Apply pre-commit formatting and use torch.accelerator API

- Apply ruff, clang-format formatting fixes
- Replace torch.cuda.set_device/device_count/synchronize with
  torch.accelerator equivalents per project convention

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Signed-off-by: Alexander Matveev <alexm-redhat@dgx-b200-02.mgmt.accl-001.lab.rdu2.dc.redhat.com>

Signed-off-by:  <>

.buildkite/ci_config_rocm.yaml

@@ -0,0 +1,23 @@
+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"

.buildkite/hardware_tests/amd.yaml

@@ -1,22 +1,73 @@
-group: Hardware - AMD Build 
+group: Hardware - AMD Build
 steps:
-  - label: "AMD: :docker: build image"
-    key: image-build-amd
+  # 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
     depends_on: []
     device: amd_cpu
     no_plugin: true
     commands:
-    - >
-      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:${BUILDKITE_COMMIT}"
+      - bash .buildkite/scripts/ci-bake-rocm.sh ci-base-rocm-ci-with-deps
     env:
       DOCKER_BUILDKIT: "1"
+      VLLM_BAKE_FILE: "docker/docker-bake-rocm.hcl"
+      PYTORCH_ROCM_ARCH: "gfx90a;gfx942;gfx950"
+      REMOTE_VLLM: "1"
+      VLLM_BRANCH: "$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

.buildkite/hardware_tests/cpu.yaml

@@ -12,32 +12,35 @@ steps:
   - vllm/_custom_ops.py
   - tests/kernels/attention/test_cpu_attn.py
   - tests/kernels/moe/test_cpu_fused_moe.py
-  - tests/kernels/moe/test_cpu_fp8_fused_moe.py
+  - tests/kernels/moe/test_cpu_quant_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_fp8_fused_moe.py
+      pytest -x -v -s tests/kernels/moe/test_cpu_quant_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/quantization/test_cpu_fp8_scaled_mm.py
+      pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py"
 
-- label: CPU-Compatibility Tests
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies:
-  - cmake/cpu_extension.cmake
-  - setup.py
-  - vllm/platforms/cpu.py
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-compatibility-test.sh"
+# 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-Language Generation and Pooling Model Tests
   depends_on: []
@@ -54,23 +57,41 @@ 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/cpu_wna16.py
-  - vllm/model_executor/layers/quantization/gptq_marlin.py
+  - vllm/model_executor/layers/quantization/auto_gptq.py
   - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
-  - vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py
-  - vllm/model_executor/layers/quantization/kernels/mixed_precision/cpu.py
+  - vllm/model_executor/kernels/linear/mixed_precision/cpu.py
+  - vllm/model_executor/kernels/linear/scaled_mm/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 20m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
       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"
       

.buildkite/hardware_tests/intel.yaml

@@ -8,10 +8,3 @@ 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

.buildkite/image_build/image_build.yaml

@@ -13,6 +13,60 @@ 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: []
@@ -56,3 +110,21 @@ 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

.buildkite/image_build/image_build_arm64.sh

@@ -0,0 +1,37 @@
+#!/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

.buildkite/image_build/image_build_hpu.sh

@@ -11,7 +11,7 @@ REPO=$2
 BUILDKITE_COMMIT=$3
 
 # authenticate with AWS ECR
-aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY"
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
 
 # skip build if image already exists
 if [[ -z $(docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu) ]]; then

.buildkite/image_build/image_build_xpu.sh

@@ -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 "$REGISTRY"
-aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
+aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
 
 # skip build if image already exists
 if ! docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then

.buildkite/intel_jobs/basic_correctness.yaml

@@ -0,0 +1,22 @@
+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'

.buildkite/intel_jobs/expert_parallelism_intel.yaml

@@ -0,0 +1,23 @@
+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'

.buildkite/intel_jobs/lora_intel.yaml

@@ -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'
+      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]"'
 
 - label: LoRA Punica Kernels
   timeout_in_minutes: 45

.buildkite/intel_jobs/misc_intel.yaml

@@ -38,7 +38,17 @@ steps:
     REPO: "vllm-ci-test-repo"
     VLLM_TEST_DEVICE: "xpu"
   source_file_dependencies:
-    - vllm/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/model_executor/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/utils/
+    - vllm/v1/
     - tests/v1/sample
     - tests/v1/logits_processors
     - tests/v1/test_oracle.py
@@ -52,4 +62,126 @@ 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/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'

.buildkite/intel_jobs/models_multimodal_intel.yaml

@@ -0,0 +1,111 @@
+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

.buildkite/intel_jobs/test-intel.yaml

@@ -36,9 +36,14 @@ 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 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
+        '
   - label: "XPU V1 test"
     depends_on:
       - image-build-xpu
@@ -62,4 +67,23 @@ 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'
+        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'

.buildkite/lm-eval-harness/configs/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8.yaml

@@ -6,9 +6,7 @@ tasks:
     value: 0.7142
   - name: "exact_match,flexible-extract"
     value: 0.4579
-env_vars:
-  VLLM_USE_FLASHINFER_MOE_FP8: "1"
-  VLLM_FLASHINFER_MOE_BACKEND: "throughput"
+moe_backend: "flashinfer_cutlass"
 limit: 1319
 num_fewshot: 5
 max_model_len: 262144

.buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh

@@ -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]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.12"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh

@@ -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]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.12"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -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]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.12"
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh

@@ -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]>=0.4.11"
+#   pip install "lm-eval[api]>=0.4.12"
 
 usage() {
     echo``

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

@@ -68,6 +68,10 @@ 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(

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

@@ -2,7 +2,6 @@
     {
         "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

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

@@ -2,7 +2,6 @@
     {
         "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,

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

@@ -13,7 +13,6 @@
       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,

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

@@ -5,7 +5,6 @@
     ],
     "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": {

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

@@ -9,7 +9,6 @@
       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,

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

@@ -5,7 +5,6 @@
     ],
     "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,
@@ -31,30 +30,9 @@
     }
   },
   "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",
@@ -62,158 +40,10 @@
         "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",
-        "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
+        "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
       },
       "client_parameters": {
         "model": "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
@@ -225,8 +55,7 @@
     {
       "test_name": "serving_llama8B_int8_tp1_random_128_128",
       "server_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-        "tensor_parallel_size": 1
+        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
       },
       "client_parameters": {
         "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
@@ -236,26 +65,12 @@
       }
     },
     {
-      "test_name": "serving_llama8B_int8_tp2_random_128_128",
+      "test_name": "serving_llama1B_tp1_random_128_128",
       "server_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-        "tensor_parallel_size": 2
+        "model": "meta-llama/Llama-3.2-1B"
       },
       "client_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-        "dataset_name": "random",
-        "random-input-len": 128,
-        "random-output-len": 128
-      }
-    },
-    {
-      "test_name": "serving_llama8B_int8_tp4_random_128_128",
-      "server_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
-        "tensor_parallel_size": 4
-      },
-      "client_parameters": {
-        "model": "RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
+        "model": "meta-llama/Llama-3.2-1B",
         "dataset_name": "random",
         "random-input-len": 128,
         "random-output-len": 128
@@ -264,8 +79,7 @@
     {
       "test_name": "serving_llama3B_tp1_random_128_128",
       "server_parameters": {
-        "model": "meta-llama/Llama-3.2-3B-Instruct",
-        "tensor_parallel_size": 1
+        "model": "meta-llama/Llama-3.2-3B-Instruct"
       },
       "client_parameters": {
         "model": "meta-llama/Llama-3.2-3B-Instruct",
@@ -274,11 +88,22 @@
         "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",
-        "tensor_parallel_size": 1
+        "model": "ibm-granite/granite-3.2-2b-instruct"
       },
       "client_parameters": {
         "model": "ibm-granite/granite-3.2-2b-instruct",
@@ -290,8 +115,7 @@
     {
       "test_name": "serving_qwen1.7B_tp1_random_128_128",
       "server_parameters": {
-        "model": "Qwen/Qwen3-1.7B",
-        "tensor_parallel_size": 1
+        "model": "Qwen/Qwen3-1.7B"
       },
       "client_parameters": {
         "model": "Qwen/Qwen3-1.7B",
@@ -303,8 +127,7 @@
     {
       "test_name": "serving_qwen4B_tp1_random_128_128",
       "server_parameters": {
-        "model": "Qwen/Qwen3-4B",
-        "tensor_parallel_size": 1
+        "model": "Qwen/Qwen3-4B"
       },
       "client_parameters": {
         "model": "Qwen/Qwen3-4B",
@@ -316,8 +139,7 @@
     {
       "test_name": "serving_qwen8B_tp1_random_128_128",
       "server_parameters": {
-        "model": "Qwen/Qwen3-8B",
-        "tensor_parallel_size": 1
+        "model": "Qwen/Qwen3-8B"
       },
       "client_parameters": {
         "model": "Qwen/Qwen3-8B",
@@ -326,11 +148,34 @@
         "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",
-        "tensor_parallel_size": 1
+        "model": "zai-org/glm-4-9b-hf"
       },
       "client_parameters": {
         "model": "zai-org/glm-4-9b-hf",
@@ -342,8 +187,7 @@
     {
       "test_name": "serving_gemma7B_tp1_random_128_128",
       "server_parameters": {
-        "model": "google/gemma-7b",
-        "tensor_parallel_size": 1
+        "model": "google/gemma-7b"
       },
       "client_parameters": {
         "model": "google/gemma-7b",
@@ -351,6 +195,94 @@
         "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
+      }
     }
   ]
 }

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

@@ -5,7 +5,6 @@
     ],
     "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,

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

@@ -1,77 +1,112 @@
-[
-    {
-        "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
-        }
+{
+  "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_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 
-        }
+    "client_parameters": {
+      "model": "meta-llama/Llama-3.1-8B-Instruct",
+      "backend": "vllm",
+      "ignore-eos": "",
+      "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
+      }
+    }
+  ]
+}

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

@@ -2,7 +2,6 @@
     {
         "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

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

@@ -2,7 +2,6 @@
     {
         "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,

