[Core] Add explicit layer parallel plans

Co-authored-by: OpenAI Codex <codex@openai.com>
Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

.buildkite/hardware_tests/cpu.yaml

@@ -141,9 +141,22 @@ steps:
   commands:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 45m "
-      pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
+      pytest -x -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_pixtral.py --ignore=tests/models/multimodal/generation/test_qwen2_5_vl.py -m cpu_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
   parallelism: 4
 
+- label: CPU-Qwen2.5-VL Multimodal Tests
+  depends_on: []
+  device: intel_cpu
+  no_plugin: true
+  source_file_dependencies:
+  # - vllm/
+  - vllm/model_executor/layers/rotary_embedding
+  - tests/models/multimodal/generation/
+  commands:
+    - |
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 40m "
+      VLLM_CI_ENV=0 pytest -x -v -s tests/models/multimodal/generation/test_qwen2_5_vl.py"
+
 - label: "Arm CPU Test"
   depends_on: []
   soft_fail: false

.buildkite/image_build/image_build.sh

@@ -79,12 +79,18 @@ setup_buildx_builder() {
     docker buildx ls | grep -E '^\*|^NAME' || docker buildx ls
 }
 
+annotate_image_tags() {
+    .buildkite/scripts/annotate-image-build.sh \
+        "${IMAGE_TAG:-}" "${IMAGE_TAG_LATEST:-}"
+}
+
 check_and_skip_if_image_exists() {
     if [[ -n "${IMAGE_TAG:-}" ]]; then
         echo "--- :mag: Checking if image exists"
         if docker manifest inspect "${IMAGE_TAG}" >/dev/null 2>&1; then
             echo "Image already exists: ${IMAGE_TAG}"
             echo "Skipping build"
+            annotate_image_tags
             exit 0
         fi
         echo "Image not found, proceeding with build"
@@ -171,6 +177,18 @@ BRANCH=$4
 IMAGE_TAG=$5
 IMAGE_TAG_LATEST=${6:-} # only used for main branch, optional
 
+# When TORCH_NIGHTLY=1, build the base CI image against PyTorch nightly so the
+# entire existing pipeline runs on nightly torch (CUDA/GPU lane only). Delegate
+# to the dedicated nightly build (PYTORCH_NIGHTLY=1, CUDA 13.0) and tag it at the
+# normal IMAGE_TAG that every test step already pulls -- no separate image tag,
+# no duplicate "vLLM Against PyTorch Nightly" pipeline section.
+if [[ "${TORCH_NIGHTLY:-0}" == "1" ]]; then
+    echo "--- :warning: TORCH_NIGHTLY=1 -- building base image on PyTorch nightly"
+    SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+    exec "${SCRIPT_DIR}/image_build_torch_nightly.sh" \
+        "${REGISTRY}" "${REPO}" "${BUILDKITE_COMMIT}" "${BRANCH}" "${IMAGE_TAG}"
+fi
+
 # build config
 TARGET="test-ci"
 VLLM_BAKE_FILE_PATH="${VLLM_BAKE_FILE_PATH:-docker/docker-bake.hcl}"
@@ -254,3 +272,5 @@ echo "--- :docker: Building ${TARGET}"
 docker --debug buildx bake -f "${VLLM_BAKE_FILE_PATH}" -f "${CI_HCL_PATH}" --progress plain "${TARGET}"
 
 echo "--- :white_check_mark: Build complete"
+
+annotate_image_tags

.buildkite/image_build/image_build_arm64.sh

@@ -9,30 +9,31 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
+IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-arm64"
 
 # 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
+if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
   echo "Image found"
-  exit 0
+else
+  echo "Image not found, proceeding with build..."
+  # build for arm64 GPU targets: Grace/GH200 (sm_90),
+  # Blackwell/Thor (sm_100/sm_103/sm_110), and DGX Spark/GB10
+  # (sm_121, family-covered by 12.0 under CUDA 13)
+  docker build --file docker/Dockerfile \
+    --platform linux/arm64 \
+    --build-arg max_jobs=16 \
+    --build-arg nvcc_threads=4 \
+    --build-arg torch_cuda_arch_list="9.0 10.0 11.0 12.0" \
+    --build-arg USE_SCCACHE=1 \
+    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+    --tag "$IMAGE" \
+    --target test \
+    --progress plain .
+  # push
+  docker push "$IMAGE"
 fi
 
-# build for arm64 GPU targets: Grace/GH200 (sm_90) and DGX Spark/GB10
-# (sm_121, family-covered by 12.0 under CUDA 13)
-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 12.0" \
-  --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":"$BUILDKITE_COMMIT"-arm64
+.buildkite/scripts/annotate-image-build.sh "$IMAGE"

.buildkite/image_build/image_build_cpu.sh

@@ -9,26 +9,26 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
+IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-cpu"
 
 # 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"-cpu) ]]; then
-  echo "Image not found, proceeding with build..."
-else
+if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
   echo "Image found"
-  exit 0
+else
+  echo "Image not found, proceeding with build..."
+  # build
+  docker build --file docker/Dockerfile.cpu \
+    --build-arg max_jobs=16 \
+    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+    --build-arg VLLM_CPU_X86=true \
+    --tag "$IMAGE" \
+    --target vllm-test \
+    --progress plain .
+  # push
+  docker push "$IMAGE"
 fi
 
-# build
-docker build --file docker/Dockerfile.cpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --build-arg VLLM_CPU_X86=true \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu \
-  --target vllm-test \
-  --progress plain .
-
-# push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-cpu
+.buildkite/scripts/annotate-image-build.sh "$IMAGE"

.buildkite/image_build/image_build_cpu_arm64.sh

@@ -9,25 +9,25 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
+IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-arm64-cpu"
 
 # 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-cpu) ]]; then
-  echo "Image not found, proceeding with build..."
-else
+if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
   echo "Image found"
-  exit 0
+else
+  echo "Image not found, proceeding with build..."
+  # build
+  docker build --file docker/Dockerfile.cpu \
+    --build-arg max_jobs=16 \
+    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+    --tag "$IMAGE" \
+    --target vllm-test \
+    --progress plain .
+  # push
+  docker push "$IMAGE"
 fi
 
-# build
-docker build --file docker/Dockerfile.cpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu \
-  --target vllm-test \
-  --progress plain .
-
-# push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-arm64-cpu
+.buildkite/scripts/annotate-image-build.sh "$IMAGE"

.buildkite/image_build/image_build_hpu.sh

@@ -9,26 +9,26 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
+IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-hpu"
 
 # 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"-hpu) ]]; then
-  echo "Image not found, proceeding with build..."
-else
+if docker manifest inspect "$IMAGE" >/dev/null 2>&1; then
   echo "Image found"
-  exit 0
+else
+  echo "Image not found, proceeding with build..."
+  # build
+  docker build \
+    --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
+    --build-arg max_jobs=16 \
+    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+    --tag "$IMAGE" \
+    --progress plain \
+    https://github.com/vllm-project/vllm-gaudi.git
+  # push
+  docker push "$IMAGE"
 fi
 
-# build
-docker build \
-  --file tests/pytorch_ci_hud_benchmark/Dockerfile.hpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu \
-  --progress plain \
-  https://github.com/vllm-project/vllm-gaudi.git
-
-# push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-hpu
+.buildkite/scripts/annotate-image-build.sh "$IMAGE"

.buildkite/image_build/image_build_torch_nightly.sh

@@ -40,6 +40,7 @@ docker buildx ls
 echo "--- :mag: Checking if image already exists"
 if docker manifest inspect "$IMAGE_TAG" >/dev/null 2>&1; then
   echo "Image found: $IMAGE_TAG — skipping build"
+  .buildkite/scripts/annotate-image-build.sh "$IMAGE_TAG"
   exit 0
 fi
 echo "Image not found, proceeding with build..."
@@ -66,3 +67,5 @@ docker buildx build --file docker/Dockerfile \
   --progress plain .
 
 echo "--- :white_check_mark: Torch nightly image build complete: $IMAGE_TAG"
+
+.buildkite/scripts/annotate-image-build.sh "$IMAGE_TAG"

.buildkite/image_build/image_build_xpu.sh

@@ -9,26 +9,26 @@ fi
 REGISTRY=$1
 REPO=$2
 BUILDKITE_COMMIT=$3
+IMAGE="$REGISTRY/$REPO:$BUILDKITE_COMMIT-xpu"
 
 # authenticate with AWS ECR
 aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$REGISTRY" || true
 aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 936637512419.dkr.ecr.us-east-1.amazonaws.com || true
 
 # skip build if image already exists
-if ! docker manifest inspect "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu &> /dev/null; then
-  echo "Image not found, proceeding with build..."
-else
+if docker manifest inspect "$IMAGE" &> /dev/null; then
   echo "Image found"
-  exit 0
+else
+  echo "Image not found, proceeding with build..."
+  # build
+  docker build \
+    --file docker/Dockerfile.xpu \
+    --build-arg max_jobs=16 \
+    --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
+    --tag "$IMAGE" \
+    --progress plain .
+  # push
+  docker push "$IMAGE"
 fi
 
-# build
-docker build \
-  --file docker/Dockerfile.xpu \
-  --build-arg max_jobs=16 \
-  --build-arg buildkite_commit="$BUILDKITE_COMMIT" \
-  --tag "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu \
-  --progress plain .
-
-# push
-docker push "$REGISTRY"/"$REPO":"$BUILDKITE_COMMIT"-xpu
+.buildkite/scripts/annotate-image-build.sh "$IMAGE"

.buildkite/intel_jobs/misc_intel.yaml

@@ -72,9 +72,7 @@ steps:
       pytest -v -s v1/test_oracle.py &&
       pytest -v -s v1/test_request.py &&
       pytest -v -s v1/test_outputs.py &&
-      pytest -v -s v1/sample/test_topk_topp_sampler.py &&
-      pytest -v -s v1/sample/test_logprobs.py &&
-      pytest -v -s v1/sample/test_logprobs_e2e.py'
+      pytest -v -s v1/sample'
 
 - label: Basic Models Tests (Initialization)
   timeout_in_minutes: 60

.buildkite/release-pipeline.yaml

@@ -350,6 +350,25 @@ steps:
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
           - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"'
 
+      - label: ":docker: Build release image - x86_64 - XPU"
+        depends_on: ~
+        id: build-xpu-release-image
+        agents:
+          queue: cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              $(bash .buildkite/scripts/docker-build-metadata-args.sh xpu) \
+              --build-arg GIT_REPO_CHECK=1 \
+              --target vllm-openai \
+              --progress plain \
+              -f docker/Dockerfile.xpu .
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-xpu"
+          - 'bash .buildkite/scripts/annotate-build-artifact.sh "$$BUILDKITE_LABEL" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-xpu"'
+        env:
+          DOCKER_BUILDKIT: "1"
+
       - block: "Build release image for x86_64 CPU"
         key: block-cpu-release-image-build
         depends_on: ~
@@ -445,6 +464,16 @@ steps:
           - "docker manifest push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
           - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: CUDA 12.9 Ubuntu 24.04" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"'
 
+      - label: "Create manifest - XPU"
+        depends_on:
+          - build-xpu-release-image
+        id: create-manifest-xpu
+        agents:
+          queue: small_cpu_queue_release
+        commands:
+          - "bash .buildkite/scripts/xpu/create-xpu-ecr-manifest.sh"
+          - 'bash .buildkite/scripts/annotate-build-artifact.sh "Manifest: XPU" "public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-xpu"'
+
       - label: "Publish nightly multi-arch image to DockerHub"
         depends_on:
           - create-multi-arch-manifest
@@ -819,6 +848,23 @@ steps:
       DOCKER_BUILDKIT: "1"
       S3_BUCKET: "vllm-wheels"
 
+  - label: "Publish nightly XPU image to DockerHub"
+    depends_on:
+      - create-manifest-xpu
+    if: build.env("NIGHTLY") == "1"
+    agents:
+      queue: small_cpu_queue_release
+    commands:
+      - "bash .buildkite/scripts/xpu/push-nightly-builds-xpu.sh"
+      - "bash .buildkite/scripts/cleanup-nightly-builds.sh nightly- vllm/vllm-openai-xpu"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+      DOCKERHUB_USERNAME: "vllmbot"
+
   - label: "Publish nightly ROCm image to DockerHub"
     depends_on:
       - build-rocm-release-image
@@ -849,6 +895,7 @@ steps:
       - create-multi-arch-manifest-cuda-12-9
       - create-multi-arch-manifest-ubuntu2404
       - create-multi-arch-manifest-cuda-12-9-ubuntu2404
+      - create-manifest-xpu
       - build-rocm-release-image
       - input-release-version
       # Wait for CPU builds if their block steps were unblocked, so publish

.buildkite/scripts/annotate-image-build.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Append the Docker image tag(s) an image-build step pushed to a Buildkite
+# annotation, so the built image tags show up on the build page instead of
+# being buried in the job logs.
+#
+# Usage: annotate-image-build.sh <image_tag> [<image_tag> ...]
+set -euo pipefail
+
+# buildkite-agent only exists on Buildkite agents; no-op elsewhere so the
+# image build scripts stay runnable locally.
+if ! command -v buildkite-agent >/dev/null 2>&1; then
+    echo "buildkite-agent not found; skipping image tag annotation"
+    exit 0
+fi
+
+label="${BUILDKITE_LABEL:-Image build}"
+content=""
+for image in "$@"; do
+    [[ -n "$image" ]] || continue
+    content+="- **${label}**: \`${image}\`"$'\n'
+done
+
+if [[ -z "$content" ]]; then
+    echo "No image tags provided; nothing to annotate"
+    exit 0
+fi
+
+# Best-effort: a flaky annotation must never fail an otherwise successful
+# (and expensive) image build.
+if ! printf '%s' "$content" | \
+    buildkite-agent annotate --append --style 'info' --context 'docker-images'; then
+    echo "warning: failed to annotate build with image tags"
+fi

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

@@ -29,7 +29,11 @@ if python3 -c "import torch; assert torch.version.hip" 2>/dev/null; then
         TORCH_INDEX_URL=""
     fi
 else
-    TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
+    if [ "${TORCH_NIGHTLY:-0}" = "1" ]; then
+        TORCH_INDEX_URL="https://download.pytorch.org/whl/nightly/cu130"
+    else
+        TORCH_INDEX_URL="https://download.pytorch.org/whl/cu130"
+    fi
 fi
 echo ">>> Using PyTorch index: ${TORCH_INDEX_URL:-PyPI default}"
 

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

@@ -130,6 +130,22 @@ docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm
 docker push vllm/vllm-openai-rocm:latest-base
 docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
 
+# ---- XPU ----
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu vllm/vllm-openai-xpu:latest-x86_64
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-xpu vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai-xpu:latest-x86_64
+docker push vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64
+
+docker manifest rm vllm/vllm-openai-xpu:latest || true
+docker manifest rm vllm/vllm-openai-xpu:v${RELEASE_VERSION} || true
+docker manifest create vllm/vllm-openai-xpu:latest vllm/vllm-openai-xpu:latest-x86_64 --amend
+docker manifest create vllm/vllm-openai-xpu:v${RELEASE_VERSION} vllm/vllm-openai-xpu:v${RELEASE_VERSION}-x86_64 --amend
+docker manifest push vllm/vllm-openai-xpu:latest
+docker manifest push vllm/vllm-openai-xpu:v${RELEASE_VERSION}
+
 # ---- CPU ----
 # CPU images are behind separate block steps and may not have been built.
 # All-or-nothing: inspect both arches first, then either publish everything

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

@@ -21,16 +21,20 @@ export CARGO_HOME="${CARGO_HOME:-$HOME/.cargo}"
 export RUSTUP_HOME="${RUSTUP_HOME:-$HOME/.rustup}"
 export PATH="$CARGO_HOME/bin:$PATH"
 
+PROTOC_VERSION="${PROTOC_VERSION:-31.1}"
+CARGO_BINSTALL_VERSION="${CARGO_BINSTALL_VERSION:-1.20.1}"
+UV_VERSION="${UV_VERSION:-0.11.28}"
+PYO3_PYTHON_VERSION="${PYO3_PYTHON_VERSION:-3.12}"
+
+CARGO_SORT_VERSION_REQ="${CARGO_SORT_VERSION_REQ:-2}"
+CARGO_DENY_VERSION_REQ="${CARGO_DENY_VERSION_REQ:-0.20}"
+CARGO_NEXTEST_VERSION_REQ="${CARGO_NEXTEST_VERSION_REQ:-0.9}"
+
 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)
@@ -45,16 +49,17 @@ install_protoc() {
       ;;
   esac
 
-  local url="https://github.com/protocolbuffers/protobuf/releases/download/v${version}/protoc-${version}-linux-${arch}.zip"
+  local url="https://github.com/protocolbuffers/protobuf/releases/download/v${PROTOC_VERSION}/protoc-${PROTOC_VERSION}-linux-${arch}.zip"
   local tmp_dir
   tmp_dir="$(mktemp -d)"
 
-  log_section "Installing protoc ${version}"
+  log_section "Installing protoc ${PROTOC_VERSION}"
   curl -L --proto '=https' --tlsv1.2 -sSf "$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"
+  protoc --version
 }
 
 rust_toolchain() {
@@ -75,66 +80,48 @@ install_rust_toolchain() {
 }
 
 install_cargo_binstall() {
-  if command -v cargo-binstall >/dev/null 2>&1; then
-    return
-  fi
-
-  log_section "Installing cargo-binstall"
+  log_section "Installing cargo-binstall ${CARGO_BINSTALL_VERSION}"
   curl -L --proto '=https' --tlsv1.2 -sSf \
-    https://raw.githubusercontent.com/cargo-bins/cargo-binstall/main/install-from-binstall-release.sh \
-    | bash
+    "https://raw.githubusercontent.com/cargo-bins/cargo-binstall/v${CARGO_BINSTALL_VERSION}/install-from-binstall-release.sh" \
+    | env BINSTALL_VERSION="$CARGO_BINSTALL_VERSION" bash
+  cargo-binstall -V
 }
 
 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
+  log_section "Installing cargo-sort ${CARGO_SORT_VERSION_REQ}"
+  cargo binstall --no-confirm --force "cargo-sort@${CARGO_SORT_VERSION_REQ}"
 }
 
 install_cargo_deny() {
-  if command -v cargo-deny >/dev/null 2>&1; then
-    return
-  fi
-
-  log_section "Installing cargo-deny"
-  install_cargo_binstall
-  cargo binstall --no-confirm cargo-deny
+  log_section "Installing cargo-deny ${CARGO_DENY_VERSION_REQ}"
+  cargo binstall --no-confirm --force "cargo-deny@${CARGO_DENY_VERSION_REQ}"
 }
 
 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
+  log_section "Installing cargo-nextest ${CARGO_NEXTEST_VERSION_REQ}"
+  cargo binstall \
+    --no-confirm \
+    --force \
+    --secure \
+    "cargo-nextest@${CARGO_NEXTEST_VERSION_REQ}"
 }
 
 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 \
+  log_section "Installing uv ${UV_VERSION}"
+  curl -L --proto '=https' --tlsv1.2 -sSf \
+    "https://github.com/astral-sh/uv/releases/download/${UV_VERSION}/uv-installer.sh" \
     | env UV_INSTALL_DIR="$CARGO_HOME/bin" sh
+  uv --version
 }
 
 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"
+  log_section "Installing Python ${PYO3_PYTHON_VERSION} for PyO3 tests"
+  uv python install "$PYO3_PYTHON_VERSION"
   PYO3_PYTHON="$(uv python find \
     --managed-python \
     --no-project \
     --resolve-links \
-    "$python_version")"
+    "$PYO3_PYTHON_VERSION")"
   export PYO3_PYTHON
 
   local python_libdir
@@ -156,6 +143,7 @@ PY
 }
 
 run_style_clippy() {
+  install_cargo_binstall
   install_cargo_sort
   install_cargo_deny
 
@@ -168,8 +156,8 @@ run_style_clippy() {
   log_section "Checking Rust dependency bans"
   cargo deny \
     --manifest-path rust/Cargo.toml \
-    check \
     --config rust/deny.toml \
+    check \
     bans
 
   log_section "Running clippy"
@@ -186,6 +174,7 @@ run_style_clippy() {
 run_tests() {
   install_uv
   setup_pyo3_python
+  install_cargo_binstall
   install_cargo_nextest
 
   log_section "Running cargo nextest"

.buildkite/scripts/trigger-ci-build.sh

@@ -23,6 +23,7 @@ NC='\033[0m' # No Color
 # Default configuration
 PIPELINE="ci"
 DRY_RUN=true
+TORCH_NIGHTLY=false
 
 usage() {
     cat <<EOF
@@ -34,12 +35,14 @@ Sets RUN_ALL=1 and NIGHTLY=1 environment variables.
 SAFETY: Dry-run by default. Use --execute to actually trigger a build.
 
