mypy.py 0 "3.10"
+    stages: [pre-commit] # Don't run in CI
    <<: &mypy_common
      language: python
      types_or: [python, pyi]
      require_serial: true
-      additional_dependencies: ["mypy==1.20.2", regex, types-cachetools, types-setuptools, types-PyYAML, types-requests, types-torch, pydantic]
+      additional_dependencies: ["mypy[faster-cache]==1.19.1", regex, types-cachetools, types-setuptools, types-PyYAML, types-requests, types-torch, pydantic]
+  - id: mypy-3.10 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
+    name: Run mypy for Python 3.10
+    entry: python tools/pre_commit/mypy.py 1 "3.10"
+    <<: *mypy_common
+    stages: [manual] # Only run in CI
  - id: mypy-3.11 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
    name: Run mypy for Python 3.11
-    entry: python tools/pre_commit/mypy.py "3.11"
+    entry: python tools/pre_commit/mypy.py 1 "3.11"
    <<: *mypy_common
    stages: [manual] # Only run in CI
  - id: mypy-3.12 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
    name: Run mypy for Python 3.12
-    entry: python tools/pre_commit/mypy.py "3.12"
+    entry: python tools/pre_commit/mypy.py 1 "3.12"
    <<: *mypy_common
    stages: [manual] # Only run in CI
  - id: mypy-3.13 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
    name: Run mypy for Python 3.13
-    entry: python tools/pre_commit/mypy.py "3.13"
+    entry: python tools/pre_commit/mypy.py 1 "3.13"
    <<: *mypy_common
    stages: [manual] # Only run in CI
  - id: shellcheck
@@ -216,12 +222,6 @@ repos:
    name: Update Dockerfile dependency graph
    entry: tools/pre_commit/update-dockerfile-graph.sh
    language: script
-  - id: test-nonroot-entrypoint
-    name: Test non-root entrypoint wrapper
-    entry: bash docker/entrypoints/test_vllm_nonroot_entrypoint.sh
-    language: system
-    pass_filenames: false
-    files: ^docker/entrypoints/(vllm-nonroot-entrypoint|test_vllm_nonroot_entrypoint)\.sh$
  - id: check-forbidden-imports
    name: Check for forbidden imports
    entry: python tools/pre_commit/check_forbidden_imports.py
@@ -256,32 +256,6 @@ repos:
    entry: python tools/pre_commit/check_boolean_context_manager.py
    language: python
    types: [python]
-  # Rust hooks. These shell out to `cargo`; tools/pre_commit/rust-check.sh
-  # skips with a warning when cargo is not installed.
-  - id: rust-cargo-autoinherit
-    name: Rust - Normalize Cargo manifests with autoinherit
-    entry: tools/pre_commit/rust-check.sh autoinherit --prefer-simple-dotted
-    language: script
-    pass_filenames: false
-    require_serial: true
-    stages: [pre-commit] # Only run locally as Buildkite will cover this
-    files: ^rust/(Cargo\.toml|src/.*/Cargo\.toml)$
-  - id: rust-cargo-sort
-    name: Rust - Sort Cargo manifest sections
-    entry: tools/pre_commit/rust-check.sh sort --workspace
-    language: script
-    pass_filenames: false
-    require_serial: true
-    stages: [pre-commit] # Only run locally as Buildkite will cover this
-    files: ^rust/(Cargo\.toml|src/.*/Cargo\.toml)$
-  - id: rust-cargo-fmt
-    name: Rust - Format code
-    entry: tools/pre_commit/rust-check.sh fmt
-    language: script
-    pass_filenames: false
-    require_serial: true
-    stages: [pre-commit] # Only run locally as Buildkite will cover this
-    files: ^rust/.*(\.rs|Cargo\.toml|rustfmt\.toml)$
  # Keep `suggestion` last
  - id: suggestion
    name: Suggestion
@@ -9,8 +9,45 @@ build:
    python: "3.12"
  jobs:
    post_checkout:
+      - |
+        if [ "$READTHEDOCS_VERSION_TYPE" = "external" ]; then
+          MAX_WAIT=300
+          INTERVAL=60
+          ELAPSED=0
+          while :; do
+            RAW=$(curl -sS -w "\n%{http_code}" "https://api.github.com/repos/vllm-project/vllm/commits/${READTHEDOCS_GIT_COMMIT_HASH}/check-runs?check_name=pre-run-check&filter=latest")
+            HTTP_CODE=$(printf %s "$RAW" | tail -n1)
+            BODY=$(printf %s "$RAW" | head -n -1)
+            if [ "$HTTP_CODE" != "200" ]; then
+              echo "GitHub API returned HTTP $HTTP_CODE (likely rate-limited); skipping pre-run-check gate."
+              break
+            fi
+            STATUS=$(printf %s "$BODY" | python3 -c "import sys, json; r=json.load(sys.stdin).get(\"check_runs\",[]); print((r[0].get(\"status\") or \"\") if r else \"none\")")
+            CONCLUSION=$(printf %s "$BODY" | python3 -c "import sys, json; r=json.load(sys.stdin).get(\"check_runs\",[]); print((r[0].get(\"conclusion\") or \"\") if r else \"\")")
+            if [ "$STATUS" = "none" ]; then
+              echo "no pre-run-check found for this commit; skipping gate."
+              break
+            fi
+            if [ -n "$CONCLUSION" ]; then
+              echo "pre-run-check conclusion: $CONCLUSION"
+              if [ "$CONCLUSION" = "failure" ] || [ "$CONCLUSION" = "cancelled" ] || [ "$CONCLUSION" = "timed_out" ]; then
+                echo "pre-run-check did not pass; failing docs build."
+                exit 1
+              fi
+              break
+            fi
+            if [ "$ELAPSED" -ge "$MAX_WAIT" ]; then
+              echo "pre-run-check status=$STATUS after ${MAX_WAIT}s; skipping gate."
+              break
+            fi
+            echo "pre-run-check status=$STATUS; waiting ${INTERVAL}s..."
+            sleep "$INTERVAL"
+            ELAPSED=$((ELAPSED + INTERVAL))
+          done
+        else
+          echo "Not a PR build (version type=$READTHEDOCS_VERSION_TYPE); skipping pre-run-check gate."
+        fi
      - git fetch origin main --unshallow --no-tags --filter=blob:none || true
-      - bash docs/pre_run_check.sh
    pre_create_environment:
      - pip install uv
    create_environment:
@@ -98,31 +98,7 @@ pre-commit run --all-files
 pre-commit run ruff-check --all-files