.buildkite/release-pipeline.yaml

@@ -1,12 +1,28 @@
 # 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:
-  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"
+  # 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_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"
@@ -24,7 +40,7 @@ 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=\"${CUDA_ARCH_AARCH64_CU129}\" --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --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 ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
           - "bash .buildkite/scripts/upload-nightly-wheels.sh"
@@ -66,7 +82,7 @@ steps:
         agents:
           queue: cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --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}\" --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 ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
           - "bash .buildkite/scripts/upload-nightly-wheels.sh"
@@ -136,6 +152,8 @@ steps:
               --build-arg CUDA_VERSION=13.0.2 \
               --build-arg torch_cuda_arch_list="${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 \
@@ -162,6 +180,8 @@ 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="${MOONCAKE_WHEEL_X86_64}" \
               --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
               --target vllm-openai \
               --progress plain \
@@ -185,6 +205,8 @@ steps:
               --build-arg CUDA_VERSION=12.9.1 \
               --build-arg torch_cuda_arch_list="${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 .
@@ -210,6 +232,8 @@ 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="${MOONCAKE_WHEEL_X86_64}" \
               --target vllm-openai \
               --progress plain \
               -f docker/Dockerfile .
@@ -234,6 +258,8 @@ steps:
               --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
               --build-arg torch_cuda_arch_list="${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 \
@@ -261,6 +287,8 @@ 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="${MOONCAKE_WHEEL_X86_64}" \
               --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
               --target vllm-openai \
               --progress plain \
@@ -286,6 +314,8 @@ steps:
               --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
               --build-arg torch_cuda_arch_list="${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 .
@@ -312,6 +342,8 @@ 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 .
@@ -718,7 +750,7 @@ steps:
       - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
     env:
       S3_BUCKET: "vllm-wheels"
-      VARIANT: "rocm722"
+      VARIANT: "rocm723"
 
   # ROCm Job 6: Build ROCm Release Docker Image
   - label: ":docker: Build release image - x86_64 - ROCm"
@@ -827,7 +859,6 @@ 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:

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

@@ -13,5 +13,8 @@ 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"

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

@@ -1,55 +1,178 @@
 #!/bin/bash
-# Usage: ./ci-fetch-log.sh <buildkite_job_url> [output_file]
-#        ./ci-fetch-log.sh <build_number> <job_uuid> [output_file]
+# Fetch vLLM Buildkite CI logs (public; no login required).
 #
-# 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.
+# 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]
 #
-# 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>.
+# --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.
 
 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() {
-    echo "Usage: $0 <buildkite_job_url> [output_file]"
-    echo "       $0 <build_number> <job_uuid> [output_file]"
+    sed -n '2,15p' "$0" | sed 's/^# \{0,1\}//'
     exit 1
 }
 
-if [ $# -lt 1 ]; then usage; fi
+die() {
+    echo "$1" >&2
+    exit 1
+}
 
-if [[ "$1" == https://* ]]; then
+BUILD="" JOB="" SID="" OUT=""
+SCOPE="failed"
+
+while :; do
+    case "${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://*)
     BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
-    JOB=$(echo "$1" | grep -oE '[0-9a-f]{8}-[0-9a-f-]+' | head -n 1)
-    OUT="${2:-ci-${BUILD}-${JOB:0:8}.log}"
-else
-    if [ $# -lt 2 ]; then usage; fi
+    JOB=$(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
     BUILD="$1"
     JOB="$2"
-    OUT="${3:-ci-${BUILD}-${JOB:0:8}.log}"
-fi
-
-if [ -z "$BUILD" ] || [ -z "$JOB" ]; then
-    echo "Could not parse build number or job UUID from: $1" >&2
+    OUT="${3:-}"
+    ;;
+*)
     usage
-fi
+    ;;
+esac
 
 COOKIES=$(mktemp)
-trap 'rm -f "$COOKIES"' EXIT
+JOBS_TSV=$(mktemp)
+trap 'rm -f "$COOKIES" "$JOBS_TSV"' EXIT
 
-# Buildkite issues a session cookie on first hit; subsequent /download needs it.
-curl -fsSL -c "$COOKIES" -A "vllm-ci-fetch-log" \
+# Buildkite issues a session cookie on first hit; later requests need it.
+curl -fsSL -c "$COOKIES" -A "$UA" \
     "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}" -o /dev/null
 
-curl -fsSL -b "$COOKIES" -A "vllm-ci-fetch-log" \
-    "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/${JOB}/download" \
-    -o "$OUT"
+# 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
 
-bash "$(dirname "$0")/ci-clean-log.sh" "$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}"
 
-echo "$OUT"
+if [ -n "$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

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

@@ -28,32 +28,18 @@
 ###############################################################################
 set -o pipefail
 
-# Export Python path
-export PYTHONPATH=".."
+# 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:-..}"
 
 ###############################################################################
 # Helper Functions
 ###############################################################################
 
-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
+report_docker_usage() {
+  echo "--- Docker usage"
+  docker system df || true
 }
 
 cleanup_network() {
@@ -68,6 +54,108 @@ 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="$1"
   # Primary signal: NUM_NODES environment variable set by the pipeline
@@ -114,8 +202,7 @@ handle_pytest_exit() {
 # unquoted since they have no spaces and work fine.
 #
 # Already-quoted expressions (containing literal single quotes) are passed
-# through untouched to avoid double-quoting values injected by
-# apply_rocm_test_overrides.
+# through untouched to avoid double-quoting well-formed shell fragments.
 #
 # NOTE: This ONLY fixes -m/-k flags. It cannot recover arbitrary inner
 # double-quotes stripped by the calling shell (see header comment).
@@ -247,102 +334,6 @@ re_quote_pytest_markers() {
   echo "${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
 ###############################################################################
@@ -351,20 +342,36 @@ apply_rocm_test_overrides() {
 echo "--- ROCm info"
 rocminfo
 
-# --- Docker housekeeping ---
-cleanup_docker
+# --- Docker status ---
+report_docker_usage
 
 # --- Pull test image ---
 echo "--- Pulling container"
-image_name="rocm/vllm-ci:${BUILDKITE_COMMIT}"
+image_name="${VLLM_CI_FALLBACK_IMAGE:-rocm/vllm-ci:${BUILDKITE_COMMIT:-local}}"
+artifact_work_dir=""
 container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
-docker pull "${image_name}"
 
 remove_docker_container() {
-  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
+  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
 }
 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"
 
@@ -372,6 +379,14 @@ 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
@@ -409,10 +424,16 @@ else
   echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
 fi
 
-commands=$(apply_rocm_test_overrides "$commands")
 echo "Final commands: $commands"
 
-MYPYTHONPATH=".."
+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\""
 
 # Verify GPU access
 render_gid=$(getent group render | cut -d: -f3)
@@ -489,6 +510,8 @@ else
     --group-add "$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 \
@@ -496,10 +519,15 @@ 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}" \
     "${image_name}" \
-    /bin/bash -c "${commands}"
+    /bin/bash -c "${CONTAINER_PREFLIGHT} && ${commands}"
 
   exit_code=$?
   handle_pytest_exit "$exit_code"

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

@@ -37,7 +37,8 @@ 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/moe/test_moe.py -k test_cpu_fused_moe_basic
+    pytest -x -v -s tests/kernels/mamba/cpu/test_cpu_gdn_ops.py"
 
   # skip tests requiring model downloads if HF_TOKEN is not set
   # due to rate-limits

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

@@ -324,23 +324,6 @@ 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
 }
@@ -352,17 +335,36 @@ if [[ -z "${ZE_AFFINITY_MASK:-}" ]]; then
   echo "Warning: ZE_AFFINITY_MASK is not set. Proceeding without device affinity." >&2
 fi
 
-docker run \
+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 \
     --device /dev/dri:/dev/dri \
     --net=host \
     --ipc=host \
     --privileged \
     -v /dev/dri/by-path:/dev/dri/by-path \
-    -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
-    --entrypoint="" \
-    -e "HF_TOKEN=${HF_TOKEN:-}" \
-    -e "ZE_AFFINITY_MASK=${ZE_AFFINITY_MASK:-}" \
-    -e "CMDS=${commands}" \
+    -v "${HOME}/.cache/huggingface:/root/.cache/huggingface" \
+    --entrypoint='' \
+    -e HF_TOKEN \
+    -e ZE_AFFINITY_MASK \
+    -e BUILDKITE_PARALLEL_JOB \
+    -e BUILDKITE_PARALLEL_JOB_COUNT \
+    -e CMDS \
     --name "${container_name}" \
-    "${image_name}" \
-    bash -c 'set -e; echo "ZE_AFFINITY_MASK is ${ZE_AFFINITY_MASK:-}"; eval "$CMDS"'
+    "${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}"

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

@@ -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]>=0.4.11" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.12" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 

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

@@ -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]>=0.4.11" \
+    && python3 -m pip install --progress-bar off "lm-eval[api]>=0.4.12" \
     && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 

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

@@ -1,53 +0,0 @@
-#!/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
-'

.buildkite/scripts/install-kv-connectors.sh

@@ -0,0 +1,39 @@
+#!/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

.buildkite/scripts/run-rust-frontend-cargo-ci.sh

@@ -0,0 +1,187 @@
+#!/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

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh

@@ -49,6 +49,7 @@ 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" \

.buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh

@@ -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}' \
+    --eplb-config '{"window_size":10, "step_interval":100, "num_redundant_experts":0, "log_balancedness":true, "use_async":false}' \
     --tensor-parallel-size "${TENSOR_PARALLEL_SIZE}" \
     --data-parallel-size "${DATA_PARALLEL_SIZE}" \
     --enable-expert-parallel \

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

@@ -70,7 +70,7 @@ echo "============================================"
 # ---- Install bfcl-eval if missing ----
 if ! python3 -c "import bfcl_eval" 2>/dev/null; then
     echo "Installing bfcl-eval..."
-    pip install "bfcl-eval>=2025.10.20.1,<2026"
+    uv 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
-    --no-enable-prefix-caching
+    --enable-prefix-caching
 )
 