 Options:
-    --execute       Actually trigger the build (default: dry-run)
-    --pipeline      Buildkite pipeline slug (default: ${PIPELINE})
-    --commit        Override commit SHA (default: current HEAD)
-    --branch        Override branch name (default: current branch)
-    --message       Custom build message (default: auto-generated)
-    --help          Show this help message
+    --execute        Actually trigger the build (default: dry-run)
+    --pipeline       Buildkite pipeline slug (default: ${PIPELINE})
+    --commit         Override commit SHA (default: current HEAD)
+    --branch         Override branch name (default: current branch)
+    --message        Custom build message (default: auto-generated)
+    --torch-nightly  Also build and run the full suite against torch nightly
+                     (sets TORCH_NIGHTLY=1)
+    --help           Show this help message
 
 Prerequisites:
     - bk CLI installed: brew tap buildkite/buildkite && brew install buildkite/buildkite/bk
@@ -49,6 +52,7 @@ Examples:
     $(basename "$0")                        # Dry-run, show what would happen
     $(basename "$0") --execute              # Actually trigger the build
     $(basename "$0") --pipeline ci-shadow   # Dry-run with different pipeline
+    $(basename "$0") --torch-nightly        # Dry-run a full torch-nightly run
 EOF
     exit 1
 }
@@ -96,6 +100,10 @@ while [[ $# -gt 0 ]]; do
             MESSAGE="$2"
             shift 2
             ;;
+        --torch-nightly)
+            TORCH_NIGHTLY=true
+            shift
+            ;;
         --help|-h)
             usage
             ;;
@@ -171,11 +179,17 @@ if [[ $(echo "$REMOTE_BRANCHES" | wc -l) -gt 5 ]]; then
 fi
 echo ""
 
+# Environment variables passed to the build.
+BUILD_ENV=("RUN_ALL=1" "NIGHTLY=1")
+if [[ "$TORCH_NIGHTLY" == true ]]; then
+    BUILD_ENV+=("TORCH_NIGHTLY=1")
+fi
+
 log_info "Pipeline: ${PIPELINE}"
 log_info "Branch: ${BRANCH}"
 log_info "Commit: ${COMMIT}"
 log_info "Message: ${MESSAGE}"
-log_info "Environment: RUN_ALL=1, NIGHTLY=1"
+log_info "Environment: ${BUILD_ENV[*]}"
 echo ""
 
 # Build the command
@@ -187,9 +201,10 @@ CMD=(bk build create
     --commit "${COMMIT}"
     --branch "${BRANCH}"
     --message "${MESSAGE}"
-    --env "RUN_ALL=1"
-    --env "NIGHTLY=1"
 )
+for env_var in "${BUILD_ENV[@]}"; do
+    CMD+=(--env "${env_var}")
+done
 
 if [[ "$DRY_RUN" == true ]]; then
     echo "=========================================="
@@ -210,8 +225,14 @@ if [[ "$DRY_RUN" == true ]]; then
     echo "    --commit '$(escape_for_shell "${COMMIT}")' \\"
     echo "    --branch '$(escape_for_shell "${BRANCH}")' \\"
     echo "    --message '$(escape_for_shell "${MESSAGE}")' \\"
-    echo "    --env 'RUN_ALL=1' \\"
-    echo "    --env 'NIGHTLY=1'"
+    last_idx=$(( ${#BUILD_ENV[@]} - 1 ))
+    for i in "${!BUILD_ENV[@]}"; do
+        if [[ $i -eq $last_idx ]]; then
+            echo "    --env '$(escape_for_shell "${BUILD_ENV[$i]}")'"
+        else
+            echo "    --env '$(escape_for_shell "${BUILD_ENV[$i]}")' \\"
+        fi
+    done
     echo ""
     echo "=========================================="
     echo -e "${YELLOW}To actually trigger this build, run:${NC}"

.buildkite/scripts/xpu/create-xpu-ecr-manifest.sh

@@ -0,0 +1,13 @@
+#!/bin/bash
+
+set -euo pipefail
+
+REGISTRY="public.ecr.aws/q9t5s3a7"
+REPO="vllm-release-repo"
+ARCH_TAG="${BUILDKITE_COMMIT}-$(uname -m)-xpu"
+PLATFORM_TAG="${BUILDKITE_COMMIT}-xpu"
+
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin ${REGISTRY}
+docker manifest rm ${REGISTRY}/${REPO}:${PLATFORM_TAG} || true
+docker manifest create ${REGISTRY}/${REPO}:${PLATFORM_TAG} ${REGISTRY}/${REPO}:${ARCH_TAG} --amend
+docker manifest push ${REGISTRY}/${REPO}:${PLATFORM_TAG}

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

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+set -ex
+
+ORIG_TAG_NAME="$BUILDKITE_COMMIT"
+REPO="vllm/vllm-openai-xpu"
+
+echo "Pushing original XPU tag ${ORIG_TAG_NAME}-xpu to nightly tags in ${REPO}"
+
+aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64-xpu
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:"$ORIG_TAG_NAME"-x86_64-xpu ${REPO}:nightly-x86_64
+docker push ${REPO}:nightly-x86_64
+
+docker manifest rm ${REPO}:nightly || true
+docker manifest rm ${REPO}:nightly-"$BUILDKITE_COMMIT" || true
+docker manifest create ${REPO}:nightly ${REPO}:nightly-x86_64 --amend
+docker manifest create ${REPO}:nightly-"$BUILDKITE_COMMIT" ${REPO}:nightly-x86_64 --amend
+docker manifest push ${REPO}:nightly
+docker manifest push ${REPO}:nightly-"$BUILDKITE_COMMIT"

.buildkite/test-amd.yaml

@@ -275,7 +275,7 @@ steps:
   - pytest -v -s v1/cudagraph/test_cudagraph_mode.py
 
 - label: e2e Core (1 GPU) # TBD
-  timeout_in_minutes: 35
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   optional: true
@@ -439,7 +439,7 @@ steps:
 #-----------------------------------------------------------  mi250 · docker  ----------------------------------------------------------#
 
 - label: Docker Build Metadata (ROCm) # TBD
-  timeout_in_minutes: 20
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   no_gpu: true
@@ -467,7 +467,7 @@ steps:
 #-----------------------------------------------------  mi300 · basic_correctness  -----------------------------------------------------#
 
 - label: Basic Correctness # TBD
-  timeout_in_minutes: 95
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   fast_check: true
@@ -484,7 +484,7 @@ steps:
   - pytest -v -s basic_correctness/test_cpu_offload.py
 
 - label: Distributed Model Tests (2 GPUs) # TBD
-  timeout_in_minutes: 110
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_2
   num_gpus: 2
@@ -724,7 +724,7 @@ steps:
   - pytest -v -s distributed/test_eplb_spec_decode.py
 
 - label: Distributed Tests (2xH100-2xMI300) # TBD
-  timeout_in_minutes: 75
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_2
   num_gpus: 2
@@ -1297,7 +1297,7 @@ steps:
 #---------------------------------------------------------  mi300 · examples  ----------------------------------------------------------#
 
 - label: Examples # TBD
-  timeout_in_minutes: 90
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -1348,7 +1348,7 @@ steps:
   - pytest -v -s tests/kernels/ir
 
 - label: Kernels Attention Test %N # TBD
-  timeout_in_minutes: 100
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -1397,7 +1397,7 @@ steps:
   - pytest -v -s kernels/test_kda.py
 
 - label: Kernels MoE Test %N # TBD
-  timeout_in_minutes: 95
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -1511,7 +1511,7 @@ steps:
 #----------------------------------------------------  mi300 · model_runner_v2  -------------------------------------------------------#
 
 - label: Model Runner V2 Core Tests # TBD
-  timeout_in_minutes: 60
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -1535,7 +1535,7 @@ 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 # TBD
-  timeout_in_minutes: 60
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -1569,7 +1569,7 @@ steps:
   - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Model Runner V2 Distributed (2 GPUs) # TBD
-  timeout_in_minutes: 60
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_2
   num_gpus: 2
@@ -1661,7 +1661,7 @@ steps:
   - pytest -v -s models/test_initialization.py::test_can_initialize_small_subset
 
 - label: Basic Models Tests (Other) # TBD
-  timeout_in_minutes: 90
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -1806,7 +1806,7 @@ steps:
   - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model
 
 - label: Multi-Modal Processor # 1h 42m
-  timeout_in_minutes: 138
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -1938,6 +1938,11 @@ steps:
   - pytest -v -s plugins_tests/test_stats_logger_plugins.py
   - pip uninstall dummy_stat_logger -y
   # END: `stat_logger` plugins test
+  # BEGIN: `endpoint` plugins test
+  - pip install -e ./plugins/vllm_add_dummy_endpoint_plugin
+  - pytest -v -s plugins_tests/test_endpoint_plugins.py
+  - pip uninstall vllm_add_dummy_endpoint_plugin -y
+  # END: `endpoint` plugins test
   # BEGIN: other tests
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
@@ -2021,7 +2026,7 @@ steps:
   - pytest -v -s entrypoints/serve/instrumentator/test_basic.py -k "not show_version and not server_load"
   - pytest -v -s entrypoints/scale_out/token_in_token_out/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/tokenize/test_tokenization.py -k "not tokenizer_info and not test_tokenize_chat"
+  - pytest -v -s entrypoints/serve/tokenize/test_tokenization.py -k "not tokenizer_info"
 
 - label: Rust Frontend Core Correctness # TBD
   timeout_in_minutes: 180
@@ -2250,7 +2255,7 @@ steps:
   - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
 - label: Spec Decode Eagle # TBD
-  timeout_in_minutes: 90
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   optional: true
@@ -2467,7 +2472,7 @@ steps:
   - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
 - label: Metrics, Tracing (2 GPUs) # TBD
-  timeout_in_minutes: 65
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_2
   optional: true
@@ -2673,7 +2678,7 @@ steps:
 #------------------------------------------------------  mi300 · weight_loading  -------------------------------------------------------#
 
 - label: Weight Loading Multiple GPU # TBD
-  timeout_in_minutes: 75
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_2
   num_gpus: 2
@@ -2685,7 +2690,7 @@ steps:
   - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
 
 - label: Weight Loading Multiple GPU - Large Models # TBD
-  timeout_in_minutes: 75
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_2
   num_gpus: 2
@@ -3084,7 +3089,7 @@ steps:
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-gfx950.txt
 
 - label: LM Eval Qwen3-5 Models (B200-MI355) # TBD
-  timeout_in_minutes: 120
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_2
   num_gpus: 2
@@ -3166,7 +3171,7 @@ steps:
 #---------------------------------------------------------  mi355 · examples  ----------------------------------------------------------#
 
 - label: Examples # TBD
-  timeout_in_minutes: 100
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/examples"
@@ -3201,7 +3206,7 @@ steps:
 #----------------------------------------------------------  mi355 · kernels  ----------------------------------------------------------#
 
 - label: Kernels (B200-MI355) # TBD
-  timeout_in_minutes: 15
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/"
@@ -3226,7 +3231,7 @@ steps:
   - pytest -v -s tests/kernels/attention/test_rocm_aiter_mla_decode_metadata.py
 
 - label: Kernels Attention Test %N # TBD
-  timeout_in_minutes: 100
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   parallelism: 2
@@ -3243,7 +3248,7 @@ steps:
   - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
 - label: Kernels MoE Test %N # TBD
-  timeout_in_minutes: 50
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   parallelism: 5
@@ -3507,7 +3512,7 @@ steps:
   - pytest -v -s v1/attention
 
 - label: V1 Core + KV + Metrics # TBD
-  timeout_in_minutes: 60
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   optional: true
@@ -3533,7 +3538,7 @@ steps:
   - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
 - label: V1 Sample + Logits # TBD
-  timeout_in_minutes: 60
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   optional: true
@@ -3553,7 +3558,7 @@ steps:
   - pytest -v -s v1/test_outputs.py
 
 - label: V1 Spec Decode # TBD
-  timeout_in_minutes: 60
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
@@ -3566,7 +3571,7 @@ steps:
 #------------------------------------------------------  mi355 · weight_loading  -------------------------------------------------------#
 
 - label: Weight Loading Multiple GPU # TBD
-  timeout_in_minutes: 75
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_2
   num_gpus: 2
@@ -3578,7 +3583,7 @@ steps:
   - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-amd.txt
 
 - label: Weight Loading Multiple GPU - Large Models # TBD
-  timeout_in_minutes: 75
+  timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_2
   working_dir: "/vllm-workspace/tests"

.buildkite/test_areas/attention.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: V1 attention (H100-MI300)
   key: v1-attention-h100-mi300
-  timeout_in_minutes: 30
+  timeout_in_minutes: 85
   device: h100
   source_file_dependencies:
     - vllm/config/attention.py
@@ -12,11 +12,12 @@ steps:
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - pytest -v -s v1/attention
+    - pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 70
+      timeout_in_minutes: 95
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -30,7 +31,7 @@ steps:
 
 - label: V1 attention (B200)
   key: v1-attention-b200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 80
   device: b200-k8s
   source_file_dependencies:
     - vllm/config/attention.py
@@ -38,4 +39,5 @@ steps:
     - vllm/v1/attention
     - tests/v1/attention
   commands:
-    - pytest -v -s v1/attention
+    - pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2

.buildkite/test_areas/basic_correctness.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Correctness
   key: basic-correctness
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -19,6 +19,6 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 50
+      timeout_in_minutes: 70
       depends_on:
       - image-build-amd

.buildkite/test_areas/benchmarks.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Benchmarks CLI Test
   key: benchmarks-cli-test
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -23,7 +23,7 @@ steps:
   num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/"
-  timeout_in_minutes: 10
+  timeout_in_minutes: 20
   source_file_dependencies:
   - benchmarks/attention_benchmarks/
   - vllm/v1/attention/

.buildkite/test_areas/compile.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Sequence Parallel Correctness Tests (2 GPUs)
   key: sequence-parallel-correctness-tests-2-gpus
-  timeout_in_minutes: 50
+  timeout_in_minutes: 80
   working_dir: "/vllm-workspace/"
   num_devices: 2
   source_file_dependencies:
@@ -19,7 +19,7 @@ steps:
 
 - label: Sequence Parallel Correctness Tests (2xH100)
   key: sequence-parallel-correctness-tests-2xh100
-  timeout_in_minutes: 50
+  timeout_in_minutes: 75
   working_dir: "/vllm-workspace/"
   device: h100
   optional: true
@@ -30,7 +30,7 @@ steps:
 
 - label: AsyncTP Correctness Tests (2xH100)
   key: asynctp-correctness-tests-2xh100
-  timeout_in_minutes: 50
+  timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: h100
   optional: true
@@ -41,7 +41,7 @@ steps:
 
 - label: AsyncTP Correctness Tests (B200)
   key: asynctp-correctness-tests-b200
-  timeout_in_minutes: 50
+  timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: b200-k8s
   optional: true
@@ -52,7 +52,7 @@ steps:
 
 - label: Distributed Compile Unit Tests (2xH100)
   key: distributed-compile-unit-tests-2xh100
-  timeout_in_minutes: 20
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/"
   device: h100
   num_devices: 2
@@ -66,7 +66,7 @@ steps:
 
 - label: Fusion and Compile Unit Tests (2xB200)
   key: fusion-and-compile-unit-tests-2xb200
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: b200-k8s
   source_file_dependencies:
@@ -96,7 +96,7 @@ steps:
 
 - label: Fusion E2E Quick (H100)
   key: fusion-e2e-quick-h100
-  timeout_in_minutes: 15
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/"
   device: h100
   num_devices: 1
@@ -115,7 +115,7 @@ steps:
 
 - label: Fusion E2E Config Sweep (H100)
   key: fusion-e2e-config-sweep-h100
-  timeout_in_minutes: 30
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/"
   device: h100
   num_devices: 1
@@ -149,7 +149,7 @@ steps:
 
 - label: Fusion E2E TP2 Quick (H100)
   key: fusion-e2e-tp2-quick-h100
-  timeout_in_minutes: 20
+  timeout_in_minutes: 35
   working_dir: "/vllm-workspace/"
   device: h100
   num_devices: 2
@@ -167,7 +167,7 @@ steps:
 
 - label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
   key: fusion-e2e-tp2-ar-rms-config-sweep-h100
-  timeout_in_minutes: 40
+  timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: h100
   num_devices: 2
@@ -207,7 +207,7 @@ steps:
 
 - label: Fusion E2E TP2 (B200)
   key: fusion-e2e-tp2-b200
-  timeout_in_minutes: 20
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/"
   device: b200-k8s
   num_devices: 2

.buildkite/test_areas/cuda.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Platform Tests
   key: platform-tests
-  timeout_in_minutes: 15
+  timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
   - vllm/envs.py
@@ -19,7 +19,7 @@ steps:
 
 - label: Cudagraph
   key: cudagraph
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   source_file_dependencies:
   - tests/v1/cudagraph
   - vllm/v1/cudagraph_dispatcher.py

.buildkite/test_areas/disaggregated.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Distributed NixlConnector PD accuracy (4 GPUs)
   key: distributed-nixlconnector-pd-accuracy-4-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 55
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -16,7 +16,7 @@ steps:
   mirror:
     amd:
       device: mi300_4
-      timeout_in_minutes: 110
+      timeout_in_minutes: 85
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -29,7 +29,7 @@ steps:
 
 - label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
   key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 55
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -39,6 +39,19 @@ steps:
     - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
     - FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
+- label: Push NixlConnector PP prefill PD accuracy (4 GPUs)
+  key: push-nixlconnector-pp-prefill-pd-accuracy-4-gpus
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/tests"
+  num_devices: 4
+  source_file_dependencies:
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - tests/v1/kv_connector/nixl_integration/
+    - tests/v1/kv_connector/nixl_push_integration/
+  commands:
+    - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+    - bash v1/kv_connector/nixl_push_integration/config_sweep_accuracy_test.sh
+
 - label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
   key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
   timeout_in_minutes: 30
@@ -53,7 +66,7 @@ steps:
   mirror:
     amd:
       device: mi300_4
-      timeout_in_minutes: 50
+      timeout_in_minutes: 60
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -66,7 +79,7 @@ steps:
 
 - label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
   key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 55
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -78,7 +91,7 @@ steps:
   mirror:
     amd:
       device: mi300_4
-      timeout_in_minutes: 110
+      timeout_in_minutes: 85
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -91,7 +104,7 @@ steps:
 