 # Run mypy as it is in CI:
-pre-commit run mypy-3.12 --all-files --hook-stage manual
-```
-
-The line length limit for Python code is 88 characters. If you are not sure, use pre-commit to check.
-
-Use [Google-style docstrings](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) (`Args:`/`Returns:`/`Raises:` sections), not reStructuredText/Sphinx fields (`:param:`, `:return:`, `:rtype:`).
-
-### Coding style guidelines
-
-Follow these rules for all code changes in this repository:
-
- Try to match existing code style.
- Code should be self-documenting and self-explanatory.
- Keep comments and docstrings minimal and concise.
- Assume the reader is familiar with vLLM.
-
-### Diagnosing CI failures
-
-Buildkite logs are public; no login needed. Details: [docs/contributing/ci/failures.md](docs/contributing/ci/failures.md).
-
-```bash
-# All failed-job logs for a PR's latest build (current branch's PR if omitted):
-.buildkite/scripts/ci-fetch-log.sh --pr <PR>
-# Any Buildkite build or job URL also works:
-.buildkite/scripts/ci-fetch-log.sh "<buildkite_url>"
+pre-commit run mypy-3.10 --all-files --hook-stage manual
 ```

 ### Commit messages
@@ -4,7 +4,6 @@ include requirements/cuda.txt
 include requirements/rocm.txt
 include requirements/cpu.txt
 include CMakeLists.txt
-include tools/build_rust.py

 recursive-include cmake *
 recursive-include csrc *
@@ -34,15 +34,6 @@ Vulnerabilities that cause denial of service or partial disruption, but do not a

 Minor issues such as informational disclosures, logging errors, non-exploitable flaws, or weaknesses that require local or high-privilege access and offer negligible impact. Examples include side channel attacks or hash collisions. These issues often have CVSS scores less than 4.0

-## Fix disclosure policy
-
-When a security report is accepted, the fix process depends on the severity:
-
-* **CRITICAL and HIGH severity**: Fixes are developed in a private security fork and coordinated with the prenotification group before public disclosure.
-* **MODERATE and LOW severity**: Fixes are developed and submitted as public pull requests. These issues do not require embargo since they do not enable arbitrary code execution or significant data breach, and public visibility accelerates community review and adoption of the fix.
-
-The vulnerability management team reserves the right to adjust the disclosure approach on a case-by-case basis, taking into account factors such as active exploitation, unusual attack surface, or coordination requirements with downstream vendors.
-
 ## Prenotification policy

 For certain security issues of CRITICAL, HIGH, or MODERATE severity level, we may prenotify certain organizations or vendors that ship vLLM. The purpose of this prenotification is to allow for a coordinated release of fixes for severe issues.
@@ -108,6 +108,7 @@ python benchmark.py \
    --backends flash triton flashinfer \
    --batch-specs "q2k" "8q1s1k" "2q2k_32q1s1k" \
    --num-layers 10 \
+    --repeats 5 \
    --output-csv results.csv
 ```

@@ -163,17 +164,14 @@ python benchmark.py \
 # Model configuration
 --num-layers N                      # Number of layers
 --head-dim N                        # Head dimension
--v-head-dim N                      # Value head dimension (defaults to --head-dim)
 --num-q-heads N                     # Query heads
 --num-kv-heads N                    # KV heads
 --block-size N                      # Block size
--kv-lora-rank N                    # MLA KV LoRA rank
--qk-nope-head-dim N                # MLA non-RoPE QK head dim
--qk-rope-head-dim N                # MLA RoPE QK head dim

 # Benchmark settings
 --device DEVICE                     # Device (default: cuda:0)
--warmup-ms N                       # Warmup window in ms for triton do_bench
+--repeats N                         # Repetitions
+--warmup-iters N                    # Warmup iterations
 --profile-memory                    # Profile memory usage

 # Parameter sweeps
@@ -213,6 +211,8 @@ config = BenchmarkConfig(
    num_kv_heads=1,
    block_size=128,
    device="cuda:0",
+    repeats=5,
+    warmup_iters=3,
 )

 # CUTLASS MLA with specific num_kv_splits
@@ -253,10 +253,14 @@ formatter.save_json(results, "output.json")

 ## Tips

-**1. Save results** - Always use `--output-csv` or `--output-json`
+**1. Warmup matters** - Use `--warmup-iters 10` for stable results

-**2. Test incrementally** - Start with `--num-layers 1`
+**2. Multiple repeats** - Use `--repeats 20` for low variance

-**3. Extended grammar** - Leverage spec decode, chunked prefill patterns
+**3. Save results** - Always use `--output-csv` or `--output-json`

-**4. Parameter sweeps** - Use `--sweep-param` and `--sweep-values` to find optimal values
+**4. Test incrementally** - Start with `--num-layers 1 --repeats 1`
+
+**5. Extended grammar** - Leverage spec decode, chunked prefill patterns
+
+**6. Parameter sweeps** - Use `--sweep-param` and `--sweep-values` to find optimal values
@@ -26,9 +26,6 @@ Examples:
 """

 import argparse
-import os
-import shutil
-import subprocess
 import sys
 from dataclasses import replace
 from pathlib import Path
@@ -86,15 +83,13 @@ def run_benchmark(config: BenchmarkConfig, **kwargs) -> BenchmarkResult:
        else:
            return run_standard_attention_benchmark(config)
    except Exception as e:
-        error_msg = str(e) or repr(e)
        return BenchmarkResult(
            config=config,
            mean_time=float("inf"),
-            median_time=float("inf"),
            std_time=0,
            min_time=float("inf"),
            max_time=float("inf"),
-            error=error_msg,
+            error=str(e),
        )


@@ -120,12 +115,9 @@ def run_model_parameter_sweep(
    """
    all_results = []

-    sweep_desc = (
-        f"{sweep.param_name} = {sweep.values}"
-        if sweep.param_name
-        else f"{len(sweep.values)} configurations"
+    console.print(
+        f"[yellow]Model sweep mode: testing {sweep.param_name} = {sweep.values}[/]"
    )
-    console.print(f"[yellow]Model sweep mode: testing {sweep_desc}[/]")

    total = len(backends) * len(batch_specs) * len(sweep.values)

@@ -133,9 +125,9 @@ def run_model_parameter_sweep(
        for backend in backends:
            for spec in batch_specs:
                for value in sweep.values:
-                    # Create config with modified model parameter(s)
+                    # Create config with modified model parameter
                    config_args = base_config_args.copy()
-                    sweep.apply(config_args, value)
+                    config_args[sweep.param_name] = value

                    # Create config with original backend for running
                    clean_config = BenchmarkConfig(
@@ -152,21 +144,13 @@ def run_model_parameter_sweep(
                    all_results.append(result)

                    if not result.success:
-                        err_label = (
-                            f"{sweep.param_name}={value}"
-                            if sweep.param_name
-                            else f"{value}"
-                        )
                        console.print(
-                            f"[red]Error {backend} {spec} {err_label}"
-                            f": {result.error}[/]"
+                            f"[red]Error {backend} {spec} {sweep.param_name}="
+                            f"{value}: {result.error}[/]"
                        )

                    pbar.update(1)

-    if base_config_args.get("ncu_profile"):
-        return all_results
-
    # Display sweep results - create separate table for each parameter value
    console.print("\n[bold green]Model Parameter Sweep Results:[/]")
    formatter = ResultsFormatter(console)
@@ -200,10 +184,7 @@ def run_model_parameter_sweep(
    )

    for param_value in sorted_param_values:
-        label = (
-            f"{sweep.param_name} = {param_value}" if sweep.param_name else param_value
-        )
-        console.print(f"\n[bold cyan]{label}[/]")
+        console.print(f"\n[bold cyan]{sweep.param_name} = {param_value}[/]")
        param_results = by_param_value[param_value]

        # Create modified results with original backend names
@@ -219,9 +200,8 @@ def run_model_parameter_sweep(
        formatter.print_table(modified_results, backends, compare_to_fastest=True)

    # Show optimal backend for each (param_value, batch_spec) combination
-    sweep_name = sweep.param_name or "config"
    console.print(
-        f"\n[bold cyan]Optimal backend for each ({sweep_name}, batch_spec):[/]"
+        f"\n[bold cyan]Optimal backend for each ({sweep.param_name}, batch_spec):[/]"
    )