 # Append reasoning parser if specified

.buildkite/test-amd.yaml

@@ -88,16 +88,16 @@
 #     - Do NOT remove `VLLM_WORKER_MULTIPROC_METHOD=spawn` setting as ROCm requires this for certain models to function.            #
 #   * [Transformers Nightly Models]: Whisper needs `VLLM_WORKER_MULTIPROC_METHOD=spawn` to avoid deadlock.                          #
 #   * [Plugin Tests (2 GPUs)]:                                                                                                      #
-#     - {`pytest -v -s entrypoints/openai/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                                       #
+#     - {`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                                 #
 #   * [LoRA TP (Distributed)]:                                                                                                      #
 #     - There is some Tensor Parallelism related processing logic in LoRA that requires multi-GPU testing for validation.           #
 #     - {`pytest -v -s -x lora/test_gptoss_tp.py`}: Disabled for now because MXFP4 backend on non-cuda platform doesn't support     #
 #                                                   LoRA yet.                                                                       #
 #   * [Distributed Tests (NxGPUs)(HW-TAG)]: Don't test llama model here, it seems hf implementation is buggy. See:                  #
 #                                           https://github.com/vllm-project/vllm/pull/5689                                          #
-#   * [Distributed Tests (NxGPUs)(HW-TAG)]: Some old E2E tests were removed in https://github.com/vllm-project/vllm/pull/33293     #
+#   * [Distributed Tests (NxGPUs)(HW-TAG)]: Some old E2E tests were removed in https://github.com/vllm-project/vllm/pull/33293      #
 #                                           in favor of new tests in fusions_e2e. We avoid replicating the new jobs in              #
 #                                           this file as it's deprecated.                                                           #
 #                                                                                                                                   #
@@ -139,19 +139,6 @@ steps:
   - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
   - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
 
-#--------------------------------------------------------  mi250 · benchmarks  ---------------------------------------------------------#
-
-- label: Benchmarks # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  working_dir: "/vllm-workspace/.buildkite"
-  source_file_dependencies:
-  - benchmarks/
-  - vllm/platforms/rocm.py
-  commands:
-  - bash scripts/run-benchmarks.sh
-
 #----------------------------------------------------------  mi250 · compile  ----------------------------------------------------------#
 
 - label: PyTorch Compilation Unit Tests # TBD
@@ -328,24 +315,6 @@ steps:
   - pytest -v -s distributed/test_pp_cudagraph.py
   - pytest -v -s distributed/test_pipeline_parallel.py
 
-#----------------------------------------------------------  mi250 · engine  -----------------------------------------------------------#
-
-- label: Engine # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/engine
-  - tests/test_sequence
-  - tests/test_config
-  - tests/test_logger
-  - tests/test_vllm_port
-  commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
-
 #-----------------------------------------------------------  mi250 · evals  -----------------------------------------------------------#
 
 - label: Multi-Modal Accuracy Eval (Small Models) # TBD
@@ -429,7 +398,7 @@ steps:
   - tests/kernels/helion/
   - vllm/platforms/rocm.py
   commands:
-  - pip install helion==1.0.0
+  - pip install helion==1.1.0
   - pytest -v -s kernels/helion/
 
 - label: Kernels Mamba Test # TBD
@@ -462,30 +431,7 @@ steps:
   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
 
-#------------------------------------------------------  mi250 · model_executor  -------------------------------------------------------#
-
-- label: Model Executor # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  optional: true
-  torch_nightly: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/engine/arg_utils.py
-  - vllm/config/model.py
-  - vllm/model_executor
-  - tests/model_executor
-  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  commands:
-  - apt-get update && apt-get install -y curl libsodium23
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s model_executor -m '(not slow_test)'
-  - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py
-
-#----------------------------------------------------------  mi250 · models  -----------------------------------------------------------#
+#------------------------------------------------------  mi250 · models / basic  -------------------------------------------------------#
 
 - label: Basic Models Test (Other CPU) # TBD
   timeout_in_minutes: 180
@@ -546,6 +492,8 @@ steps:
   commands:
   - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
 
+#-----------------------------------------------------  mi250 · models / language  -----------------------------------------------------#
+
 - label: Language Models Test (MTEB) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
@@ -590,6 +538,8 @@ steps:
   - pip freeze | grep -E 'torch'
   - pytest -v -s models/language -m 'core_model and slow_test' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
+#----------------------------------------------------  mi250 · models / multimodal  ----------------------------------------------------#
+
 - label: Multi-Modal Models (Extended Generation 2) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
@@ -667,9 +617,9 @@ steps:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
   - pytest -v -s distributed/test_distributed_oot.py
-  - pytest -v -s entrypoints/openai/chat_completion/test_oot_registration.py
-  - pytest -v -s models/test_oot_registration.py
-  - pytest -v -s plugins/lora_resolvers
+  - 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
 
 #------------------------------------------------------------  mi250 · v1  -------------------------------------------------------------#
 
@@ -798,32 +748,6 @@ steps:
   commands:
   - pytest -v -s v1/attention
 
-- label: V1 Core + KV + Metrics # TBD
-  timeout_in_minutes: 60
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/v1/core
-  - tests/v1/executor
-  - tests/v1/kv_offload
-  - tests/v1/worker
-  - tests/v1/kv_connector/unit
-  - tests/v1/metrics
-  - tests/entrypoints/openai/correctness/test_lmeval.py
-  commands:
-  - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - pytest -v -s -m 'not cpu_test' v1/core
-  - pytest -v -s v1/executor
-  - pytest -v -s v1/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
-  - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
-  - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
-
 - label: V1 Sample + Logits # TBD
   timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
@@ -880,7 +804,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
 - label: V1 e2e (2 GPUs) # TBD
   timeout_in_minutes: 180
@@ -906,7 +830,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 #-------------------------------------------------------------  mi250 · misc  ------------------------------------------------------------#
 
@@ -966,10 +890,10 @@ steps:
   - vllm/
   - tests/basic_correctness/test_basic_correctness
   - tests/basic_correctness/test_cpu_offload
-  - tests/basic_correctness/test_cumem.py
+  - tests/basic_correctness/test_mem.py
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s basic_correctness/test_cumem.py
+  - pytest -v -s basic_correctness/test_mem.py
   - pytest -v -s basic_correctness/test_basic_correctness.py
   - pytest -v -s basic_correctness/test_cpu_offload.py
 
@@ -1002,18 +926,6 @@ steps:
 
 #--------------------------------------------------------  mi300 · benchmarks  ---------------------------------------------------------#
 
-- label: Benchmarks # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/.buildkite"
-  source_file_dependencies:
-  - benchmarks/
-  - vllm/platforms/rocm.py
-  commands:
-  - bash scripts/run-benchmarks.sh
-
 - label: Benchmarks CLI Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -1275,83 +1187,20 @@ steps:
 
 #--------------------------------------------------------  mi300 · entrypoints  --------------------------------------------------------#
 
-- label: Entrypoints Integration (API Server 2) # TBD
+- label: Entrypoints Unit Tests # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   fast_check: true
-  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/rpc
-  - tests/entrypoints/serve/instrumentator
-  - tests/tool_use
+  - vllm/entrypoints
+  - tests/entrypoints/unit_tests
+  - tests/entrypoints/weight_transfer
+  - vllm/platforms/rocm.py
   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
-
-- label: Entrypoints Integration (API Server openai - Part 1) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  fast_check: true
-  torch_nightly: true
-  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/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) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  fast_check: true
-  torch_nightly: true
-  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/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
-  - pytest -v -s entrypoints/test_chat_utils.py
-
-- label: Entrypoints Integration (API Server openai - Part 3) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  fast_check: true
-  torch_nightly: true
-  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/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
-
-- label: Entrypoints Integration (Speech to Text) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  agent_pool: mi300_1
-  fast_check: true
-  torch_nightly: true
-  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
+  - pytest -v -s entrypoints/unit_tests
+  - pytest -v -s entrypoints/weight_transfer
 
 - label: Entrypoints Integration (LLM) # TBD
   timeout_in_minutes: 180
@@ -1364,26 +1213,81 @@ 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
-  - pytest -v -s entrypoints/llm/test_generate.py
-  - pytest -v -s entrypoints/offline_mode
+  - 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/test_generate.py # it needs a clean process
+  - pytest -v -s entrypoints/llm/offline_mode # Needs to avoid interference with other tests
 
-- label: Entrypoints Integration (Pooling) # TBD
+- label: Entrypoints Integration (API Server) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   fast_check: true
   torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/entrypoints/pooling
+  - tests/entrypoints/serve
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/pooling
+  - pytest -v -s entrypoints/serve --ignore=entrypoints/serve/dev/rpc
+  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc
+
+- label: Entrypoints Integration (API Server OpenAI - Part 1) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
+  optional: true
+  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/completion --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/correctness
+
+- label: Entrypoints Integration (API Server OpenAI - Part 2) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
+  optional: true
+  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/chat_completion
+  - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
+
+- label: Entrypoints Integration (API Server Generate) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  fast_check: true
+  torch_nightly: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/tool_use
+  - tests/entrypoints/tool_parsers
+  - tests/entrypoints/anthropic
+  - tests/entrypoints/generate
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s tool_use
+  - pytest -v -s entrypoints/tool_parsers
+  - pytest -v -s entrypoints/generate
+  - pytest -v -s entrypoints/anthropic
 
 - label: Entrypoints Integration (Responses API) # TBD
   timeout_in_minutes: 180
@@ -1399,29 +1303,57 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/openai/responses
 
-- label: Entrypoints Unit Tests # TBD
+- label: Entrypoints Integration (Speech to Text) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   fast_check: true
+  torch_nightly: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/entrypoints
-  - tests/entrypoints/
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/entrypoints/speech_to_text
   commands:
-  - 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
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/speech_to_text
+
+- label: Entrypoints Integration (Multimodal)
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
+  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) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  fast_check: true
+  torch_nightly: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/entrypoints/pooling
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s entrypoints/pooling
 
 - label: OpenAI API correctness # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/
-  - vllm/model_executor/models/whisper.py
   - vllm/model_executor/layers/
   - vllm/v1/attention/backends/
   - vllm/v1/attention/selector.py
@@ -1475,6 +1407,7 @@ steps:
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
   - csrc/
@@ -1527,7 +1460,7 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx-fp8-and-mixed.txt
 
-- label: DeepSeek V2-Lite Accuracy (4xH100-4xMI300) # TBD
+- label: DeepSeek V2-Lite Sync EPLB Accuracy (4xH100-4xMI300) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_4
@@ -1569,7 +1502,7 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
-- label: Qwen3-30B-A3B-FP8-block Accuracy (4xH100-4xMI300) # TBD
+- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (4xH100-4xMI300) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_4
@@ -1728,6 +1661,7 @@ steps:
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   parallelism: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -1780,12 +1714,36 @@ steps:
   - pytest -v -s -x lora/test_gptoss_tp.py
   - pytest -v -s -x lora/test_qwen35_densemodel_lora.py
 