 - label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
   key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
-  timeout_in_minutes: 25
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -103,7 +116,7 @@ steps:
   mirror:
     amd:
       device: mi300_4
-      timeout_in_minutes: 60
+      timeout_in_minutes: 80
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -130,7 +143,7 @@ steps:
 
 - label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
   key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -145,7 +158,7 @@ steps:
 
 - label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
   key: nixlconnector-pd-spec-decode-acceptance-2-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   device: a100
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -159,7 +172,7 @@ steps:
   mirror:
     amd:
       device: mi300_2
-      timeout_in_minutes: 60
+      timeout_in_minutes: 70
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -173,7 +186,7 @@ steps:
 
 - label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
   key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:

.buildkite/test_areas/distributed.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Distributed Comm Ops
   key: distributed-comm-ops
-  timeout_in_minutes: 20
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -18,7 +18,7 @@ steps:
 
 - label: Distributed DP Tests (2 GPUs)
   key: distributed-dp-tests-2-gpus
-  timeout_in_minutes: 20
+  timeout_in_minutes: 35
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -55,7 +55,7 @@ steps:
 
 - label: Distributed Compile + RPC Tests (2 GPUs)
   key: distributed-compile-rpc-tests-2-gpus
-  timeout_in_minutes: 20
+  timeout_in_minutes: 65
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -78,7 +78,7 @@ steps:
 
 - label: Distributed Torchrun + Shutdown Tests (2 GPUs)
   key: distributed-torchrun-shutdown-tests-2-gpus
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -133,7 +133,7 @@ steps:
 
 - label: Distributed DP Tests (4 GPUs)
   key: distributed-dp-tests-4-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -154,7 +154,7 @@ steps:
 
 - label: Distributed Compile + Comm (4 GPUs)
   key: distributed-compile-comm-4-gpus
-  timeout_in_minutes: 30
+  timeout_in_minutes: 70
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -176,7 +176,7 @@ steps:
 
 - label: Distributed Tests (8xH100)
   key: distributed-tests-8xh100
-  timeout_in_minutes: 10
+  timeout_in_minutes: 20
   device: h100
   num_devices: 8
   working_dir: "/vllm-workspace/tests"
@@ -212,7 +212,7 @@ steps:
 
 - label: Distributed Tests (2xH100-2xMI300)
   key: distributed-tests-2xh100-2xmi300
-  timeout_in_minutes: 15
+  timeout_in_minutes: 30
   device: h100
   optional: true
   working_dir: "/vllm-workspace/"
@@ -259,7 +259,7 @@ steps:
 
 - label: Pipeline + Context Parallelism (4 GPUs)
   key: pipeline-context-parallelism-4-gpus
-  timeout_in_minutes: 60
+  timeout_in_minutes: 55
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -274,7 +274,7 @@ steps:
 
 - label: RayExecutorV2 (4 GPUs)
   key: rayexecutorv2-4-gpus
-  timeout_in_minutes: 60
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:

.buildkite/test_areas/docker.yaml

@@ -3,7 +3,7 @@ depends_on:
   - image-build-cpu
 steps:
 - label: Docker Build Metadata
-  timeout_in_minutes: 10
+  timeout_in_minutes: 20
   device: cpu-small
   source_file_dependencies:
     - .buildkite/release-pipeline.yaml

.buildkite/test_areas/e2e_integration.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: DeepSeek V2-Lite Sync EPLB Accuracy (4xH100)
   key: deepseek-v2-lite-sync-eplb-accuracy-4xh100
-  timeout_in_minutes: 60
+  timeout_in_minutes: 25
   device: h100
   optional: true
   num_devices: 4
@@ -14,7 +14,7 @@ steps:
 
 - label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (4xH100)
   key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-4xh100
-  timeout_in_minutes: 60
+  timeout_in_minutes: 25
   device: h100
   optional: true
   num_devices: 4
@@ -24,7 +24,7 @@ steps:
 
 - label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (2xB200)
   key: qwen3-30b-a3b-fp8-block-sync-eplb-accuracy-2xb200
-  timeout_in_minutes: 60
+  timeout_in_minutes: 20
   device: b200-k8s
   optional: true
   num_devices: 2
@@ -34,7 +34,7 @@ steps:
 
 - label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
   key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
-  timeout_in_minutes: 60
+  timeout_in_minutes: 25
   device: h100
   optional: true
   num_devices: 4
@@ -44,7 +44,7 @@ steps:
 
 - label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
   key: deepseek-v2-lite-prefetch-offload-accuracy-h100
-  timeout_in_minutes: 60
+  timeout_in_minutes: 20
   device: h100
   optional: true
   num_devices: 1

.buildkite/test_areas/engine.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Engine
   key: engine
-  timeout_in_minutes: 15
+  timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
   - vllm/compilation/
@@ -29,13 +29,13 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 60
+      timeout_in_minutes: 50
       depends_on:
       - image-build-amd
 
 - label: Engine (1 GPU)
   key: engine-1-gpu
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   source_file_dependencies:
     - vllm/v1/engine/
     - tests/v1/engine/
@@ -45,13 +45,13 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 40
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
 
 - label: e2e Scheduling (1 GPU)
   key: e2e-scheduling-1-gpu
-  timeout_in_minutes: 30
+  timeout_in_minutes: 35
   device: h200_18gb
   source_file_dependencies:
     - vllm/v1/
@@ -60,15 +60,15 @@ steps:
     - pytest -v -s v1/e2e/general/test_async_scheduling.py
   mirror:
     amd:
-      device: mi250_1
-      timeout_in_minutes: 60
+      device: mi325_1
+      timeout_in_minutes: 70
       depends_on:
       - image-build-amd
 
 - label: e2e Core (1 GPU)
   device: h200_35gb
   key: e2e-core-1-gpu
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   source_file_dependencies:
     - vllm/v1/
     - tests/v1/e2e/general/
@@ -76,8 +76,8 @@ steps:
     - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
   mirror:
     amd:
-      device: mi250_1
-      timeout_in_minutes: 35
+      device: mi325_1
+      timeout_in_minutes: 60
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -87,7 +87,7 @@ steps:
 
 - label: V1 e2e (2 GPUs)
   key: v1-e2e-2-gpus
-  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
+  timeout_in_minutes: 25 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 2
   source_file_dependencies:
@@ -120,7 +120,7 @@ steps:
 
 - label: V1 e2e (4 GPUs)
   key: v1-e2e-4-gpus
-  timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
+  timeout_in_minutes: 20 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 4
   source_file_dependencies:
@@ -148,7 +148,7 @@ steps:
 
 - label: V1 e2e (4xH100)
   key: v1-e2e-4xh100
-  timeout_in_minutes: 60
+  timeout_in_minutes: 35
   device: h100
   num_devices: 4
   optional: true

.buildkite/test_areas/entrypoints.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Entrypoints Unit Tests
   key: entrypoints-unit-tests
-  timeout_in_minutes: 10
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/entrypoints
@@ -16,7 +16,7 @@ steps:
 
 - label: Entrypoints Integration (LLM)
   key: entrypoints-integration-llm
-  timeout_in_minutes: 40
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -37,7 +37,7 @@ steps:
 - label: Entrypoints Integration (API Server)
   key: entrypoints-integration-api-server
   device: h200_35gb
-  timeout_in_minutes: 130
+  timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -56,7 +56,7 @@ steps:
 
 - label: Entrypoints Integration (API Server OpenAI - Part 1)
   key: entrypoints-integration-api-server-openai-part-1
-  timeout_in_minutes: 50
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -68,13 +68,13 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 80
+      timeout_in_minutes: 65
       depends_on:
       - image-build-amd
 
 - label: Entrypoints Integration (API Server OpenAI - Part 2)
   key: entrypoints-integration-api-server-openai-part-2
-  timeout_in_minutes: 50
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -109,7 +109,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 60
+      timeout_in_minutes: 65
       depends_on:
       - image-build-amd
 
@@ -126,7 +126,7 @@ steps:
 - label: Entrypoints Integration (Speech to Text)
   device: h200_35gb
   key: entrypoints-integration-speech_to_text
-  timeout_in_minutes: 50
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -138,7 +138,7 @@ steps:
 - label: Entrypoints Integration (Multimodal)
   device: h200_35gb
   key: entrypoints-integration-multimodal
-  timeout_in_minutes: 50
+  timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -160,7 +160,7 @@ steps:
 
 - label: OpenAI API Correctness
   key: openai-api-correctness
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
   - csrc/

.buildkite/test_areas/expert_parallelism.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: EPLB Algorithm
   key: eplb-algorithm
-  timeout_in_minutes: 15
+  timeout_in_minutes: 20
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -27,7 +27,7 @@ steps:
 
 - label: EPLB Execution # 17min
   key: eplb-execution
-  timeout_in_minutes: 27
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
@@ -39,7 +39,7 @@ steps:
 
 - label: Elastic EP Scaling Test
   key: elastic-ep-scaling-test
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   device: h100
   working_dir: "/vllm-workspace/tests"
   num_devices: 4

.buildkite/test_areas/kernels.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: vLLM IR Tests
   key: vllm-ir-tests
-  timeout_in_minutes: 10
+  timeout_in_minutes: 35
   device: h200_18gb
   working_dir: "/vllm-workspace/"
   source_file_dependencies:
@@ -16,18 +16,19 @@ steps:
 
 - label: Kernels Core Operation Test
   key: kernels-core-operation-test
-  timeout_in_minutes: 75
+  timeout_in_minutes: 120
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
   - tests/kernels/test_concat_mla_q.py
   - tests/kernels/test_fused_qk_norm_rope_gate.py
   commands:
-    - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_fused_qk_norm_rope_gate.py
+    - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py kernels/test_concat_mla_q.py kernels/test_fused_qk_norm_rope_gate.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 3
 
 - label: Kernels MiniMax Reduce RMS Test (2 GPUs)
   key: kernels-minimax-reduce-rms-test-2-gpus
-  timeout_in_minutes: 15
+  timeout_in_minutes: 20
   num_devices: 2
   device: h100
   source_file_dependencies:
@@ -41,7 +42,7 @@ steps:
 
 - label: Deepseek V4 Kernel Test (H100)
   key: deepseek-v4-kernel-test-h100
-  timeout_in_minutes: 15
+  timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
   - csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
@@ -54,7 +55,7 @@ steps:
 
 - label: Deepseek V4 Kernel Test (B200)
   key: deepseek-v4-kernel-test-b200
-  timeout_in_minutes: 15
+  timeout_in_minutes: 20
   device: b200-k8s
   source_file_dependencies:
   - csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu
@@ -65,7 +66,7 @@ steps:
 
 - label: Kernels Attention Test %N
   key: kernels-attention-test
-  timeout_in_minutes: 35
+  timeout_in_minutes: 65
   source_file_dependencies:
   - csrc/attention/
   - vllm/v1/attention
@@ -79,7 +80,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 55
+      timeout_in_minutes: 90
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -106,7 +107,7 @@ steps:
 
 - label: Kernels Quantization Test %N
   key: kernels-quantization-test
-  timeout_in_minutes: 90
+  timeout_in_minutes: 60
   source_file_dependencies:
   - csrc/quantization/
   - vllm/model_executor/layers/quantization
@@ -131,7 +132,7 @@ steps:
 
 - label: Kernels MoE Test %N
   key: kernels-moe-test
-  timeout_in_minutes: 25
+  timeout_in_minutes: 50
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
   - csrc/moe/
@@ -147,7 +148,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 50
+      timeout_in_minutes: 65
       source_file_dependencies:
       - csrc/quantization/cutlass_w8a8/moe/
       - csrc/moe/
@@ -163,7 +164,7 @@ steps:
 
 - label: Kernels Mamba Test
   key: kernels-mamba-test
-  timeout_in_minutes: 45
+  timeout_in_minutes: 40
   source_file_dependencies:
   - csrc/mamba/
   - tests/kernels/mamba
@@ -172,7 +173,7 @@ steps:
     - pytest -v -s kernels/mamba
 
 - label: Kernels KDA Test
-  timeout_in_minutes: 20
+  timeout_in_minutes: 25
   device: h200_18gb
   source_file_dependencies:
   - vllm/model_executor/layers/fla/ops/kda.py
@@ -184,7 +185,7 @@ steps:
 
 - label: Kernels DeepGEMM Test (H100)
   key: kernels-deepgemm-test-h100
-  timeout_in_minutes: 45
+  timeout_in_minutes: 35
   device: h100
   num_devices: 1
   source_file_dependencies:
@@ -211,7 +212,7 @@ steps:
 
 - label: Kernels (B200)
   key: kernels-b200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 80
   working_dir: "/vllm-workspace/"
   device: b200-k8s
   # optional: true
@@ -264,19 +265,20 @@ steps:
 
 - label: Kernels Helion Test
   key: kernels-helion-test
-  timeout_in_minutes: 30
+  timeout_in_minutes: 115
   device: h100
   source_file_dependencies:
   - vllm/utils/import_utils.py
   - tests/kernels/helion/
   commands:
     - pip install helion==1.1.0
-    - pytest -v -s kernels/helion/
+    - pytest -v -s kernels/helion/ --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2
 
  
 - label: Kernels FP8 MoE Test (1xH100)
   key: kernels-fp8-moe-test-1xh100
-  timeout_in_minutes: 90
+  timeout_in_minutes: 40
   device: h100
   num_devices: 1
   optional: true
@@ -293,7 +295,7 @@ steps:
 
 - label: Kernels FP8 MoE Test (2xH100)
   key: kernels-fp8-moe-test-2xh100
-  timeout_in_minutes: 90
+  timeout_in_minutes: 45
   device: h100
   num_devices: 2
   optional: true
@@ -303,7 +305,7 @@ steps:
 
 - label: Kernels Fp4 MoE Test (B200)
   key: kernels-fp4-moe-test-b200
-  timeout_in_minutes: 60
+  timeout_in_minutes: 25
   device: b200-k8s
   num_devices: 1
   optional: true
@@ -316,7 +318,7 @@ steps:
 
 - label: Kernels FusedMoE Layer Test (2 H100s)
   key: kernels-fusedmoe-layer-test-2-h100s
-  timeout_in_minutes: 90
+  timeout_in_minutes: 30
   device: h100
   num_devices: 2
   source_file_dependencies:

.buildkite/test_areas/lm_eval.yaml

@@ -5,7 +5,7 @@ steps:
 - label: LM Eval Small Models
   device: h200_35gb
   key: lm-eval-small-models
-  timeout_in_minutes: 75
+  timeout_in_minutes: 45
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
@@ -56,7 +56,7 @@ steps:
 
 - label: LM Eval Small Models (1xB200)
   key: lm-eval-small-models-1xb200
-  timeout_in_minutes: 120
+  timeout_in_minutes: 50
   device: b200-k8s
   optional: true
   source_file_dependencies:
@@ -80,7 +80,7 @@ steps:
 
 - label: LM Eval Large Models EP (2xB200)
   key: lm-eval-large-models-ep-2xb200
-  timeout_in_minutes: 120
+  timeout_in_minutes: 60
   device: b200-k8s
   optional: true
   num_devices: 2
@@ -92,7 +92,7 @@ steps:
 
 - label: LM Eval Qwen3.5 Models (2xB200)
   key: lm-eval-qwen3-5-models-2xb200
-  timeout_in_minutes: 120
+  timeout_in_minutes: 45
   device: b200-k8s
   optional: true
   num_devices: 2
@@ -109,7 +109,7 @@ steps:
 
 - label: LM Eval Large Models (8xH200)
   key: lm-eval-large-models-8xh200
-  timeout_in_minutes: 60
+  timeout_in_minutes: 50
   device: h200
   optional: true
   num_devices: 8
@@ -118,7 +118,7 @@ steps:
   mirror:
     amd:
       device: mi300_8
-      timeout_in_minutes: 180
+      timeout_in_minutes: 60
       depends_on:
       - image-build-amd
       commands:
@@ -152,7 +152,7 @@ steps:
 
 - label: LM Eval Humming f16 (A100 - TEMPORARY)
   key: lm-eval-humming-f16-a100
-  timeout_in_minutes: 120
+  timeout_in_minutes: 75
   device: a100
   optional: true
   num_devices: 1
@@ -167,7 +167,7 @@ steps:
 
 - label: LM Eval Humming Act int8 (A100 - TEMPORARY)
   key: lm-eval-humming-act-a100
-  timeout_in_minutes: 120
+  timeout_in_minutes: 45
   device: a100
   optional: true
   num_devices: 1
@@ -182,7 +182,7 @@ steps:
 
 - label: LM Eval Humming f16 (H100 - TEMPORARY)
   key: lm-eval-humming-f16-h100
-  timeout_in_minutes: 120
+  timeout_in_minutes: 70
   device: h100
   optional: true
   num_devices: 1
@@ -197,7 +197,7 @@ steps:
 
 - label: LM Eval Humming Act fp8/int8 (H100 - TEMPORARY)
   key: lm-eval-humming-act-h100
-  timeout_in_minutes: 120
+  timeout_in_minutes: 70
   device: h100
   optional: true
   num_devices: 1
@@ -213,7 +213,7 @@ steps:
 
 - label: LM Eval Humming f16 (B200 - TEMPORARY)
   key: lm-eval-humming-f16-b200
-  timeout_in_minutes: 120
+  timeout_in_minutes: 50
   device: b200-k8s
   optional: true
   num_devices: 1
@@ -228,7 +228,7 @@ steps:
 
 - label: LM Eval Humming Act fp8/int8 (B200 - TEMPORARY)
   key: lm-eval-humming-act-b200
-  timeout_in_minutes: 120
+  timeout_in_minutes: 50
   device: b200-k8s
   optional: true
   num_devices: 1
@@ -244,7 +244,7 @@ steps:
 
 - label: LM Eval TurboQuant KV Cache
   key: lm-eval-turboquant-kv-cache
-  timeout_in_minutes: 75
+  timeout_in_minutes: 55
   device: h200_18gb
   source_file_dependencies:
   - vllm/model_executor/layers/quantization/turboquant/
@@ -256,7 +256,7 @@ steps:
 
 - label: GPQA Eval (GPT-OSS) (2xH100)
   key: gpqa-eval-gpt-oss-2xh100
-  timeout_in_minutes: 120
+  timeout_in_minutes: 35
   device: h100
   optional: true
   num_devices: 2
@@ -270,7 +270,7 @@ steps:
 
 - label: GPQA Eval (GPT-OSS) (2xB200)
   key: gpqa-eval-gpt-oss-2xb200
-  timeout_in_minutes: 120
+  timeout_in_minutes: 30
   device: b200-k8s
   optional: true
   num_devices: 2
@@ -284,7 +284,7 @@ steps:
 
 - label: GPQA Eval (GPT-OSS) (DGX Spark)
   key: gpqa-eval-gpt-oss-spark
-  timeout_in_minutes: 120
+  timeout_in_minutes: 35
   device: dgx-spark
   optional: true
   num_devices: 1
@@ -298,9 +298,50 @@ steps:
     - uv pip install --system 'gpt-oss[eval]==0.0.5'
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-spark.txt
 