    # Group by (param_value, batch_spec)
@@ -256,10 +236,7 @@ def run_model_parameter_sweep(
    for param_value, spec in sorted_keys:
        # Print header when param value changes
        if param_value != current_param_value:
-            header = (
-                f"{sweep.param_name}={param_value}" if sweep.param_name else param_value
-            )
-            console.print(f"\n  [bold]{header}:[/]")
+            console.print(f"\n  [bold]{sweep.param_name}={param_value}:[/]")
            current_param_value = param_value

        results = by_param_and_spec[(param_value, spec)]
@@ -345,9 +322,6 @@ def run_parameter_sweep(

                    pbar.update(1)

-    if base_config_args.get("ncu_profile"):
-        return all_results
-
    # Display sweep results
    console.print("\n[bold green]Sweep Results:[/]")
    backend_labels = [sweep.get_label(b, v) for b in backends for v in sweep_values]
@@ -500,35 +474,11 @@ def main():
    parser.add_argument("--num-q-heads", type=int, default=32, help="Query heads")
    parser.add_argument("--num-kv-heads", type=int, default=8, help="KV heads")
    parser.add_argument("--block-size", type=int, default=16, help="Block size")
-    parser.add_argument(
-        "--v-head-dim",
-        type=int,
-        default=None,
-        help="Value head dimension (defaults to --head-dim if unset)",
-    )
-
-    # MLA-specific model dimensions
-    parser.add_argument(
-        "--kv-lora-rank", type=int, default=None, help="MLA KV LoRA rank"
-    )
-    parser.add_argument(
-        "--qk-nope-head-dim", type=int, default=None, help="MLA non-RoPE QK head dim"
-    )
-    parser.add_argument(
-        "--qk-rope-head-dim", type=int, default=None, help="MLA RoPE QK head dim"
-    )

    # Benchmark settings
    parser.add_argument("--device", default="cuda:0", help="Device")
-    parser.add_argument(
-        "--warmup-ms",
-        type=int,
-        default=None,
-        help=(
-            "Warmup window in ms for triton's do_bench (default: triton's own). "
-            "Has no effect with CUDA graphs; pass --no-cuda-graphs to use it."
-        ),
-    )
+    parser.add_argument("--repeats", type=int, default=1, help="Repetitions")
+    parser.add_argument("--warmup-iters", type=int, default=3, help="Warmup iterations")
    parser.add_argument("--profile-memory", action="store_true", help="Profile memory")
    parser.add_argument(
        "--kv-cache-dtype",
@@ -541,33 +491,10 @@ def main():
        action=argparse.BooleanOptionalAction,
        default=True,
        help=(
-            "Use triton do_bench_cudagraph (True) or do_bench (False) "
-            "for timing. CUDA graphs eliminate CPU launch overhead "
-            "(default: True)"
+            "Launch kernels with CUDA graphs to eliminate CPU overhead"
+            "in measurements (default: True)"
        ),
    )
-    parser.add_argument(
-        "--num-splits",
-        type=int,
-        default=None,
-        help="FlashAttention split-K factor (0=auto heuristic, 1=disabled, >1=force N)",
-    )
-    parser.add_argument(
-        "--ncu-profile",
-        action="store_true",
-        default=False,
-        help=(
-            "Enable Nsight Compute profiling mode. Automatically wraps the "
-            "script with ncu, capturing a profile with source correlation. "
-            "Use --ncu-output to set the output file name."
-        ),
-    )
-    parser.add_argument(
-        "--ncu-output",
-        type=str,
-        default="profile",
-        help="Output file name for ncu profile (default: 'profile').",
-    )

    # Parameter sweep (use YAML config for advanced sweeps)
    parser.add_argument(
@@ -649,28 +576,23 @@ def main():
            model = yaml_config["model"]
            args.num_layers = model.get("num_layers", args.num_layers)
            args.head_dim = model.get("head_dim", args.head_dim)
-            args.v_head_dim = model.get("v_head_dim", args.v_head_dim)
            args.num_q_heads = model.get("num_q_heads", args.num_q_heads)
            args.num_kv_heads = model.get("num_kv_heads", args.num_kv_heads)
            args.block_size = model.get("block_size", args.block_size)
-            # MLA-specific dimensions
-            args.kv_lora_rank = model.get("kv_lora_rank", args.kv_lora_rank)
-            args.qk_nope_head_dim = model.get("qk_nope_head_dim", args.qk_nope_head_dim)
-            args.qk_rope_head_dim = model.get("qk_rope_head_dim", args.qk_rope_head_dim)

        # Benchmark settings (top-level keys)
        if "device" in yaml_config:
            args.device = yaml_config["device"]
-        if "warmup_ms" in yaml_config:
-            args.warmup_ms = yaml_config["warmup_ms"]
+        if "repeats" in yaml_config:
+            args.repeats = yaml_config["repeats"]
+        if "warmup_iters" in yaml_config:
+            args.warmup_iters = yaml_config["warmup_iters"]
        if "profile_memory" in yaml_config:
            args.profile_memory = yaml_config["profile_memory"]
        if "kv_cache_dtype" in yaml_config:
            args.kv_cache_dtype = yaml_config["kv_cache_dtype"]
        if "cuda_graphs" in yaml_config:
            args.cuda_graphs = yaml_config["cuda_graphs"]
-        if "ncu_profile" in yaml_config:
-            args.ncu_profile = yaml_config["ncu_profile"]

        # Parameter sweep configuration
        if "parameter_sweep" in yaml_config:
@@ -690,7 +612,7 @@ def main():
        if "model_parameter_sweep" in yaml_config:
            sweep_config = yaml_config["model_parameter_sweep"]
            args.model_parameter_sweep = ModelParameterSweep(
-                param_name=sweep_config.get("param_name"),
+                param_name=sweep_config["param_name"],
                values=sweep_config["values"],
                label_format=sweep_config.get(
                    "label_format", "{backend}_{param_name}_{value}"
@@ -709,32 +631,6 @@ def main():

        console.print()

-    # Re-exec under ncu if --ncu-profile and not already inside ncu. This runs
-    # after YAML processing so ncu_profile set via config file is honored.
-    if args.ncu_profile and "_NCU_INNER" not in os.environ:
-        ncu = shutil.which("ncu")
-        if ncu is None:
-            print("Error: 'ncu' not found in PATH", file=sys.stderr)
-            sys.exit(1)
-        cmd = [
-            ncu,
-            "--profile-from-start",
-            "off",
-            "--set",
-            "full",
-            "--import-source",
-            "yes",
-            "-o",
-            args.ncu_output,
-            sys.executable,
-            *sys.argv,
-        ]
-        env = os.environ.copy()
-        env["CUTE_DSL_LINEINFO"] = "1"
-        env["_NCU_INNER"] = "1"
-        print(f"Launching: {' '.join(cmd)}")
-        sys.exit(subprocess.call(cmd, env=env))
-
    # Handle CLI-based parameter sweep (if not from YAML)
    if (
        (not hasattr(args, "parameter_sweep") or args.parameter_sweep is None)
@@ -759,18 +655,6 @@ def main():
    console.print(f"Batch specs: {', '.join(args.batch_specs)}")
    console.print(f"KV cache dtype: {args.kv_cache_dtype}")
    console.print(f"CUDA graphs: {args.cuda_graphs}")
-    if args.warmup_ms is not None and args.cuda_graphs:
-        console.print(
-            "[yellow]Warning: --warmup-ms is ignored with CUDA graphs "
-            "(do_bench_cudagraph warms up internally). Pass --no-cuda-graphs "
-            "to use it.[/]"
-        )
-    if args.num_splits == 0 and args.cuda_graphs:
-        console.print(
-            "[yellow]Warning: --num-splits 0 (FA3 heuristic) is not CUDA-graph "
-            "compatible and may fail or fall back. Pass --no-cuda-graphs or use "
-            "--num-splits >=1.[/]"
-        )
    console.print()

    init_workspace_manager(args.device)
@@ -778,15 +662,6 @@ def main():
    # Run benchmarks
    all_results = []