-#----------------------------------------------------------  mi300 · models  -----------------------------------------------------------#
+#------------------------------------------------------  mi300 · model_executor  -------------------------------------------------------#
+
+- label: Model Executor # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  torch_nightly: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/engine/arg_utils.py
+  - vllm/config/model.py
+  - vllm/model_executor
+  - tests/model_executor
+  - tests/entrypoints/openai/completion/test_tensorizer_entrypoint.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  commands:
+  - apt-get update && apt-get install -y curl libsodium23
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s model_executor -m '(not slow_test)'
+  - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py
+
+#-----------------------------------------------------  mi300 · models / language  -----------------------------------------------------#
 
 - label: Language Models Test (Extended Pooling)  # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -1807,6 +1765,8 @@ steps:
   - pip freeze | grep -E 'torch'
   - pytest -v -s models/language -m 'core_model and (not slow_test)'
 
+#----------------------------------------------------  mi300 · models / multimodal  ----------------------------------------------------#
+
 - label: Multi-Modal Models (Extended Generation 1) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -1912,10 +1872,11 @@ steps:
   - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
   - pytest -v -s models/multimodal/processing/test_tensor_schema.py
 
-- label: Multi-Modal Processor (CPU) # TBD
+- label: Multi-Modal Processor (CPU) %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  parallelism: 4
   no_gpu: true
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -1925,7 +1886,9 @@ steps:
   - tests/models/registry.py
   commands:
   - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
-  - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
+  - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+
+#-----------------------------------------------------  mi300 · models / quantized  -----------------------------------------------------#
 
 - label: Quantized Models Test # TBD
   timeout_in_minutes: 180
@@ -1941,7 +1904,31 @@ steps:
   commands:
   - pytest -v -s models/quantization
 
-- label: Transformers Nightly Models # TBD
+#--------------------------------------------------  mi300 · models / transformers  ---------------------------------------------------#
+
+- label: Transformers Nightly Models (Shardable) %N # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  parallelism: 4
+  optional: true
+  working_dir: "/vllm-workspace/"
+  source_file_dependencies:
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/multimodal/
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - tests/models/
+  commands:
+  - pip install --upgrade git+https://github.com/huggingface/transformers
+  - pytest -v -s tests/models/test_initialization.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+  - pytest -v -s tests/models/multimodal/processing/ --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+
+- label: Transformers Nightly Models (Single) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
@@ -1960,9 +1947,7 @@ steps:
   - examples/
   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
@@ -2023,7 +2008,7 @@ steps:
   - vllm/model_executor/layers
   - vllm/sampling_metadata.py
   - vllm/v1/sample/
-  - vllm/beam_search.py
+  - vllm/entrypoints/generate/beam_search/
   - tests/samplers
   - tests/conftest.py
   - vllm/_aiter_ops.py
@@ -2197,10 +2182,72 @@ steps:
   commands:
   - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
+- label: Speculators Correctness # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/config/speculative.py
+  - vllm/engine/arg_utils.py
+  - vllm/transformers_utils/config.py
+  - vllm/transformers_utils/configs/speculators/
+  - vllm/v1/spec_decode/
+  - vllm/v1/worker/gpu/spec_decode/
+  - vllm/v1/worker/gpu_model_runner.py
+  - vllm/v1/sample/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/model_executor/model_loader/
+  - vllm/model_executor/layers/
+  - vllm/model_executor/models/llama_eagle3.py
+  - vllm/model_executor/models/qwen3.py
+  - vllm/model_executor/models/qwen3_dflash.py
+  - vllm/model_executor/models/registry.py
+  - vllm/_aiter_ops.py
+  - tests/evals/gsm8k/
+  - tests/v1/spec_decode/test_speculators_correctness.py
+  - vllm/platforms/rocm.py
+  commands:
+  - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
+  - pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test
+
+- label: Extract Hidden States Integration # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  agent_pool: mi300_1
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/config/speculative.py
+  - vllm/distributed/kv_transfer/kv_connector/
+  - vllm/model_executor/layers/attention/
+  - vllm/model_executor/layers/mamba/
+  - vllm/model_executor/model_loader/
+  - vllm/model_executor/models/extract_hidden_states.py
+  - vllm/model_executor/models/llama.py
+  - vllm/model_executor/models/qwen3_5.py
+  - vllm/model_executor/models/qwen3_next.py
+  - vllm/model_executor/models/registry.py
+  - vllm/transformers_utils/configs/extract_hidden_states.py
+  - vllm/transformers_utils/configs/qwen3_5.py
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/v1/kv_cache_interface.py
+  - vllm/v1/spec_decode/extract_hidden_states.py
+  - vllm/v1/worker/gpu_model_runner.py
+  - vllm/_aiter_ops.py
+  - tests/v1/kv_connector/extract_hidden_states_integration/
+  - vllm/platforms/rocm.py
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -v -s v1/kv_connector/extract_hidden_states_integration
+
 - label: V1 attention (H100-MI300) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/config/attention.py
@@ -2359,7 +2406,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - CROSS_LAYERS_BLOCKS=True ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - CROSS_LAYERS_BLOCKS=True ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: Distributed DP Tests (4 GPUs) # TBD
   timeout_in_minutes: 180
@@ -2395,7 +2442,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
   timeout_in_minutes: 180
@@ -2409,9 +2456,9 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - DP_EP=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - DP_EP=1 ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
-- label: Hyrbid SSM NixlConnector PD accuracy tests (4 GPUs) # TBD
+- label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_4
@@ -2423,7 +2470,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - HYBRID_SSM=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - HYBRID_SSM=1 ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: V1 e2e (4 GPUs) # TBD
   timeout_in_minutes: 180
@@ -2559,6 +2606,7 @@ steps:
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
   agent_pool: mi325_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -2568,7 +2616,7 @@ steps:
   - tests/test_logger
   - tests/test_vllm_port
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
 
 #-----------------------------------------------------------  mi325 · evals  -----------------------------------------------------------#
 
@@ -2613,7 +2661,7 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm.txt --tp-size=8
 
-#----------------------------------------------------------  mi325 · models  -----------------------------------------------------------#
+#-----------------------------------------------------  mi325 · models / language  -----------------------------------------------------#
 
 - label: Language Models Test (Extended Generation) # TBD
   timeout_in_minutes: 180
@@ -2644,10 +2692,13 @@ steps:
   - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
   - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
+#----------------------------------------------------  mi325 · models / multimodal  ----------------------------------------------------#
+
 - label: Multi-Modal Models (Extended Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
   agent_pool: mi325_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -2715,23 +2766,39 @@ steps:
   optional: true
   working_dir: "/vllm-workspace/"
   source_file_dependencies:
+  - csrc/custom_quickreduce.cu
+  - csrc/ops.h
+  - csrc/torch_bindings.cpp
   - vllm/distributed/
-  - vllm/v1/distributed/
+  - vllm/model_executor/layers/
+  - vllm/entrypoints/llm.py
+  - vllm/config/parallel.py
   - vllm/model_executor/layers/fused_moe/
+  - vllm/v1/engine/
+  - vllm/v1/executor/
+  - vllm/v1/worker/
+  - vllm/v1/distributed/
   - vllm/v1/attention/backends/
   - vllm/v1/attention/selector.py
-  - tests/distributed/test_context_parallel.py
-  - tests/v1/distributed/test_dbo.py
-  - examples/features/data_parallel/data_parallel_offline.py
   - vllm/_aiter_ops.py
+  - vllm/_custom_ops.py
   - vllm/platforms/rocm.py
+  - vllm/envs.py
+  - examples/offline_inference/data_parallel.py
+  - tests/distributed/test_context_parallel.py
+  - tests/distributed/test_rocm_quick_reduce.py
+  - tests/distributed/test_quick_all_reduce.py
+  - tests/v1/distributed/test_dbo.py
+  - tests/utils.py
   commands:
   - pytest -v -s tests/distributed/test_context_parallel.py
   - pytest -v -s tests/v1/distributed/test_dbo.py
+  - pytest -v -s tests/distributed/test_rocm_quick_reduce.py
+  - pytest -v -s tests/distributed/test_quick_all_reduce.py
 
 #--------------------------------------------------------  mi355 · entrypoints  --------------------------------------------------------#
 
-- label: Entrypoints Integration (API Server 2) # TBD
+- label: Entrypoints Integration (API Server) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
@@ -2741,16 +2808,13 @@ steps:
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/entrypoints/rpc
-  - tests/entrypoints/serve/instrumentator
-  - tests/tool_use
+  - tests/entrypoints/serve
   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/serve --ignore=entrypoints/serve/dev/rpc
+  - PYTHONPATH=/vllm-workspace pytest -v -s entrypoints/serve/dev/rpc
 
-- label: Entrypoints Integration (API Server openai - Part 1) # TBD
+- label: Entrypoints Integration (API Server OpenAI - Part 1) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
@@ -2764,9 +2828,9 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - 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
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/correctness
 
-- label: Entrypoints Integration (API Server openai - Part 2) # TBD
+- label: Entrypoints Integration (API Server OpenAI - Part 2) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
@@ -2780,23 +2844,29 @@ steps:
   - 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
-  - pytest -v -s entrypoints/test_chat_utils.py
 
-- label: Entrypoints Integration (API Server openai - Part 3) # TBD
+- label: Entrypoints Integration (API Server Generate) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   fast_check: true
   torch_nightly: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
+  - tests/tool_use
+  - tests/entrypoints/tool_parsers
+  - tests/entrypoints/anthropic
+  - tests/entrypoints/generate
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
+  - pytest -v -s tool_use
+  - pytest -v -s entrypoints/tool_parsers
+  - pytest -v -s entrypoints/generate
+  - pytest -v -s entrypoints/anthropic
 
 - label: Entrypoints Integration (Speech to Text) # TBD
   timeout_in_minutes: 180
@@ -2812,6 +2882,20 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/speech_to_text
 
+- label: Entrypoints Integration (Multimodal)
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi355]
+  agent_pool: mi355_1
+  fast_check: true
+  torch_nightly: true
+  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) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
@@ -2862,7 +2946,6 @@ steps:
   - vllm/model_executor/models/qwen3_5_mtp.py
   - vllm/transformers_utils/configs/qwen3_5.py
   - vllm/transformers_utils/configs/qwen3_5_moe.py
-  - vllm/model_executor/models/qwen.py
   - vllm/model_executor/models/qwen2.py
   - vllm/model_executor/models/qwen3.py
   - vllm/model_executor/models/qwen3_next.py
@@ -2891,7 +2974,7 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-mi3xx-fp8-and-mixed.txt
 