+- label: LM Eval KV-Offload (1xH200)
+  key: kv-offload-small
+  timeout_in_minutes: 30
+  device: h200_35gb
+  source_file_dependencies:
+  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+  - vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
+  - vllm/v1/kv_offload/
+  - vllm/v1/simple_kv_offload/
+  - tests/evals/gsm8k/test_gsm8k_offloading.py
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "nemotron-h-8b or gemma-4-e4b-it"
+
+- label: LM Eval KV-Offload (2xH100)
+  key: kv-offload-medium
+  timeout_in_minutes: 30
+  device: h100
+  num_devices: 2
+  source_file_dependencies:
+  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+  - vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
+  - vllm/v1/kv_offload/
+  - vllm/v1/simple_kv_offload/
+  - tests/evals/gsm8k/test_gsm8k_offloading.py
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "qwen3.5-35b"
+
+- label: LM Eval KV-Offload (4xH100)
+  key: kv-offload-large
+  timeout_in_minutes: 40
+  device: h100
+  num_devices: 4
+  source_file_dependencies:
+  - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
+  - vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py
+  - vllm/v1/kv_offload/
+  - vllm/v1/simple_kv_offload/
+  - tests/evals/gsm8k/test_gsm8k_offloading.py
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "deepseek-v4-flash"
+
 - label: MRCR Eval Small Models
   device: h200_35gb
-  timeout_in_minutes: 30
+  timeout_in_minutes: 25
   source_file_dependencies:
   - tests/evals/mrcr/
   commands:

.buildkite/test_areas/lora.yaml

@@ -5,7 +5,7 @@ steps:
 - label: LoRA %N
   device: h200_35gb
   key: lora
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   source_file_dependencies:
   - vllm/lora
   - tests/lora
@@ -16,7 +16,7 @@ steps:
     amd:
       device: mi325_1
       working_dir: "/vllm-workspace/tests"
-      timeout_in_minutes: 60
+      timeout_in_minutes: 65
       source_file_dependencies:
       - vllm/lora
       - tests/lora
@@ -27,7 +27,7 @@ steps:
 
 - label: LoRA TP (Distributed)
   key: lora-tp-distributed
-  timeout_in_minutes: 30
+  timeout_in_minutes: 60
   num_devices: 4
   source_file_dependencies:
   - vllm/lora

.buildkite/test_areas/misc.yaml

@@ -5,7 +5,7 @@ steps:
 - label: V1 Spec Decode
   device: h200_35gb
   key: v1-spec-decode
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   source_file_dependencies:
     - vllm/config/
     - vllm/distributed/
@@ -24,13 +24,13 @@ steps:
   mirror:
     amd:
       device: mi300_1
-      timeout_in_minutes: 65
+      timeout_in_minutes: 75
       depends_on:
       - image-build-amd
 
 - label: V1 Sample + Logits
   key: v1-sample-logits
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
     - vllm/config/
@@ -64,7 +64,7 @@ steps:
 
 - label: V1 Core + KV + Metrics
   key: v1-core-kv-metrics
-  timeout_in_minutes: 30
+  timeout_in_minutes: 60
   source_file_dependencies:
     - vllm/config/
     - vllm/distributed/
@@ -89,6 +89,7 @@ steps:
     - tests/v1/simple_kv_offload
     - tests/v1/worker
     - tests/v1/kv_connector/unit
+    - tests/v1/ec_connector/unit
     - tests/v1/metrics
     - tests/entrypoints/openai/correctness/test_lmeval.py
   commands:
@@ -101,6 +102,7 @@ steps:
     - pytest -v -s v1/simple_kv_offload
     - pytest -v -s v1/worker
     - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit
+    - pytest -v -s -m 'not cpu_test' v1/ec_connector/unit
     - pytest -v -s -m 'not cpu_test' v1/metrics
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/vllm-project/lm-evaluation-harness.git@streaming-api
@@ -108,7 +110,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 60
+      timeout_in_minutes: 75
       depends_on:
       - image-build-amd
 
@@ -172,7 +174,7 @@ steps:
 
 - label: Regression
   key: regression
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
   - vllm/config/
@@ -195,7 +197,7 @@ steps:
 - label: Examples
   device: h200_35gb
   key: examples
-  timeout_in_minutes: 45
+  timeout_in_minutes: 40
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
   - vllm/entrypoints
@@ -237,7 +239,7 @@ steps:
 
 - label: Metrics, Tracing (2 GPUs)
   key: metrics-tracing-2-gpus
-  timeout_in_minutes: 20
+  timeout_in_minutes: 25
   num_devices: 2
   source_file_dependencies:
   - vllm/config/
@@ -281,7 +283,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 20
+      timeout_in_minutes: 45
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -292,7 +294,7 @@ steps:
 - label: Async Engine, Inputs, Utils, Worker
   device: h200_35gb
   key: async-engine-inputs-utils-worker
-  timeout_in_minutes: 50
+  timeout_in_minutes: 25
   source_file_dependencies:
   - vllm/assets/
   - vllm/config/
@@ -319,7 +321,7 @@ steps:
   key: async-engine-inputs-utils-worker-config-cpu
   depends_on:
   - image-build-cpu
-  timeout_in_minutes: 30
+  timeout_in_minutes: 65
   source_file_dependencies:
   - vllm/assets/
   - vllm/config/
@@ -381,7 +383,7 @@ steps:
 
 - label: Batch Invariance (A100)
   key: batch-invariance-a100
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   device: a100
   source_file_dependencies:
     - vllm/v1/attention
@@ -395,7 +397,7 @@ steps:
 
 - label: Batch Invariance (H100)
   key: batch-invariance-h100
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   device: h100
   source_file_dependencies:
     - vllm/v1/attention
@@ -411,7 +413,7 @@ steps:
 
 - label: Batch Invariance (B200)
   key: batch-invariance-b200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 35
   device: b200-k8s
   source_file_dependencies:
     - vllm/v1/attention
@@ -430,7 +432,7 @@ steps:
 - label: Acceptance Length Test (Large Models) # optional
   device: h200_35gb
   key: acceptance-length-test-large-models
-  timeout_in_minutes: 25
+  timeout_in_minutes: 20
   gpu: h100
   optional: true
   num_gpus: 1

.buildkite/test_areas/model_executor.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Model Executor
   key: model-executor
-  timeout_in_minutes: 35
+  timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/engine/arg_utils.py
   - vllm/config/model.py

.buildkite/test_areas/model_runner_v2.yaml

@@ -5,7 +5,7 @@ steps:
 - label: Model Runner V2 Core Tests
   device: h200_35gb
   key: model-runner-v2-core-tests
-  timeout_in_minutes: 45
+  timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/v1/worker/gpu/
   - vllm/v1/worker/gpu_worker.py
@@ -27,7 +27,7 @@ steps:
 - label: Model Runner V2 Examples
   device: h200_35gb
   key: model-runner-v2-examples
-  timeout_in_minutes: 45
+  timeout_in_minutes: 35
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
     - vllm/v1/worker/gpu/
@@ -63,7 +63,7 @@ steps:
 
 - label: Model Runner V2 Distributed (2 GPUs)
   key: model-runner-v2-distributed-2-gpus
-  timeout_in_minutes: 45
+  timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -84,7 +84,7 @@ steps:
 
 - label: Model Runner V2 Pipeline Parallelism (4 GPUs)
   key: model-runner-v2-pipeline-parallelism-4-gpus
-  timeout_in_minutes: 60
+  timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:

.buildkite/test_areas/models_basic.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Basic Models Tests (Initialization)
   key: basic-models-tests-initialization
-  timeout_in_minutes: 45
+  timeout_in_minutes: 25
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -17,7 +17,7 @@ steps:
 - label: Basic Models Tests (Extra Initialization) %N
   device: h200_35gb
   key: basic-models-tests-extra-initialization
-  timeout_in_minutes: 45
+  timeout_in_minutes: 100
   source_file_dependencies:
   - vllm/model_executor/models/
   - tests/models/test_initialization.py
@@ -27,12 +27,12 @@ steps:
     # subset of supported models (the complement of the small subset in the above
     # test.) Also run if model initialization test file is modified
     - pytest -v -s models/test_initialization.py -k 'not test_can_initialize_small_subset' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-  parallelism: 2
+  parallelism: 4
 
 - label: Basic Models Tests (Other)
   device: h200_35gb
   key: basic-models-tests-other
-  timeout_in_minutes: 45
+  timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/
   - tests/models/test_terratorch.py
@@ -50,7 +50,7 @@ steps:
   key: basic-models-test-other-cpu
   depends_on:
   - image-build-cpu
-  timeout_in_minutes: 10
+  timeout_in_minutes: 20
   source_file_dependencies:
   - vllm/
   - tests/models/test_utils.py

.buildkite/test_areas/models_distributed.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Distributed Model Tests (2 GPUs)
   key: distributed-model-tests-2-gpus
-  timeout_in_minutes: 50
+  timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:

.buildkite/test_areas/models_language.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Language Models Tests (Standard)
   key: language-models-tests-standard
-  timeout_in_minutes: 25
+  timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -21,7 +21,7 @@ steps:
 
 - label: Language Models Tests (Extra Standard) %N
   key: language-models-tests-extra-standard
-  timeout_in_minutes: 45
+  timeout_in_minutes: 40
   source_file_dependencies:
   - vllm/model_executor/models/
   - tests/models/language/pooling/test_embedding.py
@@ -52,7 +52,7 @@ steps:
 
 - label: Language Models Tests (Hybrid) %N
   key: language-models-tests-hybrid
-  timeout_in_minutes: 75
+  timeout_in_minutes: 65
   source_file_dependencies:
   - vllm/
   - tests/models/language/generation
@@ -67,7 +67,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 90
+      timeout_in_minutes: 70
       depends_on:
       - image-build-amd
       commands:
@@ -78,7 +78,7 @@ steps:
 - label: Language Models Test (Extended Generation) # 80min
   device: h200_35gb
   key: language-models-test-extended-generation
-  timeout_in_minutes: 110
+  timeout_in_minutes: 65
   optional: true
   source_file_dependencies:
   - vllm/
@@ -92,7 +92,7 @@ steps:
 
 - label: Language Models Test (PPL)
   key: language-models-test-ppl
-  timeout_in_minutes: 110
+  timeout_in_minutes: 30
   device: h200_18gb
   optional: true
   source_file_dependencies:
@@ -104,7 +104,7 @@ steps:
 - label: Language Models Test (Extended Pooling)  # 36min
   device: h200_35gb
   key: language-models-test-extended-pooling
-  timeout_in_minutes: 50
+  timeout_in_minutes: 70
   optional: true
   source_file_dependencies:
   - vllm/
@@ -120,7 +120,7 @@ steps:
 
 - label: Language Models Test (MTEB)
   key: language-models-test-mteb
-  timeout_in_minutes: 110
+  timeout_in_minutes: 45
   device: h200_18gb
   optional: true
   source_file_dependencies:

.buildkite/test_areas/models_multimodal.yaml

@@ -20,7 +20,7 @@ steps:
 
 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
   key: multi-modal-models-standard-2-qwen3-gemma
-  timeout_in_minutes: 45
+  timeout_in_minutes: 50
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -38,7 +38,7 @@ steps:
 - 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
+  timeout_in_minutes: 40
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
@@ -54,7 +54,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
   device: h200_35gb
   key: multi-modal-models-standard-4-other-whisper
-  timeout_in_minutes: 45
+  timeout_in_minutes: 50
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
@@ -69,22 +69,23 @@ steps:
       depends_on:
       - image-build-amd
 
-- label: Multi-Modal Processor (CPU)
+- label: Multi-Modal Processor (CPU) %N
   key: multi-modal-processor-cpu
   depends_on:
   - image-build-cpu
-  timeout_in_minutes: 60
+  timeout_in_minutes: 125
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
   - tests/models/registry.py
   device: cpu-medium
   commands:
-    - 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
+  parallelism: 4
 
 - label: Multi-Modal Processor # 44min
   key: multi-modal-processor
-  timeout_in_minutes: 60
+  timeout_in_minutes: 65
   device: h200_18gb
   source_file_dependencies:
   - vllm/
@@ -96,7 +97,7 @@ steps:
 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
   device: h200_35gb
   key: multi-modal-accuracy-eval-small-models
-  timeout_in_minutes: 70
+  timeout_in_minutes: 30
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
   - vllm/multimodal/
@@ -164,7 +165,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 60
+      timeout_in_minutes: 75
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/plugins.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Plugin Tests (2 GPUs)
   key: plugin-tests-2-gpus
-  timeout_in_minutes: 60
+  timeout_in_minutes: 35
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
@@ -37,6 +37,11 @@ steps:
   - pytest -v -s plugins_tests/test_stats_logger_plugins.py
   - pip uninstall dummy_stat_logger -y
   # end stat_logger plugins test
+  # begin endpoint plugins test
+  - pip install -e ./plugins/vllm_add_dummy_endpoint_plugin
+  - pytest -v -s plugins_tests/test_endpoint_plugins.py
+  - pip uninstall vllm_add_dummy_endpoint_plugin -y
+  # end endpoint plugins test
   # other tests continue here:
   - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model

.buildkite/test_areas/pytorch.yaml

@@ -5,7 +5,7 @@ steps:
 - label: PyTorch Compilation Unit Tests
   device: h200_35gb
   key: pytorch-compilation-unit-tests
-  timeout_in_minutes: 10
+  timeout_in_minutes: 90
   source_file_dependencies:
     - vllm/__init__.py
     - vllm/_aiter_ops.py
@@ -78,7 +78,7 @@ steps:
 
 - label: PyTorch Compilation Passes Unit Tests
   key: pytorch-compilation-passes-unit-tests
-  timeout_in_minutes: 20
+  timeout_in_minutes: 45
   source_file_dependencies:
     - vllm/__init__.py
     - vllm/_aiter_ops.py
@@ -110,13 +110,13 @@ steps:
   mirror:
     amd:
       device: mi300_1
-      timeout_in_minutes: 180
+      timeout_in_minutes: 65
       depends_on:
       - image-build-amd
 
 - label: PyTorch Fullgraph Smoke Test
   key: pytorch-fullgraph-smoke-test
-  timeout_in_minutes: 35
+  timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/__init__.py
   - vllm/_aiter_ops.py
@@ -152,7 +152,7 @@ steps:
 
 - label: PyTorch Fullgraph
   key: pytorch-fullgraph
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   device: h200_18gb
   source_file_dependencies:
   - vllm/__init__.py

.buildkite/test_areas/quantization.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Quantization
   key: quantization
-  timeout_in_minutes: 90
+  timeout_in_minutes: 60
   source_file_dependencies:
   - csrc/
   - vllm/model_executor/layers/quantization
@@ -23,7 +23,7 @@ steps:
 
 - label: Quantized Fusions
   key: quantized-fusions
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   source_file_dependencies:
   - tests/fusion
   - vllm/model_executor/layers/fusion
@@ -35,7 +35,7 @@ steps:
 
 - label: Quantized MoE Test (B200)
   key: quantized-moe-test-b200
-  timeout_in_minutes: 60
+  timeout_in_minutes: 120
   working_dir: "/vllm-workspace/"
   device: b200-k8s
   source_file_dependencies:
@@ -53,7 +53,7 @@ steps:
 
 - label: Quantized Models Test
   key: quantized-models-test
-  timeout_in_minutes: 60
+  timeout_in_minutes: 50
   source_file_dependencies:
   - vllm/model_executor/layers/quantization
   - tests/models/quantization

.buildkite/test_areas/rust_frontend.yaml

@@ -3,7 +3,7 @@ depends_on:
   - image-build
 steps:
 - label: Rust Frontend OpenAI Coverage
-  timeout_in_minutes: 90
+  timeout_in_minutes: 30
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -38,7 +38,7 @@ steps:
   - 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
+  timeout_in_minutes: 25
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -64,10 +64,10 @@ steps:
   - 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
   # /tokenizer_info is not implemented in the Rust frontend (the CLI flag is accepted as a no-op).
-  - pytest -v -s entrypoints/serve/tokenize/test_tokenization.py -k "not tokenizer_info and not test_tokenize_chat"
+  - pytest -v -s entrypoints/serve/tokenize/test_tokenization.py -k "not tokenizer_info"
 
 - label: Rust Frontend Core Correctness
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -81,7 +81,7 @@ steps:
   - pytest -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
 - label: Rust Frontend Tool Use
-  timeout_in_minutes: 60
+  timeout_in_minutes: 25
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - rust/
@@ -95,7 +95,7 @@ steps:
   - 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
+  timeout_in_minutes: 25
   num_devices: 4
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:

.buildkite/test_areas/rust_frontend_cargo.yaml

@@ -4,7 +4,7 @@ steps:
 - label: Rust Frontend Cargo Style + Clippy
   key: rust-frontend-cargo-style-clippy
   depends_on: []
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   device: cpu-medium
   no_plugin: true
   source_file_dependencies:
@@ -18,7 +18,7 @@ steps:
 - label: Rust Frontend Cargo Tests
   key: rust-frontend-cargo-tests
   depends_on: []
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   device: cpu-medium
   no_plugin: true
   source_file_dependencies:

.buildkite/test_areas/samplers.yaml

@@ -5,7 +5,7 @@ steps:
 - label: Samplers Test
   device: h200_35gb
   key: samplers-test
-  timeout_in_minutes: 75
+  timeout_in_minutes: 40
   source_file_dependencies:
   - vllm/model_executor/layers
   - vllm/sampling_metadata.py
@@ -19,7 +19,7 @@ steps:
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
   mirror:
     amd:
-      device: mi250_1
+      device: mi325_1
       depends_on:
       - image-build-amd
       commands:

.buildkite/test_areas/spec_decode.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Spec Decode Eagle
   key: spec-decode-eagle
-  timeout_in_minutes: 30
+  timeout_in_minutes: 25
   device: h200_18gb
   source_file_dependencies:
     - vllm/v1/spec_decode/
@@ -15,7 +15,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 45
+      timeout_in_minutes: 60
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -29,7 +29,7 @@ steps:
 
 - label: Spec Decode Eagle Nightly B200
   key: spec-decode-eagle-nightly-b200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 25
   device: b200-k8s
   optional: true
   source_file_dependencies:
@@ -41,7 +41,7 @@ steps:
 
 - label: Spec Decode Speculators + MTP
   key: spec-decode-speculators-mtp
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
     - vllm/v1/spec_decode/
@@ -82,7 +82,7 @@ steps:
   
 - label: Spec Decode Ngram + Suffix
   key: spec-decode-ngram-suffix
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
     - vllm/v1/spec_decode/
@@ -93,7 +93,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 65
+      timeout_in_minutes: 55
       # TODO(akaratza): Test after Torch >= 2.12 bump
       soft_fail: true
       depends_on:
@@ -109,7 +109,7 @@ steps:
 
 - label: Spec Decode Draft Model
   key: spec-decode-draft-model
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
     - vllm/v1/spec_decode/
@@ -120,7 +120,7 @@ steps:
   mirror:
     amd:
       device: mi325_1
-      timeout_in_minutes: 50
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -134,7 +134,7 @@ steps:
 
 - label: Spec Decode Draft Model Nightly B200
   key: spec-decode-draft-model-nightly-b200
-  timeout_in_minutes: 30
+  timeout_in_minutes: 40
   device: b200-k8s
   optional: true
   source_file_dependencies:
@@ -146,7 +146,7 @@ steps:
 
 - label: Speculators Correctness
   key: speculators-correctness
-  timeout_in_minutes: 60
+  timeout_in_minutes: 30
   device: h100
   optional: true
   num_devices: 1
@@ -159,7 +159,7 @@ steps:
     - pytest -v -s v1/spec_decode/test_speculators_correctness.py -m slow_test
 
 - label: Spec Decode MTP hybrid (B200)
-  timeout_in_minutes: 30
+  timeout_in_minutes: 20
   device: b200-k8s
   optional: true
   source_file_dependencies:

.buildkite/test_areas/weight_loading.yaml

@@ -4,7 +4,7 @@ depends_on:
 steps:
 - label: Weight Loading Multiple GPU  # 33min
   key: weight-loading-multiple-gpu
-  timeout_in_minutes: 45
+  timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   optional: true