-    # Under ncu profiling the kernels run only to be captured by the profiler;
-    # timings are placeholder zeros, so the result tables and saved metrics are
-    # skipped. The Nsight Compute report (--ncu-output) holds the real data.
-    if args.ncu_profile:
-        console.print(
-            "[dim]ncu profiling enabled: result tables and saved metrics are "
-            "skipped (timings are placeholder zeros).[/]"
-        )
-
    # Handle special mode: decode_vs_prefill comparison
    if hasattr(args, "mode") and args.mode == "decode_vs_prefill":
        console.print("[yellow]Mode: Decode vs Prefill pipeline comparison[/]")
@@ -833,11 +708,11 @@ def main():
                        num_kv_heads=args.num_kv_heads,
                        block_size=args.block_size,
                        device=args.device,
+                        repeats=args.repeats,
+                        warmup_iters=args.warmup_iters,
                        profile_memory=args.profile_memory,
                        kv_cache_dtype=args.kv_cache_dtype,
                        use_cuda_graphs=args.cuda_graphs,
-                        ncu_profile=args.ncu_profile,
-                        warmup_ms=args.warmup_ms,
                    )

                    # Add decode pipeline config
@@ -874,7 +749,6 @@ def main():
                        result = BenchmarkResult(
                            config=config,
                            mean_time=timing["mean"],
-                            median_time=timing.get("median", timing["mean"]),
                            std_time=timing["std"],
                            min_time=timing["min"],
                            max_time=timing["max"],
@@ -896,7 +770,6 @@ def main():
                        result = BenchmarkResult(
                            config=config,
                            mean_time=float("inf"),
-                            median_time=float("inf"),
                            std_time=0,
                            min_time=float("inf"),
                            max_time=float("inf"),
@@ -906,9 +779,6 @@ def main():

                pbar.update(1)

-        if args.ncu_profile:
-            return
-
        # Display decode vs prefill results
        console.print("\n[bold green]Decode vs Prefill Results:[/]")

@@ -988,20 +858,15 @@ def main():
        base_config_args = {
            "num_layers": args.num_layers,
            "head_dim": args.head_dim,
-            "v_head_dim": args.v_head_dim,
            "num_q_heads": args.num_q_heads,
            "num_kv_heads": args.num_kv_heads,
            "block_size": args.block_size,
            "device": args.device,
+            "repeats": args.repeats,
+            "warmup_iters": args.warmup_iters,
            "profile_memory": args.profile_memory,
            "kv_cache_dtype": args.kv_cache_dtype,
            "use_cuda_graphs": args.cuda_graphs,
-            "ncu_profile": args.ncu_profile,
-            "warmup_ms": args.warmup_ms,
-            "num_splits": args.num_splits,
-            "kv_lora_rank": args.kv_lora_rank,
-            "qk_nope_head_dim": args.qk_nope_head_dim,
-            "qk_rope_head_dim": args.qk_rope_head_dim,
        }
        all_results = run_model_parameter_sweep(
            backends,
@@ -1017,17 +882,15 @@ def main():
        base_config_args = {
            "num_layers": args.num_layers,
            "head_dim": args.head_dim,
-            "v_head_dim": args.v_head_dim,
            "num_q_heads": args.num_q_heads,
            "num_kv_heads": args.num_kv_heads,
            "block_size": args.block_size,
            "device": args.device,
+            "repeats": args.repeats,
+            "warmup_iters": args.warmup_iters,
            "profile_memory": args.profile_memory,
            "kv_cache_dtype": args.kv_cache_dtype,
            "use_cuda_graphs": args.cuda_graphs,
-            "ncu_profile": args.ncu_profile,
-            "warmup_ms": args.warmup_ms,
-            "num_splits": args.num_splits,
        }
        all_results = run_parameter_sweep(
            backends, args.batch_specs, base_config_args, args.parameter_sweep, console
@@ -1051,17 +914,15 @@ def main():
                            batch_spec=spec,
                            num_layers=args.num_layers,
                            head_dim=args.head_dim,
-                            v_head_dim=getattr(args, "v_head_dim", None),
                            num_q_heads=args.num_q_heads,
                            num_kv_heads=args.num_kv_heads,
                            block_size=args.block_size,
                            device=args.device,
+                            repeats=args.repeats,
+                            warmup_iters=args.warmup_iters,
                            profile_memory=args.profile_memory,
                            kv_cache_dtype=args.kv_cache_dtype,
                            use_cuda_graphs=args.cuda_graphs,
-                            ncu_profile=args.ncu_profile,
-                            warmup_ms=args.warmup_ms,
-                            num_splits=args.num_splits,
                        )

                        result = run_benchmark(config)
@@ -1074,10 +935,9 @@ def main():

                        pbar.update(1)

-            if not args.ncu_profile:
-                console.print("\n[bold green]Results:[/]")
-                formatter = ResultsFormatter(console)
-                formatter.print_table(decode_results, backends)
+            console.print("\n[bold green]Results:[/]")
+            formatter = ResultsFormatter(console)
+            formatter.print_table(decode_results, backends)

        # Run prefill backend comparison
        if prefill_backends:
@@ -1102,8 +962,9 @@ def main():
                            num_kv_heads=args.num_kv_heads,
                            block_size=args.block_size,
                            device=args.device,
+                            repeats=args.repeats,
+                            warmup_iters=args.warmup_iters,
                            profile_memory=args.profile_memory,
-                            warmup_ms=args.warmup_ms,
                            prefill_backend=pb,
                        )

@@ -1119,17 +980,16 @@ def main():

                        pbar.update(1)

-            if not args.ncu_profile:
-                console.print("\n[bold green]Prefill Backend Results:[/]")
-                formatter = ResultsFormatter(console)
-                formatter.print_table(
-                    prefill_results, prefill_backends, compare_to_fastest=True
-                )
+            console.print("\n[bold green]Prefill Backend Results:[/]")
+            formatter = ResultsFormatter(console)
+            formatter.print_table(
+                prefill_results, prefill_backends, compare_to_fastest=True
+            )

        all_results = decode_results + prefill_results

-    # Save results (skip ncu profiling runs: timings are placeholder zeros)
-    if all_results and not args.ncu_profile:
+    # Save results
+    if all_results:
        formatter = ResultsFormatter(console)
        if args.output_csv:
            formatter.save_csv(all_results, args.output_csv)
@@ -15,8 +15,6 @@ from batch_spec import get_batch_type, parse_batch_spec
 from rich.console import Console
 from rich.table import Table

-from vllm.triton_utils import triton
-

 def batch_spec_sort_key(spec: str) -> tuple[int, int, int]:
    """
@@ -36,30 +34,6 @@ def batch_spec_sort_key(spec: str) -> tuple[int, int, int]:
        return (0, 0, 0)


-def run_do_bench(
-    benchmark_fn,
-    use_cuda_graphs: bool,
-    warmup_ms: int | None = None,
-) -> list[float]:
-    kwargs: dict[str, Any] = {"return_mode": "all"}
-    if use_cuda_graphs:
-        result = triton.testing.do_bench_cudagraph(benchmark_fn, **kwargs)
-    else:
-        if warmup_ms is not None:
-            kwargs["warmup"] = warmup_ms
-        result = triton.testing.do_bench(benchmark_fn, **kwargs)
-    return result
-
-
-def run_ncu_profile(benchmark_fn) -> None:
-    benchmark_fn()
-    torch.accelerator.synchronize()
-    torch.cuda.cudart().cudaProfilerStart()
-    benchmark_fn()
-    torch.accelerator.synchronize()
-    torch.cuda.cudart().cudaProfilerStop()
-
-
 # Mock classes for vLLM attention infrastructure


@@ -208,37 +182,18 @@ class ParameterSweep:

@dataclass
 class ModelParameterSweep:
-    """Configuration for sweeping model configuration parameter(s).
+    """Configuration for sweeping a model configuration parameter."""