-- label: Qwen3-30B-A3B-FP8-block Accuracy (B200-MI355) # TBD
+- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (B200-MI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_2
@@ -2989,7 +3072,7 @@ steps:
   - vllm/_aiter_ops.py
   commands:
   - rocm-smi
-  - python3 examples/basic/offline_inference/chat.py
+  - python3 examples/basic/offline_inference/chat.py --attention-backend TRITON_ATTN
   - pytest -v -s tests/kernels/attention/test_attention_selector.py
 
 - label: Kernels Attention Test %N # TBD
@@ -3063,7 +3146,7 @@ steps:
   commands:
     - pytest -v -s kernels/moe/test_deepep_moe.py
 
-#----------------------------------------------------------  mi355 · models  -----------------------------------------------------------#
+#-----------------------------------------------------  mi355 · models / language  -----------------------------------------------------#
 
 - label: Language Models Test (Extended Generation) # TBD
   timeout_in_minutes: 180
@@ -3100,7 +3183,6 @@ steps:
   - vllm/model_executor/models/qwen3_5_mtp.py
   - vllm/transformers_utils/configs/qwen3_5.py
   - vllm/transformers_utils/configs/qwen3_5_moe.py
-  - vllm/model_executor/models/qwen.py
   - vllm/model_executor/models/qwen2.py
   - vllm/model_executor/models/qwen3.py
   - vllm/model_executor/models/qwen3_next.py
@@ -3131,6 +3213,8 @@ steps:
   - pip freeze | grep -E 'torch'
   - pytest -v -s models/language -m 'core_model and (not slow_test)'
 
+#----------------------------------------------------  mi355 · models / multimodal  ----------------------------------------------------#
+
 - label: Multi-Modal Models (Extended Generation 1) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
@@ -3202,6 +3286,8 @@ steps:
   - pytest -v -s models/multimodal/generation/test_memory_leak.py -m core_model
   - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
 
+#-----------------------------------------------------  mi355 · models / quantized  -----------------------------------------------------#
+
 - label: Quantized Models Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
@@ -3349,7 +3435,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
 - label: Distributed NixlConnector PD accuracy (4 GPUs) # TBD
   timeout_in_minutes: 180
@@ -3364,7 +3450,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs) # TBD
   timeout_in_minutes: 180
@@ -3379,7 +3465,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - DP_EP=1 ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
+  - DP_EP=1 ATTENTION_BACKEND=TRITON_ATTN bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 #------------------------------------------------------  mi355 · weight_loading  -------------------------------------------------------#
 

.buildkite/test_areas/attention.yaml

@@ -2,8 +2,8 @@ group: Attention
 depends_on: 
   - image-build
 steps:
-- label: V1 attention (H100)
-  key: v1-attention-h100
+- label: V1 attention (H100-MI300)
+  key: v1-attention-h100-mi300
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -13,6 +13,20 @@ 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

.buildkite/test_areas/basic_correctness.yaml

@@ -10,9 +10,9 @@ steps:
   - vllm/
   - tests/basic_correctness/test_basic_correctness
   - tests/basic_correctness/test_cpu_offload
-  - tests/basic_correctness/test_cumem.py
+  - tests/basic_correctness/test_mem.py
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s basic_correctness/test_cumem.py
+  - pytest -v -s basic_correctness/test_mem.py
   - pytest -v -s basic_correctness/test_basic_correctness.py
   - pytest -v -s basic_correctness/test_cpu_offload.py

.buildkite/test_areas/cuda.yaml

@@ -27,4 +27,5 @@ 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_cudagraph_mode.py
+    - pytest -v -s v1/cudagraph/test_breakable_cudagraph.py

.buildkite/test_areas/disaggregated.yaml

@@ -11,7 +11,7 @@ steps:
     - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
     - tests/v1/kv_connector/nixl_integration/
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - 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:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - 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:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - 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)
@@ -46,21 +46,35 @@ steps:
     - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
     - tests/v1/kv_connector/nixl_integration/
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
   key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 20
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
     - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
     - tests/v1/kv_connector/nixl_integration/
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - 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
@@ -73,7 +87,7 @@ steps:
     - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
     - tests/v1/kv_connector/nixl_integration/
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
 
 - label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
@@ -87,8 +101,8 @@ steps:
     - vllm/v1/worker/kv_connector_model_runner_mixin.py
     - tests/v1/kv_connector/nixl_integration/
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - bash v1/kv_connector/nixl_integration/config_sweep_spec_decode_test.sh
 
 - label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
   key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
@@ -102,5 +116,5 @@ steps:
     - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
     - tests/v1/kv_connector/nixl_integration/
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
-    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - bash v1/kv_connector/nixl_integration/run_multi_connector_edge_case_test.sh

.buildkite/test_areas/e2e_integration.yaml

@@ -2,8 +2,8 @@ group: E2E Integration
 depends_on: 
   - image-build
 steps:
-- label: DeepSeek V2-Lite Accuracy
-  key: deepseek-v2-lite-accuracy
+- label: DeepSeek V2-Lite Sync EPLB Accuracy
+  key: deepseek-v2-lite-sync-eplb-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 Accuracy
-  key: qwen3-30b-a3b-fp8-block-accuracy
+- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy
+  key: qwen3-30b-a3b-fp8-block-sync-eplb-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 Accuracy (B200)
-  key: qwen3-30b-a3b-fp8-block-accuracy-b200
+- label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (B200)
+  key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-b200
   timeout_in_minutes: 60
   device: b200-k8s
   optional: true

.buildkite/test_areas/engine.yaml

@@ -26,6 +26,12 @@ 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
@@ -36,6 +42,12 @@ 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
@@ -46,8 +58,15 @@ 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:

.buildkite/test_areas/entrypoints.yaml

@@ -8,10 +8,11 @@ steps:
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/entrypoints
-  - tests/entrypoints/
+  - tests/entrypoints/unit_tests
+  - tests/entrypoints/weight_transfer
   commands:
-  - 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 --ignore=entrypoints/speech_to_text
+  - pytest -v -s entrypoints/unit_tests
+  - pytest -v -s entrypoints/weight_transfer
 
 - label: Entrypoints Integration (LLM)
   key: entrypoints-integration-llm
@@ -20,19 +21,36 @@ 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
+  - 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/test_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
+  - pytest -v -s entrypoints/llm/offline_mode # Needs to avoid interference with other tests
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
-- label: Entrypoints Integration (API Server openai - Part 1)
+- 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)
   key: entrypoints-integration-api-server-openai-part-1
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
@@ -42,9 +60,15 @@ steps:
   - tests/entrypoints/test_chat_utils
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - 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
+  - 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
 
-- 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"
@@ -53,38 +77,50 @@ 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
-  - pytest -v -s entrypoints/test_chat_utils.py
+  mirror:
+    amd:
+      device: mi325_1
+      timeout_in_minutes: 80
+      depends_on:
+      - image-build-amd
 
-- label: Entrypoints Integration (API Server openai - Part 3)
-  key: entrypoints-integration-api-server-openai-part-3
+- label: Entrypoints Integration (API Server Generate)
+  key: entrypoints-integration-api-server-generate
   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/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"
@@ -95,6 +131,18 @@ steps:
   - 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
   timeout_in_minutes: 50
@@ -106,16 +154,6 @@ 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
@@ -123,6 +161,22 @@ steps:
   source_file_dependencies:
   - csrc/
   - vllm/entrypoints/openai/
-  - vllm/model_executor/models/whisper.py
   commands: # LMEval
   - 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/

.buildkite/test_areas/kernels.yaml

@@ -21,8 +21,9 @@ 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
+    - 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
 
 - label: Kernels MiniMax Reduce RMS Test (2 GPUs)
   key: kernels-minimax-reduce-rms-test-2-gpus
@@ -38,6 +39,28 @@ 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
@@ -52,6 +75,19 @@ 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
@@ -62,6 +98,19 @@ 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
@@ -137,9 +186,12 @@ steps:
   - csrc/quantization/fp4/
   - csrc/attention/mla/
   - csrc/quantization/cutlass_w8a8/moe/
-  - 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/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/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
   - vllm/v1/attention/backends/mla/cutlass_mla.py
@@ -172,6 +224,7 @@ 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
@@ -184,7 +237,7 @@ steps:
   - vllm/utils/import_utils.py
   - tests/kernels/helion/
   commands:
-    - pip install helion==1.0.0
+    - pip install helion==1.1.0
     - pytest -v -s kernels/helion/
 
  
@@ -260,3 +313,4 @@ steps:
   - vllm/config
   commands:
     - pytest -v -s kernels/moe/test_moe_layer.py
+    - pytest -v -s kernels/moe/test_deepep_v2_moe.py

.buildkite/test_areas/lm_eval.yaml

@@ -3,6 +3,7 @@ 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:
@@ -11,6 +12,21 @@ 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
@@ -152,6 +168,7 @@ 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/

.buildkite/test_areas/lora.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: LoRA %N
+  device: h200_35gb
   key: lora
   timeout_in_minutes: 30
   source_file_dependencies:

.buildkite/test_areas/misc.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: V1 Spec Decode
+  device: h200_35gb
   key: v1-spec-decode
   timeout_in_minutes: 30
   source_file_dependencies:
@@ -51,7 +52,7 @@ steps:
     - pytest -v -s v1/test_outputs.py
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
@@ -85,7 +86,7 @@ steps:
     - tests/v1/metrics
     - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
-    - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # split the test to avoid interference
     - pytest -v -s -m 'not cpu_test' v1/core
@@ -129,6 +130,34 @@ 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
@@ -152,6 +181,7 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Examples
+  device: h200_35gb
   key: examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
@@ -222,6 +252,7 @@ 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:
@@ -265,6 +296,7 @@ steps:
   - vllm/model_executor/layers/quantization/quark/
   - vllm/multimodal/
   - vllm/outputs.py
+  - vllm/parser/
   - vllm/platforms/
   - vllm/pooling_params.py
   - vllm/ray/
@@ -276,6 +308,7 @@ 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
@@ -283,28 +316,43 @@ 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
@@ -337,8 +385,10 @@ 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

.buildkite/test_areas/model_executor.yaml

@@ -14,5 +14,12 @@ steps:
   commands:
     - apt-get update && apt-get install -y curl libsodium23
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s model_executor -m '(not slow_test)'
-    - pytest -v -s entrypoints/openai/completion/test_tensorizer_entrypoint.py
+    # 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