.github/workflows/pre-commit.yml

@@ -28,7 +28,8 @@ jobs:
             pull_number: context.payload.pull_request.number,
           });
 
-          const hasReadyLabel = pr.labels.some(l => l.name === 'ready');
+          const readyLabels = ['ready', 'ready-run-all-tests'];
+          const hasReadyLabel = pr.labels.some(l => readyLabels.includes(l.name));
           const hasVerifiedLabel = pr.labels.some(l => l.name === 'verified');
 
           const { data: mergedPRs } = await github.rest.search.issuesAndPullRequests({
@@ -40,7 +41,7 @@ jobs:
           if (hasReadyLabel || hasVerifiedLabel || mergedCount >= 4) {
             core.info(`Check passed: verified label=${hasVerifiedLabel}, ready label=${hasReadyLabel}, 4+ merged PRs=${mergedCount >= 4}`);
           } else {
-            core.setFailed(`PR must have the 'verified' or 'ready' (which also triggers tests) label or the author must have at least 4 merged PRs (found ${mergedCount}).`);
+            core.setFailed(`PR must have the 'verified', 'ready', or 'ready-run-all-tests' label (the ready labels also trigger tests) or the author must have at least 4 merged PRs (found ${mergedCount}).`);
           }
 
   pre-commit:

CMakeLists.txt

@@ -70,6 +70,15 @@ endif()
 #
 set(TORCH_SUPPORTED_VERSION_CUDA "2.11.0")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.11.0")
+# TORCH_NIGHTLY=1 builds run against unpinned nightly wheels, so the supported-
+# version check would always warn. Only treat it as a nightly build when the
+# value is exactly "1" (the bootstrap exports TORCH_NIGHTLY=0 by default, which
+# must NOT suppress the warning for normal builds).
+if (DEFINED ENV{TORCH_NIGHTLY} AND "$ENV{TORCH_NIGHTLY}" STREQUAL "1")
+  set(TORCH_NIGHTLY_BUILD TRUE)
+else()
+  set(TORCH_NIGHTLY_BUILD FALSE)
+endif()
 
 #
 # Try to find python package with an executable that exactly matches
@@ -175,7 +184,7 @@ endif()
 if (NOT HIP_FOUND AND NOT PYTORCH_FOUND_HIP AND CUDA_FOUND)
   set(VLLM_GPU_LANG "CUDA")
 
-  if (NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_CUDA})
+  if (NOT TORCH_NIGHTLY_BUILD AND NOT Torch_VERSION VERSION_EQUAL ${TORCH_SUPPORTED_VERSION_CUDA})
     message(WARNING "Pytorch version ${TORCH_SUPPORTED_VERSION_CUDA} "
       "expected for CUDA build, saw ${Torch_VERSION} instead.")
   endif()
@@ -188,7 +197,7 @@ elseif(HIP_FOUND OR PYTORCH_FOUND_HIP)
   enable_language(HIP)
 
   # ROCm 5.X and 6.X
-  if (ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
+  if (NOT TORCH_NIGHTLY_BUILD AND ROCM_VERSION_DEV_MAJOR GREATER_EQUAL 5 AND
       Torch_VERSION VERSION_LESS ${TORCH_SUPPORTED_VERSION_ROCM})
     message(WARNING "Pytorch version >= ${TORCH_SUPPORTED_VERSION_ROCM} "
       "expected for ROCm build, saw ${Torch_VERSION} instead.")
@@ -381,6 +390,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
     "csrc/libtorch_stable/cuda_view.cu"
     "csrc/libtorch_stable/cuda_utils_kernels.cu"
     "csrc/libtorch_stable/activation_kernels.cu"
+    "csrc/libtorch_stable/ngram_embedding_kernels.cu"
     "csrc/libtorch_stable/quantization/activation_kernels.cu"
     "csrc/libtorch_stable/quantization/w8a8/int8/scaled_quant.cu"
     "csrc/libtorch_stable/quantization/w8a8/fp8/common.cu"

benchmarks/kernels/benchmark_flydsl_moe_w4a16.py

@@ -17,7 +17,7 @@ from vllm.model_executor.layers.fused_moe.fused_flydsl_moe import fused_flydsl_m
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe import (  # noqa: E501
     compressed_tensors_moe_w4a16_flydsl,
 )
-from vllm.platforms import current_platform
+from vllm.utils.platform_utils import get_device_name_as_file_name
 
 RoutingBuffers = tuple[
     torch.Tensor,  # sorted_token_ids
@@ -259,7 +259,7 @@ def tune_flydsl_moe_w4a16(
                             )
                             us_best = us
                             tuned_config[str(num_tokens)] = tile_config
-            device_name = current_platform.get_device_name().replace(" ", "_")
+            device_name = get_device_name_as_file_name()
             tuned_config_file_name = (
                 f"E={num_experts},N={inter_dim},device_name={device_name},"
                 f"dtype=int4_w4a16,backend=flydsl.json"

benchmarks/kernels/benchmark_relu_squared.py

@@ -0,0 +1,108 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Benchmark ReLUSquaredActivation: custom CUDA kernel vs forward_native, both
+# eager and under torch.compile (Inductor fuses relu+square into one kernel).
+
+import itertools
+
+import torch
+import torch.nn.functional as F
+
+import vllm.model_executor.layers.activation  # noqa: F401
+from vllm.benchmarks.lib.utils import default_vllm_config
+from vllm.triton_utils import triton
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, set_random_seed
+
+# Capped so the largest tensor stays under 2**31 elements: the shared activation
+# kernel computes the per-token pointer offset (blockIdx.x * d) in 32-bit, which
+# overflows for tensors with >2**32 elements. Realistic token counts are well
+# below this; the kernel-vs-native gap is already clear at these sizes.
+batch_size_range = [1, 16, 128]
+seq_len_range = [1, 16, 64, 1024]
+intermediate_size = [3072, 9728, 12288]
+configs = list(itertools.product(batch_size_range, seq_len_range, intermediate_size))
+
+
+@default_vllm_config()
+def benchmark_relu_squared(
+    batch_size: int,
+    seq_len: int,
+    intermediate_size: int,
+    provider: str,
+    dtype: torch.dtype,
+):
+    device = "cuda"
+    num_tokens = batch_size * seq_len
+    set_random_seed(42)
+    torch.set_default_device(device)
+
+    x = torch.randn(num_tokens, intermediate_size, dtype=dtype, device=device)
+    out = torch.empty_like(x)
+
+    def native(x: torch.Tensor) -> torch.Tensor:
+        return torch.square(F.relu(x))
+
+    # Verify the custom kernel matches the native implementation before timing.
+    ref = native(x)
+    torch.ops._C.relu_squared(out, x)
+    torch.testing.assert_close(out, ref)
+
+    if provider == "custom":
+        # Custom CUDA kernel — single fused kernel.
+        fn = lambda: torch.ops._C.relu_squared(out, x)
+    elif provider == "native":
+        # forward_native, eager — relu and square as separate ops.
+        fn = lambda: native(x)
+    elif provider == "native_compiled":
+        # forward_native under torch.compile — Inductor fuses relu+square.
+        # This is the real production baseline (custom ops are off when
+        # Inductor is enabled), so it is the comparison reviewers care about.
+        compiled = torch.compile(native)
+        compiled(x)  # warm up / trigger compilation before timing
+        fn = lambda: compiled(x)
+
+    ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
+        fn, quantiles=[0.5, 0.2, 0.8]
+    )
+    return ms, max_ms, min_ms
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="Benchmark ReLUSquaredActivation: custom kernel vs native."
+    )
+    parser.add_argument(
+        "--dtype",
+        type=str,
+        choices=["half", "bfloat16", "float"],
+        default="bfloat16",
+    )
+    args = parser.parse_args()
+
+    dtype = STR_DTYPE_TO_TORCH_DTYPE[args.dtype]
+
+    perf_report = triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["batch_size", "seq_len", "intermediate_size"],
+            x_vals=configs,
+            line_arg="provider",
+            line_vals=["custom", "native_compiled", "native"],
+            line_names=[
+                "Custom Kernel",
+                "Native (torch.compile)",
+                "Native (eager)",
+            ],
+            styles=[("blue", "-"), ("green", "-"), ("red", "-")],
+            ylabel="ms",
+            plot_name="relu_squared-eager-performance",
+            args={},
+        )
+    )
+
+    perf_report(
+        lambda batch_size, seq_len, intermediate_size, provider: benchmark_relu_squared(
+            batch_size, seq_len, intermediate_size, provider, dtype
+        )
+    ).run(print_data=True)

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -19,6 +19,7 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
 from vllm.utils.argparse_utils import FlexibleArgumentParser
+from vllm.utils.platform_utils import get_device_name_as_file_name
 
 mp.set_start_method("spawn", force=True)
 
@@ -264,7 +265,7 @@ def save_configs(
     input_type="fp8",
 ) -> None:
     os.makedirs(save_path, exist_ok=True)
-    device_name = current_platform.get_device_name().replace(" ", "_")
+    device_name = get_device_name_as_file_name()
     json_file_name = (
         f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,"
         f"block_shape=[{block_n},{block_k}].json"

build_vllm_ppc64le.sh

@@ -0,0 +1,241 @@
+#!/bin/bash
+set -eoux pipefail
+
+########################################
+# Resolve repo root (IMPORTANT)
+########################################
+REPO_ROOT="$(pwd)"
+
+cd "$REPO_ROOT"
+
+########################################
+# DevPI configuration
+########################################
+
+IBM_DEVPI_URL=${IBM_DEVPI_URL:-"https://wheels.developerfirst.ibm.com/ppc64le/linux/+simple/"}
+RHOAI_INDEX_URL=${RHOAI_INDEX_URL:-"https://console.redhat.com/api/pypi/public-rhai/rhoai/3.4/cpu-ubi9/simple/"}
+
+########################################
+# wheel dir
+########################################
+
+WHEEL_DIR=${WHEEL_DIR:-"/tmp/wheels"}
+mkdir -p "$WHEEL_DIR"
+
+########################################
+# Helpers
+########################################
+try_install_from_devpi() {
+    local pkg=$1
+    uv pip install \
+        --extra-index-url "${IBM_DEVPI_URL}" \
+        --index-strategy unsafe-best-match \
+        --no-build-isolation \
+        "${pkg}"
+}
+
+########################################
+# Package Versions
+########################################
+cd "$REPO_ROOT"
+TORCH_VERSION=${TORCH_VERSION:-$(grep -E '^torch==.+==\s*"ppc64le"' requirements/cpu.txt | grep -Eo '\b[0-9\.]+\b' || true)}
+TORCH_VERSION=${TORCH_VERSION:-2.11.0}
+
+TORCHVISION_VERSION=${TORCHVISION_VERSION:-0.26.0}
+TORCHAUDIO_VERSION=${TORCHAUDIO_VERSION:-${TORCH_VERSION}}
+
+export TORCH_VERSION
+export TORCHVISION_VERSION
+export TORCHAUDIO_VERSION
+export OPENCV_VERSION=${OPENCV_VERSION:-4.13.0.92}
+export XGRAMMAR_VERSION=${XGRAMMAR_VERSION:-0.2.1}
+
+########################################
+# install system dependencies
+########################################
+
+rpm -ivh https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm || true
+
+microdnf install -y \
+    python3.12 python3.12-devel python3.12-pip gcc \
+    git jq gcc-toolset-14 gcc-toolset-14-libatomic-devel \
+    automake libtool clang-devel openssl-devel \
+    harfbuzz-devel kmod lcms2-devel libimagequant-devel libjpeg-turbo-devel \
+    llvm15-devel libraqm-devel libtiff-devel libwebp-devel libxcb-devel \
+    ninja-build openjpeg2-devel pkgconfig \
+    tcl-devel tk-devel xsimd-devel zeromq-devel zlib-devel patchelf file openblas openblas-devel protobuf numactl numactl-devel openmpi openmpi-devel
+    
+rpm -ivh --nodeps \
+    https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-lite-devel-3.14.0-17.el9.ppc64le.rpm
+
+rpm -ivh --nodeps \
+    https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-devel-3.14.0-17.el9.ppc64le.rpm
+
+rpm -ivh --nodeps \
+    https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-compiler-3.14.0-17.el9.ppc64le.rpm
+
+########################################
+# Python 3.12 virtual environment
+########################################
+
+python3.12 -m venv /opt/vllm
+source /opt/vllm/bin/activate
+
+export PATH=/opt/vllm/bin:$PATH
+
+python --version
+
+########################################
+# install build tools (stable uv)
+########################################
+
+pip install -U pip setuptools-rust
+pip install uv
+pip install "setuptools<70" build wheel cmake auditwheel
+uv pip install "setuptools<70" cython meson-python pybind11 "sympy>=1.13.3" --no-build-isolation
+
+########################################
+# Rust
+########################################
+
+curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+source /root/.cargo/env
+
+########################################
+# Compiler env
+########################################
+
+source /opt/rh/gcc-toolset-14/enable
+
+export PATH=/usr/lib64/llvm15/bin:$PATH
+export LLVM_CONFIG=/usr/lib64/llvm15/bin/llvm-config
+export CMAKE_ARGS="-DPython3_EXECUTABLE=python"
+
+export MAX_JOBS=${MAX_JOBS:-$(nproc)}
+export GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
+
+########################################
+# Install Packages From Devpi
+########################################
+uv pip install numpy==2.3.5 pillow==12.2.0 --extra-index-url "$IBM_DEVPI_URL"
+try_install_from_devpi "opencv-python-headless==${OPENCV_VERSION}"
+try_install_from_devpi "torch==${TORCH_VERSION}"
+try_install_from_devpi "torchvision==${TORCHVISION_VERSION}"
+
+########################################
+# torch audio
+########################################
+
+TEMP_BUILD_DIR=$(mktemp -d)
+cd "${TEMP_BUILD_DIR}"
+export BUILD_SOX=1 BUILD_KALDI=1 BUILD_RNNT=1 USE_FFMPEG=0 USE_ROCM=0 USE_CUDA=0
+export TORCHAUDIO_TEST_ALLOW_SKIP_IF_NO_FFMPEG=1
+git clone --recursive https://github.com/pytorch/audio.git -b v${TORCHAUDIO_VERSION}
+cd audio
+#patching 
+sed -i '
+s|_CSRC_DIR / "_torchaudio.cpp"|str(_CSRC_DIR / "_torchaudio.cpp")|;
+s|_CSRC_DIR / "utils.cpp"|str(_CSRC_DIR / "utils.cpp")|;
+s|sources=\[_CSRC_DIR / s for s in sources\]|sources=[str(_CSRC_DIR / s) for s in sources]|;
+' tools/setup_helpers/extension.py
+MAX_JOBS=${MAX_JOBS:-$(nproc)} \
+BUILD_VERSION=${TORCHAUDIO_VERSION} \
+uv build --wheel --out-dir "${WHEEL_DIR}" --no-build-isolation
+uv pip install "${WHEEL_DIR}"/torchaudio*.whl
+cd "${REPO_ROOT}"
+rm -rf "${TEMP_BUILD_DIR}"
+
+########################################
+# Xgrammar
+########################################
+uv pip install \
+   "scikit-build-core==0.11.6" \
+   "pyproject-metadata<0.8" \
+    pathspec \
+    packaging \
+    distro \
+   "setuptools<70" \
+    setuptools_scm \
+    cmake \
+    ninja \
+    pybind11 \
+    nanobind
+uv pip install apache-tvm-ffi==0.1.12 \
+  --no-build-isolation \
+  --no-cache
+
+TEMP_BUILD_DIR=$(mktemp -d)
+
+pushd "${TEMP_BUILD_DIR}"
+
+export CFLAGS="-fno-lto -mcpu=power9"
+export CXXFLAGS="-fno-lto -mcpu=power9"
+export LDFLAGS="-fno-lto"
+export PATH=/opt/vllm/bin:$PATH
+
+export Python_EXECUTABLE=/opt/vllm/bin/python3
+export Python3_EXECUTABLE=/opt/vllm/bin/python3
+export PYTHON_EXECUTABLE=/opt/vllm/bin/python3
+
+export Python_ROOT_DIR=/opt/vllm
+export Python3_ROOT_DIR=/opt/vllm
+
+git clone \
+    --recursive \
+    https://github.com/mlc-ai/xgrammar \
+    -b "v${XGRAMMAR_VERSION}"
+
+cd xgrammar
+
+cp cmake/config.cmake .
+export PYTHONPATH=/opt/vllm/lib64/python3.12/site-packages:/opt/vllm/lib/python3.12/site-packages:${PYTHONPATH:-}
+
+uv build \
+    --wheel \
+    --out-dir "${WHEEL_DIR}" \
+    --no-build-isolation
+
+uv pip install "${WHEEL_DIR}"/xgrammar*.whl -v
+
+popd
+
+rm -rf "${TEMP_BUILD_DIR}"
+cd "${REPO_ROOT}"
+
+########################################
+# RHOAI Binary Downloads
+########################################
+pip download \
+    --index-url "${RHOAI_INDEX_URL}" \
+    --only-binary=:all: \
+    --no-deps \
+    llvmlite==0.47.0 \
+    -d "${WHEEL_DIR}"
+
+pip download \
+    --index-url "${RHOAI_INDEX_URL}" \
+    --only-binary=:all: \
+    --no-deps \
+    Numba==0.65.0 \
+    -d "${WHEEL_DIR}"
+
+########################################
+# install built wheels
+########################################
+uv pip install setuptools_scm maturin setuptools-rust ninja scikit-build-core pybind11 nanobind \
+    --no-build-isolation
+uv pip install "${WHEEL_DIR}"/*.whl
+
+########################################
+# install remaining deps
+########################################
+
+sed -i.bak -e 's/.*torch.*//g' pyproject.toml requirements/*.txt
+
+uv pip install "setuptools>=78.1.1" --no-build-isolation
+
+export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:/usr/local/lib64/pkgconfig:/usr/lib64/pkgconfig
+
+uv pip install -r requirements/common.txt \
+               -r requirements/cpu.txt \
+               -r requirements/build/cpu.txt --index-strategy unsafe-best-match

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA
-        GIT_TAG a6ec2ba7bd0a7dff98b3f4d3e6b52b159c48d78b
+        GIT_TAG b70aff3d110a2b1a037e62eac295166b5143643a
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""

cmake/external_projects/fmha_sm100.cmake

@@ -17,7 +17,7 @@ else()
   FetchContent_Declare(
     fmha_sm100
     GIT_REPOSITORY https://github.com/vllm-project/MSA.git
-    GIT_TAG fee783153f3efe57e3e933c5cb7e267a7cebcfb5
+    GIT_TAG 2e63ec37a0fc29bc20f39cd1a52e0f5affc33a73
     GIT_PROGRESS TRUE
     CONFIGURE_COMMAND ""
     BUILD_COMMAND ""

cmake/external_projects/vllm_flash_attn.cmake

@@ -39,7 +39,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 2c839c33742309ec41e620bf837495ec9926c56e
+          GIT_TAG bb9a72e7dde0dc614ffc663e052cd6a19ce73a42
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

csrc/libtorch_stable/activation_kernels.cu

@@ -669,6 +669,14 @@ __device__ __forceinline__ T gelu_quick_kernel(const T& x) {
   return (T)(((float)x) / (1.0f + expf(-1.702f * (float)x)));
 }
 
+template <typename T>
+__device__ __forceinline__ T relu_squared_kernel(const T& x) {
+  // relu(x)^2 — introduced in https://arxiv.org/abs/2109.08668v2
+  const float f = (float)x;
+  const float val = f > 0.0f ? f : 0.0f;
+  return (T)(val * val);
+}
+
 }  // namespace vllm
 
 void gelu_new(torch::stable::Tensor& out,    // [..., d]
@@ -688,3 +696,9 @@ void gelu_quick(torch::stable::Tensor& out,    // [..., d]
 {
   LAUNCH_ACTIVATION_KERNEL(vllm::gelu_quick_kernel);
 }
+
+void relu_squared(torch::stable::Tensor& out,    // [..., d]
+                  torch::stable::Tensor& input)  // [..., d]
+{
+  LAUNCH_ACTIVATION_KERNEL(vllm::relu_squared_kernel);
+}

csrc/libtorch_stable/fp32_router_gemm.cu

@@ -1,13 +1,18 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 //
-// Router GEMM: activation(T) x weight(fp32) -> fp32, H=3072, E=256, M<=32.
+// Router GEMM: activation(T) x weight(fp32) -> fp32, M<=32, for the
+// supported (E, H) pairs listed at the bottom of this file.
 // Supports bf16 or fp32 activation; weight is always fp32.
 // Adapted from dsv3_router_gemm_float_out.cu.
+// (E=256, H=6144) bf16 uses a B300-tuned wide-block geometry; see
+// invokeFp32RouterGemm.
 