-    Supports two modes:
-    - Single param: param_name="head_dim", values=[128, 256, 512]
-    - Multi param: values=[{head_dim: 192, v_head_dim: 128}, {head_dim: 256}]
-      When values are dicts, each dict's keys are applied as config overrides.
-    """
-
-    param_name: str | None = None
-    values: list[Any] | None = None
-    label_format: str = "{backend}_{param_name}_{value}"
+    param_name: str  # Name of the model config parameter to sweep (e.g., "num_q_heads")
+    values: list[Any]  # List of values to test
+    label_format: str = "{backend}_{param_name}_{value}"  # Result label template

    def get_label(self, backend: str, value: Any) -> str:
        """Generate a label for a specific parameter value."""
-        if isinstance(value, dict):
-            return self.label_format.format(
-                backend=backend, param_name=self.param_name, value=value, **value
-            )
        return self.label_format.format(
            backend=backend, param_name=self.param_name, value=value
        )

-    def apply(self, config_args: dict, value: Any) -> None:
-        """Apply a sweep value to config args."""
-        if isinstance(value, dict):
-            config_args.update(value)
-        elif self.param_name is not None:
-            config_args[self.param_name] = value
-        else:
-            raise ValueError("param_name must be set if sweep values are not dicts")
-

@dataclass
 class BenchmarkConfig:
@@ -253,10 +208,10 @@ class BenchmarkConfig:
    block_size: int
    device: str
    dtype: torch.dtype = torch.float16
+    repeats: int = 1
+    warmup_iters: int = 3
    profile_memory: bool = False
    use_cuda_graphs: bool = False
-    ncu_profile: bool = False
-    warmup_ms: int | None = None

    # "auto" or "fp8"
    kv_cache_dtype: str = "auto"
@@ -271,7 +226,6 @@ class BenchmarkConfig:
    # Backend-specific tuning
    num_kv_splits: int | None = None  # CUTLASS MLA
    reorder_batch_threshold: int | None = None  # FlashAttn MLA, FlashMLA
-    num_splits: int | None = None  # FlashAttention split-K (0=auto, 1=disabled)


@dataclass
@@ -280,7 +234,6 @@ class BenchmarkResult:

    config: BenchmarkConfig
    mean_time: float  # seconds
-    median_time: float  # seconds
    std_time: float  # seconds
    min_time: float  # seconds
    max_time: float  # seconds
@@ -299,7 +252,6 @@ class BenchmarkResult:
        return {
            "config": asdict(self.config),
            "mean_time": self.mean_time,
-            "median_time": self.median_time,
            "std_time": self.std_time,
            "min_time": self.min_time,
            "max_time": self.max_time,
@@ -56,6 +56,8 @@ backends:
  - TOKENSPEED_MLA  # Blackwell + R1 dims + FP8 KV (use --kv-cache-dtype fp8)

 device: "cuda:0"
+repeats: 100
+warmup_iters: 10
 profile_memory: true

 # Backend-specific tuning
@@ -51,6 +51,8 @@ backends:
  - FLASHMLA         # Hopper only

 device: "cuda:0"
+repeats: 5
+warmup_iters: 3
 profile_memory: true

 # Analyze chunked prefill workspace size impact
@@ -124,3 +124,5 @@ prefill_backends:
  - tokenspeed

 device: "cuda:0"
+repeats: 20
+warmup_iters: 5
@@ -53,4 +53,6 @@ backends:
  - FLASHINFER_MLA_SPARSE

 device: "cuda:0"
+repeats: 100
+warmup_iters: 10
 profile_memory: true
@@ -57,4 +57,6 @@ backends:
  - FLASHINFER_MLA_SPARSE

 device: "cuda:0"
+repeats: 10
+warmup_iters: 3
 profile_memory: true
@@ -63,6 +63,8 @@ model:

 # Benchmark settings
 device: "cuda:0"
+repeats: 15          # More repeats for spec decode variance
+warmup_iters: 5
 profile_memory: false

 # Output
@@ -49,6 +49,8 @@ backends:

 # Benchmark settings
 device: "cuda:0"
+repeats: 10  # More repeats for statistical significance
+warmup_iters: 5
 profile_memory: false