.buildkite/test_areas/model_runner_v2.yaml

@@ -3,6 +3,7 @@ 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:
@@ -26,6 +27,7 @@ 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"
@@ -92,13 +94,16 @@ 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"

.buildkite/test_areas/models_basic.yaml

@@ -18,6 +18,7 @@ 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:
@@ -34,6 +35,7 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Other)
+  device: h200_35gb
   key: basic-models-tests-other
   timeout_in_minutes: 45
   source_file_dependencies:
@@ -56,35 +58,3 @@ 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

.buildkite/test_areas/models_language.yaml

@@ -50,7 +50,8 @@ steps:
   mirror:
     torch_nightly: {}
     amd:
-      device: mi300_1
+      device: mi325_1
+      timeout_in_minutes: 90
       depends_on:
       - image-build-amd
       commands:
@@ -59,6 +60,7 @@ 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
@@ -84,6 +86,7 @@ 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
@@ -92,6 +95,12 @@ 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

.buildkite/test_areas/models_multimodal.yaml

@@ -15,7 +15,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
@@ -33,11 +33,12 @@ steps:
     - pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_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:
@@ -49,11 +50,12 @@ steps:
     - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_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:
@@ -92,6 +94,7 @@ 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"
@@ -115,11 +118,12 @@ steps:
     - pytest -v -s models/multimodal/test_mapping.py
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_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:
@@ -130,6 +134,7 @@ 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:
@@ -148,3 +153,12 @@ 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

.buildkite/test_areas/plugins.yaml

@@ -37,6 +37,20 @@ steps:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
   - pytest -v -s distributed/test_distributed_oot.py
-  - pytest -v -s entrypoints/openai/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
+  - 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

.buildkite/test_areas/pytorch.yaml

@@ -3,6 +3,7 @@ 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:

.buildkite/test_areas/quantization.yaml

@@ -21,6 +21,18 @@ 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

.buildkite/test_areas/rust_frontend.yaml

@@ -0,0 +1,107 @@
+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"

.buildkite/test_areas/rust_frontend_cargo.yaml

@@ -0,0 +1,30 @@
+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

.buildkite/test_areas/samplers.yaml

@@ -3,6 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Samplers Test
+  device: h200_35gb
   key: samplers-test
   timeout_in_minutes: 75
   source_file_dependencies:
@@ -10,6 +11,7 @@ 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.

.buildkite/test_areas/spec_decode.yaml

@@ -32,10 +32,26 @@ 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
@@ -60,6 +76,20 @@ 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
@@ -71,6 +101,20 @@ 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

.dockerignore

@@ -2,6 +2,7 @@
 /build
 dist
 vllm/*.so
+vllm/vllm-rs
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -31,3 +32,4 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
+rust/target/

.github/CODEOWNERS

@@ -23,9 +23,14 @@
 
 # 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 @hmellor @yewentao256 @ProExpertProg
+/vllm/config @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @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
@@ -34,12 +39,19 @@
 /vllm/entrypoints/speech_to_text/realtime @njhill
 /vllm/entrypoints/speech_to_text @NickLucche
 /vllm/entrypoints/pooling @noooop
-/vllm/entrypoints/sagemaker @DarkLight1337
+/vllm/entrypoints/serve/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
@@ -68,25 +80,27 @@
 /vllm/v1/worker/kv_connector_model_runner_mixin.py @orozery @NickLucche
 
 # Model runner V2
-/vllm/v1/worker/gpu @WoosukKwon @njhill
+/vllm/v1/worker/gpu @WoosukKwon @njhill @yewentao256
 /vllm/v1/worker/gpu/kv_connector.py @orozery
 
 # CI & building
-/.buildkite @Harry-Chen
-/docker/Dockerfile @Harry-Chen
+/.buildkite @Harry-Chen @khluu
+/docker/Dockerfile @Harry-Chen @khluu
+/pyproject.toml @khluu
+/setup.py @khluu
 
 # 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
-/tests/evals @mgoin @vadiklyutiy
-/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye
+/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @aarnphm @NickLucche @AndreasKaratzas
+/tests/evals @mgoin @vadiklyutiy @AndreasKaratzas
+/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye @AndreasKaratzas
 /tests/kernels/ir @ProExpertProg @tjtanaa
-/tests/models @DarkLight1337 @ywang96
+/tests/models @DarkLight1337 @ywang96 @AndreasKaratzas
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye @AndreasKaratzas
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -153,9 +167,7 @@ mkdocs.yaml @hmellor
 /vllm/model_executor/models/deepseek_mtp.py @luccafong
 
 # DeepseekV4-specific files
-/vllm/v1/attention/ops/deepseek_v4_ops @zyongye
-/vllm/model_executor/layers/deepseek_compressor.py @zyongye
-/vllm/model_executor/layers/deepseek_v4_attention.py @zyongye
+/vllm/models/deepseek_v4 @zyongye
 /vllm/model_executor/layers/sparse_attn_indexer.py @zyongye
 
 # Mistral-specific files
@@ -172,21 +184,21 @@ 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
-/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
+/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
 /docs/**/*rocm* @tjtanaa
 /vllm/**/*quark* @tjtanaa
-/tests/**/*quark* @tjtanaa
+/tests/**/*quark* @tjtanaa @AndreasKaratzas
 /docs/**/*quark* @tjtanaa
-/vllm/**/*aiter* @tjtanaa
-/tests/**/*aiter* @tjtanaa
+/vllm/**/*aiter* @tjtanaa @AndreasKaratzas
+/tests/**/*aiter* @tjtanaa @AndreasKaratzas
 
 # TPU
 /vllm/v1/worker/tpu* @NickLucche

.github/dependabot.yml

@@ -21,7 +21,6 @@ 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"

.github/mergify.yml

@@ -21,6 +21,9 @@ pull_request_rules:
     - check-failure=pre-commit
     - -closed
     - -draft
+    - or:
+      - label=ready
+      - label=verified
   actions:
     comment:
       message: |
@@ -36,18 +39,6 @@ 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:
@@ -103,6 +94,19 @@ 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:
@@ -140,12 +144,12 @@ pull_request_rules:
     - label != stale
     - or:
       - files~=^examples/.*mistral.*\.py
-      - files~=^tests/.*mistral.*\.py
-      - files~=^vllm/model_executor/models/.*mistral.*\.py
+      - files~=^tests/.*(?:mistral|voxtral|mixtral|pixtral).*\.py
+      - files~=^vllm/model_executor/models/.*(?:mistral|voxtral|mixtral|pixtral).*\.py
       - files~=^vllm/reasoning/.*mistral.*\.py
       - files~=^vllm/tool_parsers/.*mistral.*\.py
-      - files~=^vllm/transformers_utils/.*mistral.*\.py
-      - title~=(?i)Mistral
+      - files~=^vllm/transformers_utils/.*(?:mistral|voxtral|pixtral).*\.py
+      - title~=(?i)(?:mistral|ministral|voxtral|mixtral|pixtral)
   actions:
     label:
       add:

.github/workflows/add_label_automerge.yml

@@ -10,7 +10,7 @@ jobs:
         runs-on: ubuntu-latest
         steps:
             -   name: Add label
-                uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+                uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
                 with:
                     script: |
                         github.rest.issues.addLabels({

.github/workflows/issue_autolabel.yml

@@ -14,7 +14,7 @@ jobs:
     steps:
       - name: Label issues based on keywords
         id: label-step
-        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3  # v9.0.0
         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@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3  # v9.0.0
         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@ed597411d8f924073f98dfc5c65a23a2325f34cd  # v8.0.0
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3  # v9.0.0
         with:
           script: |
             const body = (context.payload.issue.body || '').toLowerCase();

.github/workflows/new_pr_bot.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Update PR description
-        uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
         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@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+        uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
         with:
           script: |
             const { owner, repo } = context.repo;

.github/workflows/pre-commit.yml

@@ -20,7 +20,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Check PR label and author merge count
-      uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
+      uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9.0.0
       with:
         script: |
           const { data: pr } = await github.rest.pulls.get({

.github/workflows/scripts/build.sh

@@ -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
@@ -17,7 +17,10 @@ $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
-export TORCH_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9 9.0 10.0 12.0+PTX"
+# 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"
 
 bash tools/check_repo.sh
 

.github/workflows/stale.yml

@@ -15,7 +15,7 @@ jobs:
       actions: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
+      - uses: actions/stale@eb5cf3af3ac0a1aa4c9c45633dd1ae542a27a899 # v10.3.0
         with:
           # Increasing this value ensures that changes to this workflow
           # propagate to all issues and PRs in days rather than months

.gitignore

@@ -15,6 +15,9 @@ 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
 
@@ -26,6 +29,9 @@ __pycache__/
 # C extensions
 *.so
 
+# Rust binaries
+vllm/vllm-rs
+
 # Distribution / packaging
 .Python
 build/
@@ -230,7 +236,7 @@ actionlint
 shellcheck*/
 
 # Ignore moe/marlin_moe gen code
-csrc/moe/marlin_moe_wna16/kernel_*
+csrc/libtorch_stable/moe/marlin_moe_wna16/kernel_*
 
 # Ignore ep_kernels_workspace folder
 ep_kernels_workspace/

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
   rev: v21.1.2
   hooks:
   - id: clang-format
-    exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
+    exclude: 'csrc/libtorch_stable/moe/topk_softmax_kernels.cu|vllm/third_party/.*'
     types_or: [c++, cuda]
     args: [--style=file, --verbose]
 - repo: https://github.com/DavidAnson/markdownlint-cli2
@@ -148,33 +148,27 @@ repos:
     language: python
     entry: python tools/pre_commit/generate_nightly_torch_test.py
     files: ^requirements/test/cuda\.(in|txt)$
-  - 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
+  - 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"
     <<: &mypy_common
       language: python
       types_or: [python, pyi]
       require_serial: true
-      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
+      additional_dependencies: ["mypy==1.20.2", regex, types-cachetools, types-setuptools, types-PyYAML, types-requests, types-torch, pydantic]
   - 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 1 "3.11"
+    entry: python tools/pre_commit/mypy.py "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 1 "3.12"
+    entry: python tools/pre_commit/mypy.py "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 1 "3.13"
+    entry: python tools/pre_commit/mypy.py "3.13"
     <<: *mypy_common
     stages: [manual] # Only run in CI
   - id: shellcheck
@@ -222,6 +216,12 @@ 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
@@ -256,6 +256,32 @@ 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