 #include <cuda_bf16.h>
 #include <cuda_runtime.h>
 
+#include <type_traits>
+
 // ---------------------------------------------------------------------------
 // Load helpers
 // ---------------------------------------------------------------------------
@@ -73,94 +78,113 @@ __device__ __forceinline__ void load_activation<__nv_bfloat16, 8>(
 // InputT : type of activation (float or __nv_bfloat16)
 // Weight is always fp32; output is always fp32.
 // VPT = 16 / sizeof(InputT):  4 for fp32, 8 for bf16
-template <typename InputT, int kBlockSize, int kNumTokens, int kNumExperts,
-          int kHiddenDim>
-__global__ __launch_bounds__(128, 1) void fp32_router_gemm_kernel(
-    float* out, InputT const* mat_a, float const* mat_b) {
+// Each block computes kEPB expert columns; wider blocks / kEPB > 1 are
+// selected per (shape, M) in invokeFp32RouterGemm (B300-tuned, see below).
+// kTGroups > 1 splits the tokens across groups of kBlockSize threads within
+// the block: all groups scan the same weight K-slices (group 0 misses to
+// DRAM, later groups hit L1) so weight traffic stays 1x, while per-thread
+// accumulator registers drop by kTGroups (at M=16 the 32 fp32 accumulators
+// push the kernel to 128 regs/thread and 1 block/SM).
+template <typename InputT, int kBlockSize, int kNumTokens, int kEPB,
+          int kNumExperts, int kHiddenDim, int kTGroups = 1>
+__global__ __launch_bounds__(
+    kBlockSize* kTGroups, 1) void fp32_router_gemm_kernel(float* out,
+                                                          InputT const* mat_a,
+                                                          float const* mat_b) {
   constexpr int VPT = 16 / sizeof(InputT);
   constexpr int k_elems_per_k_iteration = VPT * kBlockSize;
   constexpr int k_iterations = kHiddenDim / k_elems_per_k_iteration;
+  static_assert(kHiddenDim % k_elems_per_k_iteration == 0);
+  static_assert(kNumTokens % kTGroups == 0);
   constexpr int kWarpSize = 32;
-  constexpr int kNumWarps = kBlockSize / kWarpSize;
+  constexpr int kNumWarps = kBlockSize / kWarpSize;  // per token group
+  constexpr int kMG = kNumTokens / kTGroups;         // tokens per group
 
-  int const n_idx = blockIdx.x;
-  int const tid = threadIdx.x;
+  int const e_base = blockIdx.x * kEPB;
+  int const tid = threadIdx.x % kBlockSize;
+  int const m0 = (threadIdx.x / kBlockSize) * kMG;
   int const warpId = tid / kWarpSize;
   int const laneId = tid % kWarpSize;
 
-  float acc[kNumTokens] = {};
-  __shared__ float sm_reduction[kNumTokens][kNumWarps];
-
-  float const* b_col = mat_b + n_idx * kHiddenDim;
-
-  int k_bases[k_iterations];
-#pragma unroll
-  for (int ki = 0; ki < k_iterations; ki++) {
-    k_bases[ki] = ki * k_elems_per_k_iteration + tid * VPT;
-  }
+  float acc[kMG][kEPB] = {};
+  __shared__ float sm_reduction[kNumTokens][kEPB][kNumWarps];
 
 #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
   cudaGridDependencySynchronize();
+  // Fire the PDL trigger right after our own wait instead of at kernel end:
+  // a gridsync-ing consumer is unaffected (its wait always targets full grid
+  // completion), while a consumer that reads none of our outputs (e.g. the
+  // NVFP4 activation quant, which reads the same hidden_states) can launch
+  // now and fully overlap this kernel's body.
+  cudaTriggerProgrammaticLaunchCompletion();
 #endif
 
+#pragma unroll
   for (int ki = 0; ki < k_iterations; ki++) {
-    int const k_base = k_bases[ki];
+    int const k_base = ki * k_elems_per_k_iteration + tid * VPT;
 
-    float b_float[VPT];
-    load_weight<VPT>(b_col + k_base, b_float);
+    float b_float[kEPB][VPT];
+#pragma unroll
+    for (int e = 0; e < kEPB; e++) {
+      load_weight<VPT>(mat_b + (e_base + e) * kHiddenDim + k_base, b_float[e]);
+    }
 
 #pragma unroll
-    for (int m_idx = 0; m_idx < kNumTokens; m_idx++) {
+    for (int m_idx = 0; m_idx < kMG; m_idx++) {
       float a_float[VPT];
-      load_activation<InputT, VPT>(mat_a + m_idx * kHiddenDim + k_base,
-                                   a_float);
+      load_activation<InputT, VPT>(
+          mat_a + (size_t)(m0 + m_idx) * kHiddenDim + k_base, a_float);
 #pragma unroll
-      for (int k = 0; k < VPT; k++) {
-        acc[m_idx] += a_float[k] * b_float[k];
+      for (int e = 0; e < kEPB; e++) {
+#pragma unroll
+        for (int k = 0; k < VPT; k++) {
+          acc[m_idx][e] += a_float[k] * b_float[e][k];
+        }
       }
     }
   }
 
   // Warp-level butterfly reduction
 #pragma unroll
-  for (int m = 0; m < kNumTokens; m++) {
-    float sum = acc[m];
-    sum += __shfl_xor_sync(0xffffffff, sum, 16);
-    sum += __shfl_xor_sync(0xffffffff, sum, 8);
-    sum += __shfl_xor_sync(0xffffffff, sum, 4);
-    sum += __shfl_xor_sync(0xffffffff, sum, 2);
-    sum += __shfl_xor_sync(0xffffffff, sum, 1);
-    if (laneId == 0) sm_reduction[m][warpId] = sum;
+  for (int m = 0; m < kMG; m++) {
+#pragma unroll
+    for (int e = 0; e < kEPB; e++) {
+      float sum = acc[m][e];
+      sum += __shfl_xor_sync(0xffffffff, sum, 16);
+      sum += __shfl_xor_sync(0xffffffff, sum, 8);
+      sum += __shfl_xor_sync(0xffffffff, sum, 4);
+      sum += __shfl_xor_sync(0xffffffff, sum, 2);
+      sum += __shfl_xor_sync(0xffffffff, sum, 1);
+      if (laneId == 0) sm_reduction[m0 + m][e][warpId] = sum;
+    }
   }
 
   __syncthreads();
 
-  if (tid == 0) {
+  // Parallel finalize: one thread per (m, e) output.
+  for (int idx = threadIdx.x; idx < kNumTokens * kEPB;
+       idx += kBlockSize * kTGroups) {
+    int const m = idx / kEPB;
+    int const e = idx % kEPB;
+    float final_sum = 0.0f;
 #pragma unroll
-    for (int m = 0; m < kNumTokens; m++) {
-      float final_sum = 0.0f;
-#pragma unroll
-      for (int w = 0; w < kNumWarps; w++) final_sum += sm_reduction[m][w];
-      out[m * kNumExperts + n_idx] = final_sum;
-    }
+    for (int w = 0; w < kNumWarps; w++) final_sum += sm_reduction[m][e][w];
+    out[m * kNumExperts + e_base + e] = final_sum;
   }
-
-#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 900)
-  cudaTriggerProgrammaticLaunchCompletion();
-#endif
 }
 
 // ---------------------------------------------------------------------------
 // Launcher
 // ---------------------------------------------------------------------------
 
-template <typename InputT, int kNumTokens, int kNumExperts, int kHiddenDim>
-void invokeFp32RouterGemm(float* output, InputT const* mat_a,
-                          float const* mat_b, cudaStream_t stream) {
-  constexpr int kBlockSize = 128;
+template <typename InputT, int kBlockSize, int kEPB, int kNumTokens,
+          int kNumExperts, int kHiddenDim, int kTGroups = 1>
+static void launchFp32RouterGemm(float* output, InputT const* mat_a,
+                                 float const* mat_b, cudaStream_t stream) {
+  static_assert(kNumExperts % kEPB == 0);
   cudaLaunchConfig_t config;
-  config.gridDim = kNumExperts;
-  config.blockDim = kBlockSize;
+  config.gridDim = kNumExperts / kEPB;
+  config.blockDim = kBlockSize * kTGroups;
   config.dynamicSmemBytes = 0;
   config.stream = stream;
   cudaLaunchAttribute attrs[1];
@@ -168,15 +192,112 @@ void invokeFp32RouterGemm(float* output, InputT const* mat_a,
   attrs[0].val.programmaticStreamSerializationAllowed = 1;
   config.numAttrs = 1;
   config.attrs = attrs;
-  cudaLaunchKernelEx(&config,
-                     fp32_router_gemm_kernel<InputT, kBlockSize, kNumTokens,
-                                             kNumExperts, kHiddenDim>,
-                     output, mat_a, mat_b);
+  cudaLaunchKernelEx(
+      &config,
+      fp32_router_gemm_kernel<InputT, kBlockSize, kNumTokens, kEPB, kNumExperts,
+                              kHiddenDim, kTGroups>,
+      output, mat_a, mat_b);
+}
+
+static bool isBlackwellFamily() {
+  static int sm = []() {
+    int dev = 0, major = 0, minor = 0;
+    cudaGetDevice(&dev);
+    cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, dev);
+    cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, dev);
+    return major * 10 + minor;
+  }();
+  return sm >= 100;
+}
+
+template <typename InputT, int kNumTokens, int kNumExperts, int kHiddenDim>
+void invokeFp32RouterGemm(float* output, InputT const* mat_a,
+                          float const* mat_b, cudaStream_t stream) {
+  // Geometry tuned on B300 per supported shape, bf16 activation, under a
+  // production-fidelity harness (CUDA-graph replay, per-layer cold weights).
+  // GLM-5.2 (E=256, H=6144):
+  //   M <= 4        : BS=768, EPB=1 (2.7us vs cast+cuBLAS 8.1us at M=1)
+  //   M in [5, 15]
+  //   or odd        : BS=384, EPB=2 (crossover vs BS=768 measured in (4, 8))
+  //   M >= 16, even : BS=192, EPB=2, 2 token groups (M=16 4.79us vs 5.04,
+  //                   M=24 5.71 vs 6.38, M=32 6.79 vs 7.72; M=12 loses at
+  //                   0.97x, so the boundary is 16).
+  // Only enabled on the Blackwell family where it was validated; Hopper and
+  // other shapes / fp32 activation keep the legacy geometry.
+  if constexpr (std::is_same_v<InputT, __nv_bfloat16> && kNumExperts == 256 &&
+                kHiddenDim == 6144) {
+    if (!isBlackwellFamily()) {
+      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
+          output, mat_a, mat_b, stream);
+      return;
+    }
+    if constexpr (kNumTokens <= 4) {
+      launchFp32RouterGemm<InputT, 768, 1, kNumTokens, kNumExperts, kHiddenDim>(
+          output, mat_a, mat_b, stream);
+    } else if constexpr (kNumTokens >= 16 && kNumTokens % 2 == 0) {
+      launchFp32RouterGemm<InputT, 192, 2, kNumTokens, kNumExperts, kHiddenDim,
+                           2>(output, mat_a, mat_b, stream);
+    } else {
+      launchFp32RouterGemm<InputT, 384, 2, kNumTokens, kNumExperts, kHiddenDim>(
+          output, mat_a, mat_b, stream);
+    }
+  } else if constexpr (std::is_same_v<InputT, __nv_bfloat16> &&
+                       kNumExperts == 128 && kHiddenDim == 6144) {
+    // MiniMax-M3. Legacy 128/1 only fills 128 blocks and pays the same
+    // accumulator register cliffs; B300 sweep:
+    //   even M in [6, 10] : BS=384, EPB=1, 2 token groups (1.26-1.43x)
+    //   even M >= 12      : BS=192, EPB=1, 2 token groups (1.59-1.66x at
+    //                       M >= 18; re-measured on B300+B200: 192 also wins
+    //                       M=12/14 by 5-11%% on both, ties 384 at 16)
+    //   M <= 5 / odd      : BS=384, EPB=1 (1.03-1.19x)
+    if (!isBlackwellFamily()) {
+      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
+          output, mat_a, mat_b, stream);
+      return;
+    }
+    if constexpr (kNumTokens >= 12 && kNumTokens % 2 == 0) {
+      launchFp32RouterGemm<InputT, 192, 1, kNumTokens, kNumExperts, kHiddenDim,
+                           2>(output, mat_a, mat_b, stream);
+    } else if constexpr (kNumTokens >= 6 && kNumTokens % 2 == 0) {
+      launchFp32RouterGemm<InputT, 384, 1, kNumTokens, kNumExperts, kHiddenDim,
+                           2>(output, mat_a, mat_b, stream);
+    } else {
+      launchFp32RouterGemm<InputT, 384, 1, kNumTokens, kNumExperts, kHiddenDim>(
+          output, mat_a, mat_b, stream);
+    }
+  } else if constexpr (std::is_same_v<InputT, __nv_bfloat16> &&
+                       kNumExperts == 256 && kHiddenDim == 3072) {
+    // MiniMax-M2/M2.5. The 3.1MB weight is latency-floor bound at small M
+    // (legacy already optimal); token groups win only at even M >= 8
+    // (1.05-1.17x). EPB crossover measured between 12 and 16.
+    if (!isBlackwellFamily()) {
+      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
+          output, mat_a, mat_b, stream);
+      return;
+    }
+    if constexpr (kNumTokens >= 14 && kNumTokens % 2 == 0) {
+      // M=14 originally measured 0.91x and stayed on legacy; two fresh
+      // sweeps (B300 dev1 + B200) both put 192/2/tg2 ahead by 3.5-4%%.
+      launchFp32RouterGemm<InputT, 192, 2, kNumTokens, kNumExperts, kHiddenDim,
+                           2>(output, mat_a, mat_b, stream);
+    } else if constexpr (kNumTokens >= 8 && kNumTokens <= 12 &&
+                         kNumTokens % 2 == 0) {
+      launchFp32RouterGemm<InputT, 192, 1, kNumTokens, kNumExperts, kHiddenDim,
+                           2>(output, mat_a, mat_b, stream);
+    } else {
+      launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
+          output, mat_a, mat_b, stream);
+    }
+  } else {
+    launchFp32RouterGemm<InputT, 128, 1, kNumTokens, kNumExperts, kHiddenDim>(
+        output, mat_a, mat_b, stream);
+  }
 }
 
 // ---------------------------------------------------------------------------
 // Explicit instantiations: M=1..32, for both input types, for the supported
-// (E, H) pairs:  (256, 3072) [MiniMax-M2/M2.5]  and  (128, 6144) [MiniMax-M3].
+// (E, H) pairs:  (256, 3072) [MiniMax-M2/M2.5],  (128, 6144) [MiniMax-M3]
+// and  (256, 6144) [GLM-5.2].
 // ---------------------------------------------------------------------------
 
 #define INSTANTIATE(T, M, E, H)                                    \
@@ -221,6 +342,8 @@ INSTANTIATE_ALL(float, 256, 3072)
 INSTANTIATE_ALL(__nv_bfloat16, 256, 3072)
 INSTANTIATE_ALL(float, 128, 6144)
 INSTANTIATE_ALL(__nv_bfloat16, 128, 6144)
+INSTANTIATE_ALL(float, 256, 6144)
+INSTANTIATE_ALL(__nv_bfloat16, 256, 6144)
 
 #undef INSTANTIATE_ALL
 #undef INSTANTIATE

csrc/libtorch_stable/fp32_router_gemm_entry.cu

@@ -25,10 +25,12 @@ inline int getSMVersion() {
 static constexpr int FP32_MAX_TOKENS = 32;
 
 // Supported (hidden_dim, num_experts) pairs (must match the instantiations in
-// fp32_router_gemm.cu): (3072, 256) for MiniMax-M2/M2.5, (6144, 128) for M3.
+// fp32_router_gemm.cu): (3072, 256) for MiniMax-M2/M2.5, (6144, 128) for M3,
+// (6144, 256) for GLM-5.2.
 static inline bool fp32_router_gemm_supported(int hidden_dim, int num_experts) {
   return (hidden_dim == 3072 && num_experts == 256) ||
-         (hidden_dim == 6144 && num_experts == 128);
+         (hidden_dim == 6144 && num_experts == 128) ||
+         (hidden_dim == 6144 && num_experts == 256);
 }
 
 // Forward declarations — 4 template params must match fp32_router_gemm.cu
@@ -77,6 +79,9 @@ void dispatchFp32RouterGemm(int num_experts, int hidden_dim, int num_tokens,
   } else if (num_experts == 128 && hidden_dim == 6144) {
     Fp32LoopUnroller<InputT, 128, 6144, 1, FP32_MAX_TOKENS>::unroll(
         num_tokens, output, mat_a, mat_b, stream);
+  } else if (num_experts == 256 && hidden_dim == 6144) {
+    Fp32LoopUnroller<InputT, 256, 6144, 1, FP32_MAX_TOKENS>::unroll(
+        num_tokens, output, mat_a, mat_b, stream);
   } else {
     throw std::invalid_argument(
         "fp32_router_gemm: unsupported (hidden_dim, num_experts) pair");
@@ -111,7 +116,7 @@ void fp32_router_gemm(
   STD_TORCH_CHECK(
       fp32_router_gemm_supported(hidden_dim, num_experts),
       "fp32_router_gemm: supported (hidden_dim, num_experts) pairs are "
-      "(3072, 256) and (6144, 128)");
+      "(3072, 256), (6144, 128) and (6144, 256)");
   STD_TORCH_CHECK(num_tokens <= FP32_MAX_TOKENS,
                   "fp32_router_gemm: num_tokens must be in [0, 32]");
   STD_TORCH_CHECK(

csrc/libtorch_stable/fused_minimax_m3_qknorm_rope_kv_insert_kernel.cu

@@ -39,12 +39,10 @@
  * The IQ/IK warps address the index_q/index_k sub-blocks *inside* qkv at the
  * fixed physical offsets (nq+2*nkv)*128 and (nq+2*nkv+niq)*128.
  *
- * Dense vs sparse is a compile-time choice via the ``kIsSparse``/``kInsertKV``
- * template bools (3 instantiations: dense <false,false>, sparse-profiling
- * <true,false>, sparse-serving <true,true>), so the index slots, the V slots
- * and the cache inserts fold away entirely on paths that don't use them. The
- * dense layer passes no caches/index: norm+RoPE happens in place and the
- * generic ``Attention`` layer owns the cache write.
+ * Dense vs sparse row layout and index-branch processing are separate template
+ * choices. Skip-index-topk reuse layers still have sparse rows and insert main
+ * K/V cache entries, but compile away index_q/index_k work and index-cache
+ * writes.
  *
  * Q/K and (sparse) index_q/index_k are all rewritten in place inside the fused
  * ``qkv`` tensor.  Caches (bf16) are scatter-written by slot.
@@ -223,10 +221,25 @@ __device__ __forceinline__ void storeCacheElems(
     // model dtype directly. FP8 cache dtypes use the conversion path below.
     storeElems<scalar_t>(reinterpret_cast<scalar_t*>(dst), elems);
   } else {
-#pragma unroll
+#ifdef USE_ROCM
+    // Match ROCm's model-dtype materialization before FP8 cache conversion.
+    using Converter = vllm::_typeConvert<scalar_t>;
+    using rounded_t = typename Converter::hip_type;
+    rounded_t rounded[kElemsPerLane];
+  #pragma unroll
+    for (int i = 0; i < kElemsPerLane; i++) {
+      rounded[i] = Converter::convert(elems[i]);
+    }
+  #pragma unroll
+    for (int i = 0; i < kElemsPerLane; i++) {
+      dst[i] = fp8::scaled_convert<cache_t, rounded_t, kv_dt>(rounded[i], 1.0f);
+    }
+#else
+  #pragma unroll
     for (int i = 0; i < kElemsPerLane; i++) {
       dst[i] = fp8::scaled_convert<cache_t, float, kv_dt>(elems[i], 1.0f);
     }
+#endif
   }
 }
 