 # Test these threshold values for optimization
@@ -43,4 +43,6 @@ backends:
  - FLASHINFER

 device: "cuda:0"
+repeats: 5
+warmup_iters: 3
 profile_memory: false
@@ -1,142 +0,0 @@
-# Standard attention decode benchmark configuration
-# Sweeps num_q_heads and num_kv_heads to isolate effects of:
-#   1. GQA ratio (fixed num_q_heads=32, vary num_kv_heads)
-#   2. Absolute head count (fixed 4:1 ratio, vary scale)
-
-model:
-  num_layers: 32
-  num_q_heads: 32 # Base value, overridden by sweep
-  num_kv_heads: 8 # Base value, overridden by sweep
-  head_dim: 128
-  block_size: 16
-
-# Head count sweep: each entry overrides num_q_heads, num_kv_heads, and
-# head_dim where it differs from the base (128). Head counts are per-GPU
-# (i.e. after TP sharding).
-#
-# Group A — vary GQA ratio (fixed q=32, head_dim=128):
-#   32:32 (MHA), 32:8 (GQA 4:1), 32:4 (GQA 8:1), 32:1 (MQA)
-#
-# Groups B-E — real model configs at various TP degrees:
-#   Model              head_dim  Full    TP2      TP4      TP8
-#   Llama 3 8B           128    32:8   16:4      8:2      4:1
-#   Llama 3 70B          128    64:8   32:4     16:2      8:1
-#   GPT-OSS 120B          64    64:8   32:4     16:2      8:1
-#   Llama 3 405B         128   128:8   64:4     32:2     16:1
-model_parameter_sweep:
-  values:
-    # --- head_dim=128 (Llama 3 family) ---
-    - { num_q_heads: 32, num_kv_heads: 32, head_dim: 128 } # MHA 1:1
-    - { num_q_heads: 32, num_kv_heads: 1, head_dim: 128 } # MQA 32:1
-    - { num_q_heads: 4, num_kv_heads: 1, head_dim: 128 } # Llama 3 8B TP8
-    - { num_q_heads: 8, num_kv_heads: 2, head_dim: 128 } # Llama 3 8B TP4
-    - { num_q_heads: 16, num_kv_heads: 4, head_dim: 128 } # Llama 3 8B TP2
-    - { num_q_heads: 32, num_kv_heads: 8, head_dim: 128 } # Llama 3 8B TP1 / GQA 4:1
-    - { num_q_heads: 8, num_kv_heads: 1, head_dim: 128 } # Llama 3 70B TP8
-    - { num_q_heads: 16, num_kv_heads: 2, head_dim: 128 } # Llama 3 70B TP4
-    - { num_q_heads: 32, num_kv_heads: 4, head_dim: 128 } # Llama 3 70B TP2 / GQA 8:1
-    - { num_q_heads: 64, num_kv_heads: 8, head_dim: 128 } # Llama 3 70B TP1
-    - { num_q_heads: 16, num_kv_heads: 1, head_dim: 128 } # Llama 3 405B TP8
-    - { num_q_heads: 32, num_kv_heads: 2, head_dim: 128 } # Llama 3 405B TP4
-    - { num_q_heads: 64, num_kv_heads: 4, head_dim: 128 } # Llama 3 405B TP2
-    - { num_q_heads: 128, num_kv_heads: 8, head_dim: 128 } # Llama 3 405B TP1
-    # --- head_dim=64 (GPT-OSS 120B) ---
-    - { num_q_heads: 8, num_kv_heads: 1, head_dim: 64 } # GPT-OSS 120B TP8
-    - { num_q_heads: 16, num_kv_heads: 2, head_dim: 64 } # GPT-OSS 120B TP4
-    - { num_q_heads: 32, num_kv_heads: 4, head_dim: 64 } # GPT-OSS 120B TP2
-    - { num_q_heads: 64, num_kv_heads: 8, head_dim: 64 } # GPT-OSS 120B TP1
-  label_format: "{backend}_q{num_q_heads}kv{num_kv_heads}d{head_dim}"
-
-batch_specs:
-  # ---- batch_size x seq_len grid (decode: q_len=1) ----
-  # Small grid for quick iteration. Uncomment for full sweep.
-
-  # Batch size 1
-  - "q1s1k"
-  - "q1s512"
-  - "q1s2k"
-  - "q1s4k"
-  - "q1s8k"
-  - "q1s16k"
-  - "q1s32k"
-
-  # Batch size 2
-  - "2q1s512"
-  - "2q1s1k"
-  - "2q1s2k"
-  - "2q1s4k"
-  - "2q1s8k"
-  - "2q1s16k"
-  - "2q1s32k"
-
-  # Batch size 4
-  - "4q1s512"
-  - "4q1s1k"
-  - "4q1s2k"
-  - "4q1s4k"
-  - "4q1s8k"
-  - "4q1s16k"
-  - "4q1s32k"
-
-  # Batch size 8
-  - "8q1s1k"
-  - "8q1s512"
-  - "8q1s2k"
-  - "8q1s4k"
-  - "8q1s8k"
-  - "8q1s16k"
-  - "8q1s32k"
-
-  # Batch size 16
-  - "16q1s512"
-  - "16q1s1k"
-  - "16q1s2k"
-  - "16q1s4k"
-  - "16q1s8k"
-  - "16q1s16k"
-  - "16q1s32k"
-
-  # Batch size 32
-  - "32q1s512"
-  - "32q1s1k"
-  - "32q1s2k"
-  - "32q1s4k"
-  - "32q1s8k"
-  - "32q1s16k"
-  - "32q1s32k"
-
-  # Batch size 64
-  - "64q1s1k"
-  - "64q1s512"
-  - "64q1s2k"
-  - "64q1s4k"
-  - "64q1s8k"
-  - "64q1s16k"
-  - "64q1s32k"
-
-  # Batch size 128
-  - "128q1s512"
-  - "128q1s1k"
-  - "128q1s2k"
-  - "128q1s4k"
-  - "128q1s8k"
-  - "128q1s16k"
-  - "128q1s32k"
-
-  # Batch size 256
-  - "256q1s1k"
-  - "256q1s512"
-  - "256q1s2k"
-  - "256q1s4k"
-  - "256q1s8k"
-  - "256q1s16k"
-  - "256q1s32k"
-
-# Available backends: FLASH_ATTN, TRITON_ATTN, FLASHINFER
-backends:
-  - FLASH_ATTN
-  - TRITON_ATTN
-  - FLASHINFER
-
-device: "cuda:0"
-profile_memory: false
@@ -1,108 +0,0 @@
-# Standard attention prefill benchmark configuration
-# Sweeps num_q_heads and num_kv_heads to isolate effects of:
-#   1. GQA ratio (fixed num_q_heads=32, vary num_kv_heads)
-#   2. Absolute head count (fixed 4:1 ratio, vary scale)
-
-model:
-  num_layers: 32
-  num_q_heads: 32 # Base value, overridden by sweep
-  num_kv_heads: 8 # Base value, overridden by sweep
-  head_dim: 128
-  block_size: 16
-
-# Head count sweep: each entry overrides num_q_heads, num_kv_heads, and
-# head_dim where it differs from the base (128). Head counts are per-GPU
-# (i.e. after TP sharding).
-#
-# Group A — vary GQA ratio (fixed q=32, head_dim=128):
-#   32:32 (MHA), 32:8 (GQA 4:1), 32:4 (GQA 8:1), 32:1 (MQA)
-#
-# Groups B-E — real model configs at various TP degrees:
-#   Model              head_dim  Full    TP2      TP4      TP8
-#   Llama 3 8B           128    32:8   16:4      8:2      4:1
-#   Llama 3 70B          128    64:8   32:4     16:2      8:1
-#   GPT-OSS 120B          64    64:8   32:4     16:2      8:1
-#   Llama 3 405B         128   128:8   64:4     32:2     16:1
-model_parameter_sweep:
-  values:
-    # --- head_dim=128 (Llama 3 family) ---
-    - { num_q_heads: 32, num_kv_heads: 32, head_dim: 128 } # MHA 1:1
-    - { num_q_heads: 32, num_kv_heads: 1, head_dim: 128 } # MQA 32:1
-    - { num_q_heads: 4, num_kv_heads: 1, head_dim: 128 } # Llama 3 8B TP8
-    - { num_q_heads: 8, num_kv_heads: 2, head_dim: 128 } # Llama 3 8B TP4
-    - { num_q_heads: 16, num_kv_heads: 4, head_dim: 128 } # Llama 3 8B TP2
-    - { num_q_heads: 32, num_kv_heads: 8, head_dim: 128 } # Llama 3 8B TP1 / GQA 4:1
-    - { num_q_heads: 8, num_kv_heads: 1, head_dim: 128 } # Llama 3 70B TP8
-    - { num_q_heads: 16, num_kv_heads: 2, head_dim: 128 } # Llama 3 70B TP4
-    - { num_q_heads: 32, num_kv_heads: 4, head_dim: 128 } # Llama 3 70B TP2 / GQA 8:1
-    - { num_q_heads: 64, num_kv_heads: 8, head_dim: 128 } # Llama 3 70B TP1
-    - { num_q_heads: 16, num_kv_heads: 1, head_dim: 128 } # Llama 3 405B TP8
-    - { num_q_heads: 32, num_kv_heads: 2, head_dim: 128 } # Llama 3 405B TP4
-    - { num_q_heads: 64, num_kv_heads: 4, head_dim: 128 } # Llama 3 405B TP2
-    - { num_q_heads: 128, num_kv_heads: 8, head_dim: 128 } # Llama 3 405B TP1
-    # --- head_dim=64 (GPT-OSS 120B) ---
-    - { num_q_heads: 8, num_kv_heads: 1, head_dim: 64 } # GPT-OSS 120B TP8
-    - { num_q_heads: 16, num_kv_heads: 2, head_dim: 64 } # GPT-OSS 120B TP4
-    - { num_q_heads: 32, num_kv_heads: 4, head_dim: 64 } # GPT-OSS 120B TP2
-    - { num_q_heads: 64, num_kv_heads: 8, head_dim: 64 } # GPT-OSS 120B TP1
-  label_format: "{backend}_q{num_q_heads}kv{num_kv_heads}d{head_dim}"
-
-batch_specs:
-  # ---- batch_size x prefill_len grid (prefill: q_len == seq_len) ----
-  # Total tokens = batch_size * prefill_len, and prefill compute scales with
-  # prefill_len^2, so the largest cells are expensive. Trim batch sizes or
-  # lengths for quick iteration.
-
-  # Batch size 1
-  - "q512"
-  - "q1k"
-  - "q2k"
-  - "q4k"
-  - "q8k"
-  - "q16k"
-  - "q32k"
-
-  # Batch size 2
-  - "2q512"
-  - "2q1k"
-  - "2q2k"
-  - "2q4k"
-  - "2q8k"
-  - "2q16k"
-  - "2q32k"
-
-  # Batch size 4
-  - "4q512"
-  - "4q1k"
-  - "4q2k"
-  - "4q4k"
-  - "4q8k"
-  - "4q16k"
-  - "4q32k"
-
-  # Batch size 8
-  - "8q512"
-  - "8q1k"
-  - "8q2k"
-  - "8q4k"
-  - "8q8k"
-  - "8q16k"
-  - "8q32k"
-
-  # Batch size 16
-  - "16q512"
-  - "16q1k"
-  - "16q2k"
-  - "16q4k"
-  - "16q8k"
-  - "16q16k"
-  - "16q32k"
-
-# Available backends: FLASH_ATTN, TRITON_ATTN, FLASHINFER
-backends:
-  - FLASH_ATTN
-  - TRITON_ATTN
-  - FLASHINFER
-
-device: "cuda:0"
-profile_memory: false
@@ -8,8 +8,6 @@ This module provides helpers for running MLA backends without
 needing full VllmConfig integration.
 """