.readthedocs.yaml

@@ -9,45 +9,8 @@ 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:

AGENTS.md

@@ -98,7 +98,31 @@ pre-commit run --all-files
 pre-commit run ruff-check --all-files
 
 # Run mypy as it is in CI:
-pre-commit run mypy-3.10 --all-files --hook-stage manual
+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>"
 ```
 
 ### Commit messages

MANIFEST.in

@@ -4,6 +4,7 @@ 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 *

SECURITY.md

@@ -34,6 +34,15 @@ 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.

benchmarks/attention_benchmarks/README.md

@@ -108,7 +108,6 @@ python benchmark.py \
     --backends flash triton flashinfer \
     --batch-specs "q2k" "8q1s1k" "2q2k_32q1s1k" \
     --num-layers 10 \
-    --repeats 5 \
     --output-csv results.csv
 ```
 
@@ -164,14 +163,17 @@ 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)
---repeats N                         # Repetitions
---warmup-iters N                    # Warmup iterations
+--warmup-ms N                       # Warmup window in ms for triton do_bench
 --profile-memory                    # Profile memory usage
 
 # Parameter sweeps
@@ -211,8 +213,6 @@ config = BenchmarkConfig(
     num_kv_heads=1,
     block_size=128,
     device="cuda:0",
-    repeats=5,
-    warmup_iters=3,
 )
 
 # CUTLASS MLA with specific num_kv_splits
@@ -253,14 +253,10 @@ formatter.save_json(results, "output.json")
 
 ## Tips
 
-**1. Warmup matters** - Use `--warmup-iters 10` for stable results
+**1. Save results** - Always use `--output-csv` or `--output-json`
 
-**2. Multiple repeats** - Use `--repeats 20` for low variance
+**2. Test incrementally** - Start with `--num-layers 1`
 
-**3. Save results** - Always use `--output-csv` or `--output-json`
+**3. Extended grammar** - Leverage spec decode, chunked prefill patterns
 
-**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
+**4. Parameter sweeps** - Use `--sweep-param` and `--sweep-values` to find optimal values

benchmarks/attention_benchmarks/benchmark.py

@@ -26,6 +26,9 @@ Examples:
 """
 
 import argparse
+import os
+import shutil
+import subprocess
 import sys
 from dataclasses import replace
 from pathlib import Path
@@ -83,13 +86,15 @@ 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=str(e),
+            error=error_msg,
         )
 
 
@@ -115,9 +120,12 @@ def run_model_parameter_sweep(
     """
     all_results = []
 
-    console.print(
-        f"[yellow]Model sweep mode: testing {sweep.param_name} = {sweep.values}[/]"
+    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_desc}[/]")
 
     total = len(backends) * len(batch_specs) * len(sweep.values)
 
@@ -125,9 +133,9 @@ 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
+                    # Create config with modified model parameter(s)
                     config_args = base_config_args.copy()
-                    config_args[sweep.param_name] = value
+                    sweep.apply(config_args, value)
 
                     # Create config with original backend for running
                     clean_config = BenchmarkConfig(
@@ -144,13 +152,21 @@ 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} {sweep.param_name}="
-                            f"{value}: {result.error}[/]"
+                            f"[red]Error {backend} {spec} {err_label}"
+                            f": {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)
@@ -184,7 +200,10 @@ def run_model_parameter_sweep(
     )
 
     for param_value in sorted_param_values:
-        console.print(f"\n[bold cyan]{sweep.param_name} = {param_value}[/]")
+        label = (
+            f"{sweep.param_name} = {param_value}" if sweep.param_name else param_value
+        )
+        console.print(f"\n[bold cyan]{label}[/]")
         param_results = by_param_value[param_value]
 
         # Create modified results with original backend names
@@ -200,8 +219,9 @@ 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.param_name}, batch_spec):[/]"
+        f"\n[bold cyan]Optimal backend for each ({sweep_name}, batch_spec):[/]"
     )
 
     # Group by (param_value, batch_spec)
@@ -236,7 +256,10 @@ def run_model_parameter_sweep(
     for param_value, spec in sorted_keys:
         # Print header when param value changes
         if param_value != current_param_value:
-            console.print(f"\n  [bold]{sweep.param_name}={param_value}:[/]")
+            header = (
+                f"{sweep.param_name}={param_value}" if sweep.param_name else param_value
+            )
+            console.print(f"\n  [bold]{header}:[/]")
             current_param_value = param_value
 
         results = by_param_and_spec[(param_value, spec)]
@@ -322,6 +345,9 @@ 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]
@@ -474,11 +500,35 @@ 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("--repeats", type=int, default=1, help="Repetitions")
-    parser.add_argument("--warmup-iters", type=int, default=3, help="Warmup iterations")
+    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("--profile-memory", action="store_true", help="Profile memory")
     parser.add_argument(
         "--kv-cache-dtype",
@@ -491,10 +541,33 @@ def main():
         action=argparse.BooleanOptionalAction,
         default=True,
         help=(
-            "Launch kernels with CUDA graphs to eliminate CPU overhead"
-            "in measurements (default: True)"
+            "Use triton do_bench_cudagraph (True) or do_bench (False) "
+            "for timing. CUDA graphs eliminate CPU launch overhead "
+            "(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(
@@ -576,23 +649,28 @@ 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 "repeats" in yaml_config:
-            args.repeats = yaml_config["repeats"]
-        if "warmup_iters" in yaml_config:
-            args.warmup_iters = yaml_config["warmup_iters"]
+        if "warmup_ms" in yaml_config:
+            args.warmup_ms = yaml_config["warmup_ms"]
         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:
@@ -612,7 +690,7 @@ 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["param_name"],
+                param_name=sweep_config.get("param_name"),
                 values=sweep_config["values"],
                 label_format=sweep_config.get(
                     "label_format", "{backend}_{param_name}_{value}"
@@ -631,6 +709,32 @@ 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)
@@ -655,6 +759,18 @@ 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)
@@ -662,6 +778,15 @@ 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[/]")
@@ -708,11 +833,11 @@ 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
@@ -749,6 +874,7 @@ 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"],
@@ -770,6 +896,7 @@ 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"),
@@ -779,6 +906,9 @@ def main():
 
                 pbar.update(1)
 
+        if args.ncu_profile:
+            return
+
         # Display decode vs prefill results
         console.print("\n[bold green]Decode vs Prefill Results:[/]")
 
@@ -858,15 +988,20 @@ 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,
@@ -882,15 +1017,17 @@ 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
@@ -914,15 +1051,17 @@ 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)
@@ -935,9 +1074,10 @@ def main():
 
                         pbar.update(1)
 
-            console.print("\n[bold green]Results:[/]")
-            formatter = ResultsFormatter(console)
-            formatter.print_table(decode_results, backends)
+            if not args.ncu_profile:
+                console.print("\n[bold green]Results:[/]")
+                formatter = ResultsFormatter(console)
+                formatter.print_table(decode_results, backends)
 
         # Run prefill backend comparison
         if prefill_backends:
@@ -962,9 +1102,8 @@ 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,
                         )
 
@@ -980,16 +1119,17 @@ def main():
 
                         pbar.update(1)
 
-            console.print("\n[bold green]Prefill Backend Results:[/]")
-            formatter = ResultsFormatter(console)
-            formatter.print_table(
-                prefill_results, prefill_backends, compare_to_fastest=True
-            )
+            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
+                )
 
         all_results = decode_results + prefill_results
 
-    # Save results
-    if all_results:
+    # Save results (skip ncu profiling runs: timings are placeholder zeros)
+    if all_results and not args.ncu_profile:
         formatter = ResultsFormatter(console)
         if args.output_csv:
             formatter.save_csv(all_results, args.output_csv)

benchmarks/attention_benchmarks/common.py

@@ -15,6 +15,8 @@ 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]:
     """
@@ -34,6 +36,30 @@ 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
 
 
@@ -182,18 +208,37 @@ class ParameterSweep:
 
 @dataclass
 class ModelParameterSweep:
-    """Configuration for sweeping a model configuration parameter."""
+    """Configuration for sweeping model configuration parameter(s).
 
-    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
+    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}"
 
     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:
@@ -208,10 +253,10 @@ 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"
@@ -226,6 +271,7 @@ 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
@@ -234,6 +280,7 @@ class BenchmarkResult:
 
     config: BenchmarkConfig
     mean_time: float  # seconds
+    median_time: float  # seconds
     std_time: float  # seconds
     min_time: float  # seconds
     max_time: float  # seconds
@@ -252,6 +299,7 @@ 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,

benchmarks/attention_benchmarks/configs/mla_decode.yaml

@@ -53,10 +53,9 @@ backends:
   - FLASHINFER_MLA
   - FLASH_ATTN_MLA  # Hopper only
   - FLASHMLA        # Hopper only
+  - 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

benchmarks/attention_benchmarks/configs/mla_mixed_batch.yaml

@@ -51,8 +51,6 @@ backends:
   - FLASHMLA         # Hopper only
 
 device: "cuda:0"
-repeats: 5
-warmup_iters: 3
 profile_memory: true
 
 # Analyze chunked prefill workspace size impact

benchmarks/attention_benchmarks/configs/mla_prefill.yaml

@@ -3,6 +3,7 @@
 # Compares all available MLA prefill backends:
 #   FA backends:  fa2, fa3, fa4 (FlashAttention versions)
 #   Non-FA:       flashinfer, cudnn, trtllm (Blackwell-only, require flashinfer)
+#   CuTe DSL:     tokenspeed (Blackwell + R1 dims, requires tokenspeed_mla)
 #
 # Uses cutlass_mla as the decode backend for impl construction
 # (only the prefill path is exercised).
@@ -120,7 +121,6 @@ prefill_backends:
   - flashinfer
   - cudnn
   - trtllm
+  - tokenspeed
 
 device: "cuda:0"
-repeats: 20
-warmup_iters: 5

benchmarks/attention_benchmarks/configs/mla_sparse_decode.yaml

@@ -53,6 +53,4 @@ backends:
   - FLASHINFER_MLA_SPARSE
 
 device: "cuda:0"
-repeats: 100
-warmup_iters: 10
 profile_memory: true

benchmarks/attention_benchmarks/configs/mla_sparse_prefill.yaml

@@ -57,6 +57,4 @@ backends:
   - FLASHINFER_MLA_SPARSE
 
 device: "cuda:0"
-repeats: 10
-warmup_iters: 3
 profile_memory: true

benchmarks/attention_benchmarks/configs/reorder_threshold.yaml

@@ -63,8 +63,6 @@ model:
 