@@ -262,20 +275,25 @@ __device__ __forceinline__ void storeElemsFp8(
 // Grid: 1D, ceil(num_tokens * slots_per_token / warps_per_block).
 // Each warp = one (token, slot).
 //
-// `kIsSparse` and `kInsertKV` are compile-time template bools, so all the
-// branch decisions that distinguish the dense layer from the sparse layer
-// (index slots, KV/index inserts, V slots) fold away per instantiation.
-// Three instantiations are built: dense <false,false>, sparse-profiling
-// <true,false> and sparse-serving <true,true>.  Slots per token:
+// `kHasIndex`, `kProcessIndex`, and `kInsertKV` are compile-time template
+// bools, so branch decisions that distinguish the dense layer from the sparse
+// layer (index slots, KV/index inserts, V slots) fold away per instantiation.
+// Slots per token:
 //     Q : nq                            (always — norm+RoPE)
 //     K : nkv                           (always — norm+RoPE; +K-cache insert)
 //     V : nkv  only if kInsertKV        (V-cache insert; no warps in dense)
-//     IQ: niq  only if kIsSparse        (norm+RoPE)
-//     IK: 1    only if kIsSparse        (norm+RoPE; +index-cache insert)
+//     IQ: niq  only if kProcessIndex    (norm+RoPE)
+//     IK: 1    only if kProcessIndex    (norm+RoPE; +index-cache insert)
 // cache_t/kv_dt: main attention KV-cache dtype (auto/fp8). out_idx_t/kFp8Idx:
 // indexer index-K cache + index-Q output dtype (scalar_t or e4m3 byte).
+// kHasIndex means the qkv row is laid out as sparse [q|k|v|index_q|index_k].
+// kProcessIndex controls whether this launch actually norms/ropes the index
+// branch and writes index_q/index_k outputs. Skip-index-topk reuse layers keep
+// kHasIndex=true but set kProcessIndex=false.
 template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt,
-          typename out_idx_t, bool kIsSparse, bool kInsertKV, bool kFp8Idx>
+          typename out_idx_t, bool kHasIndex, bool kInsertKV,
+          bool kProcessIndex,
+          bool kFp8Idx>
 __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
     scalar_t* __restrict__ qkv,  // [N, qkv_row] in/out (packs index if sparse)
     scalar_t* __restrict__ q_out,         // [N, nq*128] contiguous, or nullptr
@@ -288,16 +306,15 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
     int64_t const* __restrict__ positions,       // [N] i64
     int64_t const* __restrict__ slot_mapping,    // main K/V slots or nullptr
     int64_t const* __restrict__ index_slot_mapping,  // index K slots/nullptr
-    cache_t* __restrict__ kv_cache,       // [nb,2,bs,nkv,128] or nullptr
+    cache_t* __restrict__ kv_cache,       // [nb,nkv,bs,2*128] or nullptr
     out_idx_t* __restrict__ index_cache,  // [nb*bs, 128]; scalar_t or e4m3 byte
     float const eps, int const rotary_dim, int const num_tokens, int const nq,
     int const nkv, int const niq, int const block_size,
-    // kv_cache strides (in elements) for logical shape [nb, 2, bs, nkv, 128].
-    // The head_dim (last) dim is always innermost-contiguous (stride 1), so the
-    // NHD/HND layout choice is fully captured by these four strides: NHD keeps
-    // s_token < s_head, HND swaps them. dim_base addresses head_dim directly.
-    int64_t const kv_s_block, int64_t const kv_s_kv, int64_t const kv_s_token,
-    int64_t const kv_s_head) {
+    // kv_cache strides (in elements) for logical shape [nb, nkv, bs, 2*128].
+    // The content (last) dim is always innermost-contiguous (stride 1), so the
+    // NHD/HND layout choice is captured by the head/token strides.
+    int64_t const kv_s_block, int64_t const kv_s_head, int64_t const kv_s_token,
+    int64_t const kv_s_dim) {
 #if (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800) && !defined(USE_ROCM)
   // _typeConvert<BFloat16> is unavailable on pre-Ampere; the M3 kernel only
   // runs with bf16/fp16 inputs in practice.  Discard the bf16 body there.
@@ -309,9 +326,12 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
     int const laneId = threadIdx.x % 32;
     int const globalWarpIdx = blockIdx.x * warpsPerBlock + (threadIdx.x / 32);
 
+    static_assert(!kProcessIndex || kHasIndex,
+                  "index processing requires sparse row layout");
+
     // Slot layout (compile-time gated: dense has neither V nor index slots).
     int const v_slots = kInsertKV ? nkv : 0;
-    int const idx_slots = kIsSparse ? niq + 1 : 0;
+    int const idx_slots = kProcessIndex ? niq + 1 : 0;
     int const slots_per_token = nq + nkv + v_slots + idx_slots;
 
     int const tokenIdx = globalWarpIdx / slots_per_token;
@@ -322,14 +342,14 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
     int const k_begin = nq;
     int const v_begin = nq + nkv;             // valid only when kInsertKV
     int const iq_begin = nq + nkv + v_slots;  // index block start
-    int const ik_slot = iq_begin + niq;       // valid only when kIsSparse
+    int const ik_slot = iq_begin + niq;       // valid only when kProcessIndex
 
     bool const isQ = slot < k_begin;
     bool const isK = slot >= k_begin && slot < v_begin;
     bool isV = false;
     if constexpr (kInsertKV) isV = slot >= v_begin && slot < v_begin + nkv;
     bool isIQ = false, isIK = false;
-    if constexpr (kIsSparse) {
+    if constexpr (kProcessIndex) {
       isIQ = slot >= iq_begin && slot < ik_slot;
       isIK = slot == ik_slot;
     }
@@ -337,7 +357,7 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
     int const dim_base = laneId * kElemsPerLane;
     // Physical row width of qkv: the dense layer packs [q|k|v]; the sparse
     // layer additionally packs [index_q (niq heads) | index_k (1 head)].
-    int const qkv_row = (nq + 2 * nkv + (kIsSparse ? (niq + 1) : 0)) * kHeadDim;
+    int const qkv_row = (nq + 2 * nkv + (kHasIndex ? (niq + 1) : 0)) * kHeadDim;
 
     // ── Resolve source pointer + per-branch parameters. ────────────────────
     scalar_t* row_ptr = nullptr;       // in-place output location
@@ -368,10 +388,13 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
       row_ptr = qkv + static_cast<int64_t>(tokenIdx) * qkv_row +
                 (nq + 2 * nkv + ih) * kHeadDim;
       norm_w = iq_norm_w;
-    } else {  // isIK -- single shared index key at (nq+2*nkv+niq)*128.
+    } else if (isIK) {
+      // Single shared index key at (nq+2*nkv+niq)*128.
       row_ptr = qkv + static_cast<int64_t>(tokenIdx) * qkv_row +
                 (nq + 2 * nkv + niq) * kHeadDim;
       norm_w = ik_norm_w;
+    } else {
+      return;
     }
 
     // Store destination.  Q and index_q are gathered into dedicated contiguous
@@ -427,9 +450,12 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
     // ── Cache inserts (sparse serving only). ───────────────────────────────
     if constexpr (kInsertKV) {
       // Guard (not early-return) so every thread reaches the PDL trigger below.
-      int64_t const sm = (isK || isV)
-                             ? slot_mapping[tokenIdx]
-                             : (isIK ? index_slot_mapping[tokenIdx] : -1);
+      int64_t sm = -1;
+      if (isK || isV) {
+        sm = slot_mapping[tokenIdx];
+      } else if constexpr (kProcessIndex) {
+        if (isIK) sm = index_slot_mapping[tokenIdx];
+      }
       if (sm >= 0) {  // skip padded / unscheduled tokens
         if (isIK) {
           if constexpr (kFp8Idx) {
@@ -438,16 +464,16 @@ __global__ void fusedMiniMaxM3QNormRopeKVInsertKernel(
             storeElems<scalar_t>(index_cache + sm * kHeadDim + dim_base, elems);
           }
         } else if (isK || isV) {
-          // kv_cache logical shape [num_blocks, 2, block_size, nkv, head_dim].
+          // kv_cache logical shape [num_blocks, nkv, block_size, 2*head_dim].
           // Paging is logical (block = sm/block_size, token = sm%block_size);
           // the physical NHD/HND layout is honoured via the passed strides.
           int64_t const b = sm / block_size;
           int64_t const t = sm % block_size;
           int const kv = isK ? 0 : 1;
-          int64_t const off =
-              b * kv_s_block + kv * kv_s_kv + t * kv_s_token + head * kv_s_head;
-          storeCacheElems<scalar_t, cache_t, kv_dt>(kv_cache + off + dim_base,
-                                                    elems);
+          int64_t const off = b * kv_s_block + head * kv_s_head +
+                              t * kv_s_token +
+                              (kv * kHeadDim + dim_base) * kv_s_dim;
+          storeCacheElems<scalar_t, cache_t, kv_dt>(kv_cache + off, elems);
         }
       }
     }
@@ -474,14 +500,14 @@ void launchFusedMiniMaxM3(
     int64_t const* index_slot_mapping, cache_t* kv_cache, void* index_cache,
     float const eps, int const rotary_dim, int const num_tokens, int const nq,
     int const nkv, int const niq, int const block_size,
-    int64_t const kv_s_block, int64_t const kv_s_kv, int64_t const kv_s_token,
-    int64_t const kv_s_head, bool const has_index, bool const insert_kv,
-    bool const fp8_idx, cudaStream_t stream) {
+    int64_t const kv_s_block, int64_t const kv_s_head, int64_t const kv_s_token,
+    int64_t const kv_s_dim, bool const has_index, bool const insert_kv,
+    bool const process_index, bool const fp8_idx, cudaStream_t stream) {
   // Index outputs are scalar_t (bf16) or e4m3 bytes (uint8_t); reinterpret the
   // void* pointers per instantiation in the LAUNCH macro.
   // Slot count must match the kernel's compile-time gating.
   int const v_slots = insert_kv ? nkv : 0;
-  int const idx_slots = has_index ? niq + 1 : 0;
+  int const idx_slots = process_index ? niq + 1 : 0;
   int const slots_per_token = nq + nkv + v_slots + idx_slots;
 
   constexpr int kBlockSize = 256;
@@ -508,50 +534,59 @@ void launchFusedMiniMaxM3(
   config.attrs = attrs;
   config.numAttrs = (sm_version >= 90) ? 1 : 0;
 
-  #define LAUNCH(IS_SPARSE, INSERT, FP8, OUT_T)                                \
+  #define LAUNCH(HAS_INDEX, INSERT, PROCESS_INDEX, FP8, OUT_T)                 \
     cudaLaunchKernelEx(                                                        \
         &config,                                                               \
         fusedMiniMaxM3QNormRopeKVInsertKernel<scalar_t, cache_t, kv_dt, OUT_T, \
-                                              IS_SPARSE, INSERT, FP8>,         \
+                                              HAS_INDEX, INSERT,               \
+                                              PROCESS_INDEX, FP8>,             \
         qkv, q_out, reinterpret_cast<OUT_T*>(index_q_out), q_norm_w, k_norm_w, \
         iq_norm_w, ik_norm_w, cos_sin_cache, positions, slot_mapping,          \
         index_slot_mapping, kv_cache, reinterpret_cast<OUT_T*>(index_cache),   \
         eps, rotary_dim, num_tokens, nq, nkv, niq, block_size, kv_s_block,     \
-        kv_s_kv, kv_s_token, kv_s_head)
+        kv_s_head, kv_s_token, kv_s_dim)
 #else
   // ROCm: standard kernel launch syntax (no PDL/stream serialization).
   // clang-format off
-  #define LAUNCH(IS_SPARSE, INSERT, FP8, OUT_T)                              \
-    fusedMiniMaxM3QNormRopeKVInsertKernel<scalar_t, cache_t, kv_dt, OUT_T,   \
-                                          IS_SPARSE, INSERT, FP8>            \
-        <<<grid, kBlockSize, 0, stream>>>(                                   \
-            qkv, q_out, reinterpret_cast<OUT_T*>(index_q_out), q_norm_w,     \
-            k_norm_w, iq_norm_w, ik_norm_w, cos_sin_cache, positions,        \
-            slot_mapping, index_slot_mapping, kv_cache,                      \
-            reinterpret_cast<OUT_T*>(index_cache), eps, rotary_dim,          \
-            num_tokens, nq, nkv, niq, block_size, kv_s_block, kv_s_kv,       \
-            kv_s_token, kv_s_head)
+  #define LAUNCH(HAS_INDEX, INSERT, PROCESS_INDEX, FP8, OUT_T)              \
+    fusedMiniMaxM3QNormRopeKVInsertKernel<                                  \
+        scalar_t, cache_t, kv_dt, OUT_T, HAS_INDEX, INSERT, PROCESS_INDEX,  \
+        FP8><<<grid, kBlockSize, 0, stream>>>(                              \
+        qkv, q_out, reinterpret_cast<OUT_T*>(index_q_out), q_norm_w,        \
+        k_norm_w, iq_norm_w, ik_norm_w, cos_sin_cache, positions,           \
+        slot_mapping, index_slot_mapping, kv_cache,                         \
+        reinterpret_cast<OUT_T*>(index_cache), eps, rotary_dim, num_tokens, \
+        nq, nkv, niq, block_size, kv_s_block, kv_s_head, kv_s_token,         \
+        kv_s_dim)
   // clang-format on
 #endif
 
   if (has_index) {
-    if (insert_kv) {
-      if (fp8_idx) {
-        LAUNCH(true, true, true, uint8_t);  // sparse serving, fp8 index outputs
+    if (!process_index) {
+      if (insert_kv) {
+        LAUNCH(true, true, false, false, scalar_t);
       } else {
-        LAUNCH(true, true, false, scalar_t);  // sparse serving, bf16
+        LAUNCH(true, false, false, false, scalar_t);
+      }
+    } else if (insert_kv) {
+      if (fp8_idx) {
+        LAUNCH(true, true, true, true,
+               uint8_t);  // sparse serving, fp8 index outputs
+      } else {
+        LAUNCH(true, true, true, false, scalar_t);  // sparse serving, bf16
       }
     } else {
       if (fp8_idx) {
-        LAUNCH(true, false, true, uint8_t);  // sparse profiling, fp8 index_q
+        LAUNCH(true, false, true, true,
+               uint8_t);  // sparse profiling, fp8 index_q
       } else {
-        LAUNCH(true, false, false, scalar_t);  // sparse profiling, bf16
+        LAUNCH(true, false, true, false, scalar_t);  // sparse profiling, bf16
       }
     }
   } else {
     // Dense layer: never has an index branch and never inserts here (the
     // generic Attention layer owns the KV insert).
-    LAUNCH(false, false, false, scalar_t);
+    LAUNCH(false, false, false, false, scalar_t);
   }
 #undef LAUNCH
 }
@@ -559,6 +594,7 @@ void launchFusedMiniMaxM3(
 }  // namespace minimax_m3_fused_ops
 }  // namespace vllm
 
+// clang-format off
 #define CALL_FUSED_MINIMAX_M3(_RAW_T, CACHE_T, KV_DTYPE)                       \
   vllm::minimax_m3_fused_ops::launchFusedMiniMaxM3<st, CACHE_T, KV_DTYPE>(     \
       reinterpret_cast<st*>(qkv.data_ptr()),                                   \
@@ -568,24 +604,29 @@ void launchFusedMiniMaxM3(
           : nullptr,                                                           \
       reinterpret_cast<st const*>(q_norm_weight.data_ptr()),                   \
       reinterpret_cast<st const*>(k_norm_weight.data_ptr()),                   \
-      has_index ? reinterpret_cast<st const*>(index_q_norm_weight->data_ptr()) \
-                : nullptr,                                                     \
-      has_index ? reinterpret_cast<st const*>(index_k_norm_weight->data_ptr()) \
-                : nullptr,                                                     \
+      process_index                                                            \
+          ? reinterpret_cast<st const*>(index_q_norm_weight->data_ptr())       \
+          : nullptr,                                                           \
+      process_index                                                            \
+          ? reinterpret_cast<st const*>(index_k_norm_weight->data_ptr())       \
+          : nullptr,                                                           \
       reinterpret_cast<st const*>(cos_sin_cache.data_ptr()),                   \
       reinterpret_cast<int64_t const*>(positions.data_ptr()),                  \
       insert_kv ? reinterpret_cast<int64_t const*>(slot_mapping->data_ptr())   \
                 : nullptr,                                                     \
-      insert_kv ? reinterpret_cast<int64_t const*>(                            \
-                      effective_index_slot_mapping->data_ptr())                \
-                : nullptr,                                                     \
+      (insert_kv && process_index)                                             \
+          ? reinterpret_cast<int64_t const*>(                                  \
+                effective_index_slot_mapping->data_ptr())                      \
+          : nullptr,                                                           \
       insert_kv ? reinterpret_cast<CACHE_T*>(kv_cache->data_ptr()) : nullptr,  \
-      (insert_kv && has_index)                                                 \
+      (insert_kv && process_index)                                             \
           ? reinterpret_cast<void*>(index_cache->data_ptr())                   \
           : nullptr,                                                           \
       static_cast<float>(eps), static_cast<int>(rotary_dim), num_tokens, nq,   \
-      nkv, niq, static_cast<int>(block_size), kv_s_block, kv_s_kv, kv_s_token, \
-      kv_s_head, has_index, insert_kv, fp8_idx, stream)
+      nkv, niq, static_cast<int>(block_size), kv_s_block, kv_s_head,           \
+      kv_s_token, kv_s_dim, has_index, insert_kv, process_index, fp8_idx,       \
+      stream)
+// clang-format on
 