-import statistics
-
 import numpy as np
 import torch
 from batch_spec import parse_batch_spec
@@ -19,8 +17,6 @@ from common import (
    MockIndexer,
    MockKVBProj,
    MockLayer,
-    run_do_bench,
-    run_ncu_profile,
    setup_mla_dims,
 )

@@ -33,7 +29,6 @@ from vllm.config import (
    VllmConfig,
    set_current_vllm_config,
 )
-from vllm.v1.attention.backends.mla.prefill.registry import MLAPrefillBackendEnum

 # ============================================================================
 # VllmConfig Creation
@@ -84,8 +79,8 @@ def create_minimal_vllm_config(
        index_topk: Optional topk value for sparse MLA backends. If provided,
                    the config will include index_topk for sparse attention.
        prefill_backend: Prefill backend name (e.g., "fa3", "fa4", "flashinfer",
-                        "trtllm"). Configures the attention config to force
-                        the specified prefill backend.
+                        "cudnn", "trtllm"). Configures the attention config to
+                        force the specified prefill backend.

    Returns:
        VllmConfig for benchmarking
@@ -184,13 +179,27 @@ def create_minimal_vllm_config(

    if prefill_backend is not None:
        prefill_cfg = get_prefill_backend_config(prefill_backend)
-        vllm_config.attention_config.mla_prefill_backend = prefill_cfg[
-            "mla_prefill_backend"
-        ]
-        if prefill_cfg["flash_attn_version"] is not None:
-            vllm_config.attention_config.flash_attn_version = prefill_cfg[
-                "flash_attn_version"
+        if prefill_cfg.get("mla_prefill_backend_enum") is not None:
+            # Registry-based backends bypass the deprecated boolean flags.
+            from vllm.v1.attention.backends.mla.prefill import MLAPrefillBackendEnum
+
+            vllm_config.attention_config.mla_prefill_backend = MLAPrefillBackendEnum[
+                prefill_cfg["mla_prefill_backend_enum"]
            ]
+        else:
+            if prefill_cfg["flash_attn_version"] is not None:
+                vllm_config.attention_config.flash_attn_version = prefill_cfg[
+                    "flash_attn_version"
+                ]
+            vllm_config.attention_config.disable_flashinfer_prefill = prefill_cfg[
+                "disable_flashinfer_prefill"
+            ]
+            vllm_config.attention_config.use_cudnn_prefill = prefill_cfg[
+                "use_cudnn_prefill"
+            ]
+            vllm_config.attention_config.use_trtllm_ragged_deepseek_prefill = (
+                prefill_cfg["use_trtllm_ragged_deepseek_prefill"]
+            )

    return vllm_config

@@ -205,27 +214,34 @@ def create_minimal_vllm_config(
 _PREFILL_BACKEND_CONFIG: dict[str, dict] = {
    "fa2": {
        "flash_attn_version": 2,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": False,
    },
    "fa3": {
        "flash_attn_version": 3,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": False,
    },
    "fa4": {
        "flash_attn_version": 4,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
+        "disable_flashinfer_prefill": True,
+        "use_cudnn_prefill": False,
+        "use_trtllm_ragged_deepseek_prefill": False,
    },
    "flashinfer": {
-        "flash_attn_version": None,
-        "mla_prefill_backend": MLAPrefillBackendEnum.FLASHINFER,
+        "mla_prefill_backend_enum": "FLASHINFER",
+    },
+    "cudnn": {
+        # cuDNN prefill backend was removed; AttentionConfig raises on use.
+        "mla_prefill_backend_enum": "FLASHINFER",
    },
    "trtllm": {
-        "flash_attn_version": None,
-        "mla_prefill_backend": MLAPrefillBackendEnum.TRTLLM_RAGGED,
+        "mla_prefill_backend_enum": "TRTLLM_RAGGED",
    },
    "tokenspeed": {
-        "flash_attn_version": None,
-        "mla_prefill_backend": MLAPrefillBackendEnum.TOKENSPEED_MLA,
+        "mla_prefill_backend_enum": "TOKENSPEED_MLA",
    },
 }

@@ -824,7 +840,7 @@ def _run_single_benchmark(
            num_prefill, mla_dims, query_fmt, device, torch.bfloat16
        )

-    # Build forward function (runs a single decode/prefill pass)
+    # Build forward function
    def forward_fn():
        results = []
        if has_decode:
@@ -843,35 +859,44 @@ def _run_single_benchmark(
            )
        return results[0] if len(results) == 1 else tuple(results)

-    def benchmark_fn():
-        for _ in range(config.num_layers):
+    # Warmup
+    for _ in range(config.warmup_iters):
+        forward_fn()
+    torch.accelerator.synchronize()
+
+    # Optionally capture a CUDA graph after warmup.
+    # Graph replay eliminates CPU launch overhead so timings reflect pure
+    # kernel time.
+    if config.use_cuda_graphs:
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph):
            forward_fn()
+        benchmark_fn = graph.replay
+    else:
+        benchmark_fn = forward_fn

-    if config.ncu_profile:
-        run_ncu_profile(benchmark_fn)
-        return BenchmarkResult(
-            config=config,
-            mean_time=0.0,
-            median_time=0.0,
-            std_time=0.0,
-            min_time=0.0,
-            max_time=0.0,
-            throughput_tokens_per_sec=0.0,
-        )
+    # Benchmark
+    times = []
+    for _ in range(config.repeats):
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)