 # Benchmark settings
 device: "cuda:0"
-repeats: 15          # More repeats for spec decode variance
-warmup_iters: 5
 profile_memory: false
 
 # Output

benchmarks/attention_benchmarks/configs/speculative_decode.yaml

@@ -49,8 +49,6 @@ 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

benchmarks/attention_benchmarks/configs/standard_attention.yaml

@@ -43,6 +43,4 @@ backends:
   - FLASHINFER
 
 device: "cuda:0"
-repeats: 5
-warmup_iters: 3
 profile_memory: false

benchmarks/attention_benchmarks/configs/standard_decode.yaml

@@ -0,0 +1,142 @@
+# 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

benchmarks/attention_benchmarks/configs/standard_prefill.yaml

@@ -0,0 +1,108 @@
+# 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

benchmarks/attention_benchmarks/mla_runner.py

@@ -8,6 +8,8 @@ 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
@@ -17,6 +19,8 @@ from common import (
     MockIndexer,
     MockKVBProj,
     MockLayer,
+    run_do_bench,
+    run_ncu_profile,
     setup_mla_dims,
 )
 
@@ -29,6 +33,7 @@ from vllm.config import (
     VllmConfig,
     set_current_vllm_config,
 )
+from vllm.v1.attention.backends.mla.prefill.registry import MLAPrefillBackendEnum
 
 # ============================================================================
 # VllmConfig Creation
@@ -79,8 +84,8 @@ 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",
-                        "cudnn", "trtllm"). Configures the attention config to
-                        force the specified prefill backend.
+                        "trtllm"). Configures the attention config to force
+                        the specified prefill backend.
 
     Returns:
         VllmConfig for benchmarking
@@ -179,19 +184,13 @@ 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"
             ]
-        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
 
@@ -206,39 +205,27 @@ def create_minimal_vllm_config(
 _PREFILL_BACKEND_CONFIG: dict[str, dict] = {
     "fa2": {
         "flash_attn_version": 2,
-        "disable_flashinfer_prefill": True,
-        "use_cudnn_prefill": False,
-        "use_trtllm_ragged_deepseek_prefill": False,
+        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
     },
     "fa3": {
         "flash_attn_version": 3,
-        "disable_flashinfer_prefill": True,
-        "use_cudnn_prefill": False,
-        "use_trtllm_ragged_deepseek_prefill": False,
+        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
     },
     "fa4": {
         "flash_attn_version": 4,
-        "disable_flashinfer_prefill": True,
-        "use_cudnn_prefill": False,
-        "use_trtllm_ragged_deepseek_prefill": False,
+        "mla_prefill_backend": MLAPrefillBackendEnum.FLASH_ATTN,
     },
     "flashinfer": {
         "flash_attn_version": None,
-        "disable_flashinfer_prefill": False,
-        "use_cudnn_prefill": False,
-        "use_trtllm_ragged_deepseek_prefill": False,
-    },
-    "cudnn": {
-        "flash_attn_version": None,
-        "disable_flashinfer_prefill": True,
-        "use_cudnn_prefill": True,
-        "use_trtllm_ragged_deepseek_prefill": False,
+        "mla_prefill_backend": MLAPrefillBackendEnum.FLASHINFER,
     },
     "trtllm": {
         "flash_attn_version": None,
-        "disable_flashinfer_prefill": True,
-        "use_cudnn_prefill": False,
-        "use_trtllm_ragged_deepseek_prefill": True,
+        "mla_prefill_backend": MLAPrefillBackendEnum.TRTLLM_RAGGED,
+    },
+    "tokenspeed": {
+        "flash_attn_version": None,
+        "mla_prefill_backend": MLAPrefillBackendEnum.TOKENSPEED_MLA,
     },
 }
 
@@ -625,6 +612,21 @@ def _create_backend_impl(
     # Create mock layer
     layer = MockLayer(device, impl=impl, kv_cache_spec=kv_cache_spec)
 
+    # Attach a prefill backend (MLAAttention does this in __init__; the metadata
+    # builder reads layer.prefill_backend from static_forward_context).
+    from vllm.v1.attention.backends.mla.prefill import get_mla_prefill_backend
+
+    prefill_backend_cls = get_mla_prefill_backend(vllm_config)
+    layer.prefill_backend = prefill_backend_cls(
+        num_heads=mla_dims["num_q_heads"],
+        scale=(mla_dims["qk_nope_head_dim"] + mla_dims["qk_rope_head_dim"]) ** -0.5,
+        kv_lora_rank=mla_dims["kv_lora_rank"],
+        qk_nope_head_dim=mla_dims["qk_nope_head_dim"],
+        qk_rope_head_dim=mla_dims["qk_rope_head_dim"],
+        v_head_dim=mla_dims["v_head_dim"],
+        vllm_config=vllm_config,
+    )
+
     # Create builder instance if needed
     builder_instance = None
     if builder_class:
@@ -822,7 +824,7 @@ def _run_single_benchmark(
             num_prefill, mla_dims, query_fmt, device, torch.bfloat16
         )
 
-    # Build forward function
+    # Build forward function (runs a single decode/prefill pass)
     def forward_fn():
         results = []
         if has_decode:
@@ -841,44 +843,35 @@ def _run_single_benchmark(
             )
         return results[0] if len(results) == 1 else tuple(results)
 
-    # 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
-
-    # Benchmark
-    times = []
-    for _ in range(config.repeats):
-        start = torch.cuda.Event(enable_timing=True)
-        end = torch.cuda.Event(enable_timing=True)
-
-        start.record()
+    def benchmark_fn():
         for _ in range(config.num_layers):
-            benchmark_fn()
-        end.record()
+            forward_fn()
 
-        torch.accelerator.synchronize()
-        elapsed_ms = start.elapsed_time(end)
-        times.append(elapsed_ms / 1000.0 / config.num_layers)
+    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,
+        )
+
+    all_ms = run_do_bench(benchmark_fn, config.use_cuda_graphs, config.warmup_ms)
+
+    # Convert ms to seconds per layer
+    times = [t / 1000.0 / config.num_layers for t in all_ms]
+    mean_time = statistics.mean(times)
 
-    mean_time = float(np.mean(times))
     return BenchmarkResult(
         config=config,
         mean_time=mean_time,
-        std_time=float(np.std(times)),
-        min_time=float(np.min(times)),
-        max_time=float(np.max(times)),
+        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),
         throughput_tokens_per_sec=total_q / mean_time if mean_time > 0 else 0,
     )
 
@@ -961,19 +954,6 @@ def _run_mla_benchmark_batched(
     results = []
 
     with set_current_vllm_config(vllm_config):
-        # Clear cached prefill backend detection functions so they re-evaluate
-        # with the current VllmConfig. These are @functools.cache decorated and
-        # would otherwise return stale results from a previous backend's config.
-        from vllm.model_executor.layers.attention.mla_attention import (
-            use_cudnn_prefill,
-            use_flashinfer_prefill,
-            use_trtllm_ragged_deepseek_prefill,
-        )
-
-        use_flashinfer_prefill.cache_clear()
-        use_cudnn_prefill.cache_clear()
-        use_trtllm_ragged_deepseek_prefill.cache_clear()
-
         # Create backend impl, layer, builder, and indexer (reused across benchmarks)
         impl, layer, builder_instance, indexer = _create_backend_impl(
             backend_cfg,
@@ -985,38 +965,36 @@ def _run_mla_benchmark_batched(
             kv_cache_dtype=kv_cache_dtype,
         )
 
-        # Verify the actual prefill backend matches what was requested
+        # Verify the actual prefill backend matches what was requested. The
+        # selector + impl construction already raise on misuse; here we just
+        # check the resolved class against the requested name as a sanity guard.
         if prefill_backend is not None:
-            prefill_cfg = get_prefill_backend_config(prefill_backend)
-            fa_version = prefill_cfg["flash_attn_version"]
-
-            if fa_version is not None:
-                # FA backend: verify the impl's FA version
-                actual_fa_version = getattr(impl, "vllm_flash_attn_version", None)
+            expected_class = {
+                "fa2": "FlashAttnPrefillBackend",
+                "fa3": "FlashAttnPrefillBackend",
+                "fa4": "FlashAttnPrefillBackend",
+                "flashinfer": "FlashInferPrefillBackend",
+                "trtllm": "TrtllmRaggedPrefillBackend",
+                "tokenspeed": "TokenspeedMLAPrefillBackend",
+            }.get(prefill_backend)
+            actual_class = type(getattr(layer, "prefill_backend", None)).__name__
+            if expected_class and actual_class != expected_class:
+                raise RuntimeError(
+                    f"Prefill backend '{prefill_backend}' requested "
+                    f"{expected_class}, got {actual_class}. Check "
+                    f"attention_config plumbing or installed deps."
+                )
+            if prefill_backend in {"fa2", "fa3", "fa4"}:
+                fa_version = int(prefill_backend[2:])
+                actual_fa_version = getattr(
+                    layer.prefill_backend, "vllm_flash_attn_version", None
+                )
                 if actual_fa_version != fa_version:
                     raise RuntimeError(
                         f"Prefill backend '{prefill_backend}' requested FA "
-                        f"version {fa_version}, but the impl is using FA "
-                        f"version {actual_fa_version}. Check "
-                        f"vllm/v1/attention/backends/fa_utils.py."
+                        f"version {fa_version}, got "
+                        f"{actual_fa_version} on {actual_class}."
                     )
-            else:
-                # Non-FA backend: verify the builder picked the right path
-                expected_flags = {
-                    "flashinfer": "_use_fi_prefill",
-                    "cudnn": "_use_cudnn_prefill",
-                    "trtllm": "_use_trtllm_ragged_prefill",
-                }
-                flag_name = expected_flags.get(prefill_backend)
-                if flag_name and not getattr(builder_instance, flag_name, False):
-                    raise RuntimeError(
-                        f"Prefill backend '{prefill_backend}' was requested "
-                        f"but the metadata builder did not enable it. This "
-                        f"usually means a dependency is missing (e.g., "
-                        f"flashinfer not installed) or the platform doesn't "
-                        f"support it."
-                    )
-
         # Run each benchmark with the shared impl
         for config, threshold, num_splits in configs_with_params:
             # Set threshold for this benchmark (FlashAttn/FlashMLA only)