-    all_ms = run_do_bench(benchmark_fn, config.use_cuda_graphs, config.warmup_ms)
+        start.record()
+        for _ in range(config.num_layers):
+            benchmark_fn()
+        end.record()

-    # Convert ms to seconds per layer
-    times = [t / 1000.0 / config.num_layers for t in all_ms]
-    mean_time = statistics.mean(times)
+        torch.accelerator.synchronize()
+        elapsed_ms = start.elapsed_time(end)
+        times.append(elapsed_ms / 1000.0 / config.num_layers)

+    mean_time = float(np.mean(times))
    return BenchmarkResult(
        config=config,
        mean_time=mean_time,
-        median_time=statistics.median(times),
-        std_time=statistics.stdev(times) if len(times) > 1 else 0.0,
-        min_time=min(times),
-        max_time=max(times),
+        std_time=float(np.std(times)),
+        min_time=float(np.min(times)),
+        max_time=float(np.max(times)),
        throughput_tokens_per_sec=total_q / mean_time if mean_time > 0 else 0,
    )

@@ -995,6 +1020,7 @@ def _run_mla_benchmark_batched(
                        f"version {fa_version}, got "
                        f"{actual_fa_version} on {actual_class}."
                    )
+
        # Run each benchmark with the shared impl
        for config, threshold, num_splits in configs_with_params:
            # Set threshold for this benchmark (FlashAttn/FlashMLA only)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Mohammad Miadh Angkadandkhluu	ad7125a431	[Bugfix] Fix DeepSeek V4 MTP HC state handling (#42320 ) Signed-off-by: Mohammad Miadh Angkad <176301910+mmangkad@users.noreply.github.com> (cherry picked from commit `f1cc7aad3c`)	2026-05-14 21:28:34 -07:00
Yongye Zhu khluu Claude	9da56fd18b	[Bugfix] Add swiglu limits to deepgemm fp8 methods (#41986 ) Cherry-picked from https://github.com/vllm-project/vllm/pull/41986 Plumb SwiGLU clamp limit through DeepGemm FP8/W4A8 MoE quant configs and experts. Extend silu_mul_per_token_group_quant_fp8_colmajor with clamp support and forward the limit on all FP8/MXFP8/MXFP4 paths. Co-authored-by: Yongye Zhu <zyy1102000@gmail.com> Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: khluu <khluu000@gmail.com>	2026-05-14 12:38:36 -07:00
Yongye Zhuandkhluu	800604bf53	[MLA Attention Backend] Add TOKENSPEED_MLA backend for DSR1/Kimi K25 prefill + decode on Blackwell (#41778 ) Signed-off-by: Yongye Zhu <zyy1102000@gmail.com> Signed-off-by: Roger Wang <hey@rogerw.io> Co-authored-by: Roger Wang <hey@rogerw.io> (cherry picked from commit `0d2732dd91`)	2026-05-14 00:59:51 -07:00
khluu	75a7914326	pin cutlass-dsl to 4.4.2 Signed-off-by: khluu <khluu000@gmail.com>	2026-05-14 00:59:01 -07:00
ovidiusmandkhluu	3b581add43	[PD] Fix broken NIXL EP installation (#42542 ) Signed-off-by: Ovidiu Mara <ovidium@nvidia.com> (cherry picked from commit `cca32d55a2`)	2026-05-13 15:15:07 -07:00
Kevin H. Luu	342cec8812	[CI] Use uv with Python 3.12 for PyPI wheel upload (#42470 ) Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> (cherry picked from commit `f6e868fbdf`)	2026-05-13 02:12:34 -07:00
Jiangyun Zhuandkhluu	135453b715	[Bugfix] Install nvidia-cutlass-dsl[cu13] extra on CUDA 13 platforms (#42438 ) Signed-off-by: zjy0516 <riverclouds.zhu@qq.com> (cherry picked from commit `140dc2ec30`)	2026-05-13 02:03:17 -07:00
sychen52andkhluu	a707288c1e	Patch SlidingWindowSpec.real_page_size_bytes for nvfp4 kv (#42464 ) Signed-off-by: Shiyang Chen <shiychen@nvidia.com> (cherry picked from commit `a8c13d2837`)	2026-05-13 02:03:07 -07:00
Alecandkhluu	638f8fa979	[PD] Bump NIXL connector dependency to 1.x (#42364 ) Signed-off-by: Alec Flowers <aflowers@nvidia.com> (cherry picked from commit `07534b8782`)	2026-05-13 02:02:55 -07:00
Chao Leiandkhluu	cbaa80fede	[KV Transfer] Add MooncakeStoreConnector for KV cache offloading via Mooncake distributed store (#40900 ) Signed-off-by: leichao.lc <leichao.lc@antgroup.com> Signed-off-by: Yifan Qiao <yifanqiao@inferact.ai> Co-authored-by: leichao.lc <leichao.lc@antgroup.com> Co-authored-by: ivanium <yifanqiao@inferact.ai> Co-authored-by: aoshen524 <aoshen@inferact.ai> Co-authored-by: Dao007forever <daole@inferact.ai> Co-authored-by: Teng Ma <sima.mt@alibaba-inc.com> Co-authored-by: Pz1116 <zpbzpb123123@gmail.com> Co-authored-by: foraxe <1055696449@qq.com> Co-authored-by: Skywalker-EP <173423846@qq.com> Co-authored-by: fems14 <1804143737@qq.com> Co-authored-by: jianzs <zheng.shoujian@outlook.com> Co-authored-by: baxingpiaochong <771405853@qq.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> (cherry picked from commit `ebeb09d822`)	2026-05-13 02:02:44 -07:00
Kevin H. Luu	84a1066ccc	[CI] Inline build artifact annotations in release pipeline (#42357 ) Signed-off-by: khluu <khluu000@gmail.com> (cherry picked from commit `8c4fc4202a`)	2026-05-13 02:02:30 -07:00
Michael Goinandkhluu	d801ae8c26	[Build] Build bundled DeepGEMM `_C` per-Python so the wheel imports on every CPython (#41516 ) Signed-off-by: mgoin <mgoin64@gmail.com> Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> (cherry picked from commit `d077622d60`)	2026-05-12 14:57:17 -07:00
Jiahan Chang (Cyrus)andkhluu	65df49eba3	[Perf] Use 2D-grid to eliminate divmod in W8W8 group quant (#42153 ) Signed-off-by: jiahanc <173873397+jiahanc@users.noreply.github.com> Co-authored-by: Yongye Zhu <zyy1102000@gmail.com> (cherry picked from commit `dd6b3a5ef5`)	2026-05-12 14:57:06 -07:00
Kevin H. Luu	2a2ac21d3d	[CI] Move DockerHub and PyPI publish steps to end of release pipeline (#42355 ) Signed-off-by: khluu <khluu000@gmail.com> (cherry picked from commit `e1c8776e90`)	2026-05-12 14:56:46 -07:00
Jee Jee Liandkhluu	c6fc95806b	[Bugfix] Fix DSV4 swiglu_limit on marlin backend (#42287 ) Signed-off-by: Jee Jee Li <jeejeelee@inferact.ai> Co-authored-by: Yongye Zhu <zyy1102000@gmail.com> (cherry picked from commit `53181384e0`)	2026-05-12 14:56:29 -07:00