 // ────────────────────────────────────────────────────────────────────────────
 // Torch op wrapper
@@ -602,13 +643,13 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
     int64_t num_index_heads,                                  // niq; 0 => dense
     std::optional<torch::stable::Tensor> slot_mapping,        // [N] i64
     std::optional<torch::stable::Tensor> index_slot_mapping,  // [N] i64
-    std::optional<torch::stable::Tensor> kv_cache,     // [nb,2,bs,nkv,128]
+    std::optional<torch::stable::Tensor> kv_cache,     // [nb,nkv,bs,2*128]
     std::optional<torch::stable::Tensor> index_cache,  // [nb,bs,128]
     int64_t block_size,
     std::optional<torch::stable::Tensor> q_out,  // [N, nq*128] contiguous
     std::optional<torch::stable::Tensor>
         index_q_out,  // [N, niq*128] contiguous
-    const std::string& kv_cache_dtype) {
+    const std::string& kv_cache_dtype, bool skip_index_branch) {
   STD_TORCH_CHECK(qkv.is_cuda() && qkv.is_contiguous(),
                   "qkv must be contiguous CUDA");
   STD_TORCH_CHECK(
@@ -647,6 +688,7 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
   // (1 head)]) right after [q|k|v] in the same row; the dense layer does not.
   bool const has_index = niq > 0;
   bool const insert_kv = kv_cache.has_value();
+  bool const process_index = has_index && !skip_index_branch;
   vllm::Fp8KVCacheDataType const kv_dt =
       vllm::get_fp8_kv_cache_data_type(kv_cache_dtype);
   int const kHeadDim = vllm::minimax_m3_fused_ops::kHeadDim;
@@ -662,7 +704,9 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
   STD_TORCH_CHECK(
       !insert_kv || has_index,
       "insert mode (kv_cache) requires the index branch (sparse layer)");
-  if (has_index) {
+  STD_TORCH_CHECK(has_index || !skip_index_branch,
+                  "skip_index_branch requires sparse qkv rows");
+  if (process_index) {
     STD_TORCH_CHECK(
         index_q_norm_weight.has_value() && index_k_norm_weight.has_value(),
         "index branch requires both index norm weights");
@@ -673,24 +717,26 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
                         index_k_norm_weight->numel() == kHeadDim,
                     "index norm weights must have 128 elements");
   }
-  // kv_cache strides (logical shape [nb, 2, bs, nkv, head_dim]). Read straight
+  // kv_cache strides (logical shape [nb, nkv, bs, 2*head_dim]). Read straight
   // off the tensor so the kernel honours whatever physical layout the attention
-  // backend allocated (NHD: stride order (0,1,2,3,4); HND: (0,1,3,2,4)). No new
+  // backend allocated (NHD: stride order (0,2,1,3); HND: (0,1,2,3)). No new
   // op argument is needed -- the strides ride along with the tensor itself.
-  int64_t kv_s_block = 0, kv_s_kv = 0, kv_s_token = 0, kv_s_head = 0;
+  int64_t kv_s_block = 0, kv_s_head = 0, kv_s_token = 0, kv_s_dim = 0;
   torch::stable::Tensor const* effective_index_slot_mapping = nullptr;
   if (insert_kv) {
     STD_TORCH_CHECK(
         slot_mapping.has_value() && slot_mapping->is_cuda() &&
             slot_mapping->scalar_type() == torch::headeronly::ScalarType::Long,
         "insert mode requires int64 CUDA slot_mapping");
-    STD_TORCH_CHECK(
-        !index_slot_mapping.has_value() ||
-            (index_slot_mapping->is_cuda() &&
-             index_slot_mapping->scalar_type() ==
-                 torch::headeronly::ScalarType::Long &&
-             index_slot_mapping->numel() == slot_mapping->numel()),
-        "index_slot_mapping must be int64 CUDA with slot_mapping length");
+    if (process_index) {
+      STD_TORCH_CHECK(
+          !index_slot_mapping.has_value() ||
+              (index_slot_mapping->is_cuda() &&
+               index_slot_mapping->scalar_type() ==
+                   torch::headeronly::ScalarType::Long &&
+               index_slot_mapping->numel() == slot_mapping->numel()),
+          "index_slot_mapping must be int64 CUDA with slot_mapping length");
+    }
     // Main attention KV cache: auto matches qkv, fp8 uses uint8 storage.
     if (kv_dt == vllm::Fp8KVCacheDataType::kAuto) {
       STD_TORCH_CHECK(kv_cache->scalar_type() == qkv.scalar_type(),
@@ -701,22 +747,26 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
           "fp8 kv_cache must use uint8 storage");
     }
     // Indexer index-K cache: independent dtype -- qkv dtype or fp8 e4m3.
-    STD_TORCH_CHECK(
-        index_cache.has_value() &&
-            (index_cache->scalar_type() == qkv.scalar_type() ||
-             index_cache->scalar_type() ==
-                 torch::headeronly::ScalarType::Float8_e4m3fn),
-        "insert mode requires index_cache matching qkv dtype or fp8 e4m3");
-    STD_TORCH_CHECK(kv_cache->dim() == 5 && kv_cache->stride(4) == 1,
-                    "kv_cache must be [nb,2,bs,nkv,head_dim] with contiguous "
-                    "head_dim (stride(4)==1)");
+    if (process_index) {
+      STD_TORCH_CHECK(
+          index_cache.has_value() &&
+              (index_cache->scalar_type() == qkv.scalar_type() ||
+               index_cache->scalar_type() ==
+                   torch::headeronly::ScalarType::Float8_e4m3fn),
+          "insert mode requires index_cache matching qkv dtype or fp8 e4m3");
+    }
+    STD_TORCH_CHECK(kv_cache->dim() == 4 && kv_cache->stride(3) == 1,
+                    "kv_cache must be [nb,nkv,bs,2*head_dim] with contiguous "
+                    "content dim (stride(3)==1)");
     kv_s_block = kv_cache->stride(0);
-    kv_s_kv = kv_cache->stride(1);
+    kv_s_head = kv_cache->stride(1);
     kv_s_token = kv_cache->stride(2);
-    kv_s_head = kv_cache->stride(3);
-    effective_index_slot_mapping = index_slot_mapping.has_value()
-                                       ? &index_slot_mapping.value()
-                                       : &slot_mapping.value();
+    kv_s_dim = kv_cache->stride(3);
+    if (process_index) {
+      effective_index_slot_mapping = index_slot_mapping.has_value()
+                                         ? &index_slot_mapping.value()
+                                         : &slot_mapping.value();
+    }
   }
   // Optional contiguous gather targets: when given, the normed/roped q (and
   // index_q) are written here instead of in place, so callers avoid a separate
@@ -731,9 +781,8 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
         "q_out must have num_tokens * num_heads * 128 elements");
   }
   if (index_q_out.has_value()) {
-    STD_TORCH_CHECK(
-        has_index,
-        "index_q_out requires the index branch (num_index_heads > 0)");
+    STD_TORCH_CHECK(process_index,
+                    "index_q_out requires index branch processing");
     STD_TORCH_CHECK(
         index_q_out->is_cuda() && index_q_out->is_contiguous() &&
             (index_q_out->scalar_type() == qkv.scalar_type() ||
@@ -750,8 +799,9 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
   // q/k/v + q_out stay qkv dtype. Both index outputs must agree.
   auto const kFp8 = torch::headeronly::ScalarType::Float8_e4m3fn;
   bool const fp8_idx =
-      (index_cache.has_value() && index_cache->scalar_type() == kFp8) ||
-      (index_q_out.has_value() && index_q_out->scalar_type() == kFp8);
+      process_index &&
+      ((index_cache.has_value() && index_cache->scalar_type() == kFp8) ||
+       (index_q_out.has_value() && index_q_out->scalar_type() == kFp8));
   if (fp8_idx) {
     STD_TORCH_CHECK(
         !index_cache.has_value() || index_cache->scalar_type() == kFp8,

csrc/libtorch_stable/moe/moe_align_sum_kernels.cu

@@ -82,6 +82,21 @@ __global__ void batched_moe_align_block_size_kernel(
 }
 }  // namespace batched_moe_align_block_size
 
+template <typename scalar_t>
+__device__ __forceinline__ int get_local_expert_id(
+    size_t idx, const scalar_t* __restrict__ topk_ids,
+    int32_t* __restrict__ expert_map, int32_t num_experts,
+    bool has_expert_map) {
+  int expert_id = topk_ids[idx];
+  if (expert_id >= num_experts || expert_id < 0) {
+    return -1;
+  }
+  if (has_expert_map) {
+    expert_id = expert_map[expert_id];
+  }
+  return expert_id;
+}
+
 template <typename scalar_t>
 __device__ void _moe_align_block_size(
     const scalar_t* __restrict__ topk_ids,
@@ -126,20 +141,15 @@ __device__ void _moe_align_block_size(
   const size_t stride = blockDim.x;
 
   for (size_t i = tid; i < numel; i += stride) {
-    int expert_id = topk_ids[i];
-    if (expert_id >= num_experts) {
-      continue;
+    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
+                                            num_experts, has_expert_map);
+        expert_id != -1) {
+      int warp_idx = expert_id / experts_per_warp;
+      int expert_offset = expert_id % experts_per_warp;
+      int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
+      atomicAdd(&shared_counts[warp_idx * experts_per_warp + expert_offset],
+                mask);
     }
-    if (has_expert_map) {
-      expert_id = expert_map[expert_id];
-      // filter invalid experts
-      if (expert_id == -1) continue;
-    }
-    int warp_idx = expert_id / experts_per_warp;
-    int expert_offset = expert_id % experts_per_warp;
-    int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
-    atomicAdd(&shared_counts[warp_idx * experts_per_warp + expert_offset],
-              mask);
   }
 
   __syncthreads();
@@ -227,14 +237,12 @@ __device__ void _moe_align_block_size_small_batch_expert(
   }
 
   for (size_t i = tid; i < numel; i += stride) {
-    int32_t expert_id = topk_ids[i];
-    if (has_expert_map) {
-      expert_id = expert_map[expert_id];
-      // filter invalid expert
-      if (expert_id == -1) continue;
+    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
+                                            num_experts, has_expert_map);
+        expert_id != -1) {
+      int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
+      tokens_cnts[(tid + 1) * num_experts + expert_id] += mask;
     }
-    int mask = token_mask == nullptr ? 1 : token_mask[i / topk_num];
-    tokens_cnts[(tid + 1) * num_experts + expert_id] += mask;
   }
 
   __syncthreads();
@@ -276,18 +284,16 @@ __device__ void _moe_align_block_size_small_batch_expert(
   }
 
   for (size_t i = tid; i < numel; i += stride) {
-    int32_t expert_id = topk_ids[i];
-    if (has_expert_map) {
-      expert_id = expert_map[expert_id];
-      // filter invalid expert
-      if (expert_id == -1) continue;
-    }
-    int32_t rank_post_pad =
-        tokens_cnts[tid * num_experts + expert_id] + cumsum[expert_id];
+    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
+                                            num_experts, has_expert_map);
+        expert_id != -1) {
+      int32_t rank_post_pad =
+          tokens_cnts[tid * num_experts + expert_id] + cumsum[expert_id];
 
-    if (token_mask == nullptr || token_mask[i / topk_num]) {
-      sorted_token_ids[sorted_token_ids_offset + rank_post_pad] = i;
-      ++tokens_cnts[tid * num_experts + expert_id];
+      if (token_mask == nullptr || token_mask[i / topk_num]) {
+        sorted_token_ids[sorted_token_ids_offset + rank_post_pad] = i;
+        ++tokens_cnts[tid * num_experts + expert_id];
+      }
     }
   }
 }
@@ -303,22 +309,15 @@ __device__ void _count_and_sort_expert_tokens(
   const size_t stride = blockDim.x * gridDim.y;
 
   for (size_t i = tid; i < numel; i += stride) {
-    int32_t expert_id = topk_ids[i];
-    if (expert_id >= num_experts) {
-      continue;
-    }
-
-    if (has_expert_map) {
-      expert_id = expert_map[expert_id];
-      // filter invalid experts
-      if (expert_id == -1) continue;
-    }
-
-    if (token_mask == nullptr || token_mask[i / topk_num]) {
-      int32_t rank_post_pad = atomicAdd(
-          &cumsum_buffer[(model_offset * (num_experts + 1)) + expert_id], 1);
-      sorted_token_ids[max_num_tokens_padded * model_offset + rank_post_pad] =
-          i;
+    if (int expert_id = get_local_expert_id(i, topk_ids, expert_map,
+                                            num_experts, has_expert_map);
+        expert_id != -1) {
+      if (token_mask == nullptr || token_mask[i / topk_num]) {
+        int32_t rank_post_pad = atomicAdd(
+            &cumsum_buffer[(model_offset * (num_experts + 1)) + expert_id], 1);
+        sorted_token_ids[max_num_tokens_padded * model_offset + rank_post_pad] =
+            i;
+      }
     }
   }
 }

csrc/libtorch_stable/ngram_embedding_kernels.cu

@@ -0,0 +1,96 @@
+// N-gram embedding index kernel for LongCat-Flash (n-gram embedding variant).
+//
+// Adapted from SGLang:
+// https://github.com/sgl-project/sglang/blob/main/python/sglang/jit_kernel/csrc/ngram_embedding.cuh
+//
+// For each position, computes the hashed n-gram embedding ids that index the
+// concatenated embedder table. Integer tensors are int32 except ``row_indices``
+// (int64); the token table is ``[max_running_reqs, max_context_len]`` int32,
+// where a negative entry marks an ignored token (e.g. an EOS boundary).
+
+#include "torch_utils.h"
+
+#include "ops.h"
+
+#include <cstdint>
+
+namespace vllm::ngram_embedding {
+
+constexpr int kBlockThreads = 256;
+
+__global__ void ComputeNGramIdsKernel(
+    int batch_size, int ne_n, int ne_k,
+    int* ne_weights,                       // [ne_n-1, ne_k, ne_n]
+    int* ne_mods,                          // [ne_n-1, ne_k]
+    int* exclusive_ne_embedder_size_sums,  // [(ne_n-1)*ne_k + 1]
+    int* exclusive_req_len_sums,           // [batch_size + 1]
+    int* ne_token_table,  // [max_running_reqs, max_context_len]
+    int max_context_len,
+    const int64_t* __restrict__ row_indices,  // [batch_size]
+    int* column_starts,                       // [batch_size]
+    int* n_gram_ids                           // [token_num, (ne_n-1)*ne_k]
+) {
+  const int req_id = blockIdx.x % batch_size;
+  const int config_id = (blockIdx.x - req_id) / batch_size;
+  // n and k are offset from their physical meaning: n = real_n - 2, k = real_k
+  // - 1 (they index into ne_weights / ne_mods).
+  const int k = config_id % ne_k;
+  const int n = (config_id - config_id % ne_k) / ne_k;
+  const int ne_weight_base_idx = n * ne_k * ne_n + k * ne_n;
+  const int ne_mod = ne_mods[n * ne_k + k];
+  for (int i = exclusive_req_len_sums[req_id] + threadIdx.x;
+       i < exclusive_req_len_sums[req_id + 1]; i += blockDim.x) {
+    uint64_t n_gram_id = 0;
+    const int64_t current_token_offset = i - exclusive_req_len_sums[req_id];
+    const int64_t req_token_table_index =
+        row_indices[req_id] * static_cast<int64_t>(max_context_len);
+    const int64_t current_token_table_index =
+        req_token_table_index + column_starts[req_id] + current_token_offset;
+    for (int j = 0; j < n + 2; j++) {
+      if (current_token_table_index - j < req_token_table_index) {
+        break;  // outside this request's range
+      }
+      if (ne_token_table[current_token_table_index - j] < 0) {
+        break;  // ignored token
+      }
+      const uint64_t term =
+          (uint64_t)ne_token_table[current_token_table_index - j] *
+          (uint64_t)ne_weights[ne_weight_base_idx + j];
+      n_gram_id += term % ne_mod;
+    }
+    n_gram_id %= ne_mod;
+    n_gram_id += exclusive_ne_embedder_size_sums[n * ne_k + k];
+    n_gram_ids[i * (ne_n - 1) * ne_k + n * ne_k + k] = (int)(n_gram_id);
+  }
+}
+
+}  // namespace vllm::ngram_embedding
+
+void ngram_compute_n_gram_ids(
+    int64_t ne_n, int64_t ne_k, torch::stable::Tensor& ne_weights,
+    torch::stable::Tensor& ne_mods,
+    torch::stable::Tensor& exclusive_ne_embedder_size_sums,
+    torch::stable::Tensor& exclusive_req_len_sums,
+    torch::stable::Tensor& ne_token_table, torch::stable::Tensor& row_indices,
+    torch::stable::Tensor& column_starts, torch::stable::Tensor& n_gram_ids) {
+  const int batch_size = static_cast<int>(exclusive_req_len_sums.size(0) - 1);
+  const int max_context_len = static_cast<int>(ne_token_table.size(1));
+  const int num_configs = (static_cast<int>(ne_n) - 1) * static_cast<int>(ne_k);
+  const int grid_size = num_configs * batch_size;
+  if (grid_size <= 0) return;
+
+  const torch::stable::accelerator::DeviceGuard device_guard(
+      ne_weights.get_device_index());
+  const cudaStream_t stream = get_current_cuda_stream();
+  vllm::ngram_embedding::ComputeNGramIdsKernel<<<
+      grid_size, vllm::ngram_embedding::kBlockThreads, 0, stream>>>(
+      batch_size, static_cast<int>(ne_n), static_cast<int>(ne_k),
+      ne_weights.mutable_data_ptr<int32_t>(),
+      ne_mods.mutable_data_ptr<int32_t>(),
+      exclusive_ne_embedder_size_sums.mutable_data_ptr<int32_t>(),
+      exclusive_req_len_sums.mutable_data_ptr<int32_t>(),
+      ne_token_table.mutable_data_ptr<int32_t>(), max_context_len,
+      row_indices.const_data_ptr<int64_t>(),
+      column_starts.mutable_data_ptr<int32_t>(),
+      n_gram_ids.mutable_data_ptr<int32_t>());
+}

csrc/libtorch_stable/ops.h

@@ -313,7 +313,7 @@ void fused_minimax_m3_qknorm_rope_kv_insert(
     std::optional<torch::stable::Tensor> index_cache, int64_t block_size,
     std::optional<torch::stable::Tensor> q_out,
     std::optional<torch::stable::Tensor> index_q_out,
-    const std::string& kv_cache_dtype);
+    const std::string& kv_cache_dtype, bool skip_index_branch);
 
 // Sampler kernels (shared CUDA/ROCm)
 void apply_repetition_penalties_(
@@ -414,6 +414,8 @@ void gelu_new(torch::stable::Tensor& out, torch::stable::Tensor& input);
 void gelu_fast(torch::stable::Tensor& out, torch::stable::Tensor& input);
 void gelu_quick(torch::stable::Tensor& out, torch::stable::Tensor& input);
 
+void relu_squared(torch::stable::Tensor& out, torch::stable::Tensor& input);
+
 // INT8 quantization kernels (shared CUDA/ROCm)
 void static_scaled_int8_quant(torch::stable::Tensor& out,
                               torch::stable::Tensor const& input,
@@ -554,3 +556,12 @@ void cp_gather_indexer_k_quant_cache(
                                                 // quant_block_size * 4]
     const torch::stable::Tensor& block_table,   // [batch_size, num_blocks]
     const torch::stable::Tensor& cu_seq_lens);  // [batch_size + 1]
+
+// LongCat n-gram embedding index kernel (see ngram_embedding_kernels.cu).
+void ngram_compute_n_gram_ids(
+    int64_t ne_n, int64_t ne_k, torch::stable::Tensor& ne_weights,
+    torch::stable::Tensor& ne_mods,
+    torch::stable::Tensor& exclusive_ne_embedder_size_sums,
+    torch::stable::Tensor& exclusive_req_len_sums,
+    torch::stable::Tensor& ne_token_table, torch::stable::Tensor& row_indices,
+    torch::stable::Tensor& column_starts, torch::stable::Tensor& n_gram_ids);

csrc/libtorch_stable/torch_bindings.cpp

@@ -466,7 +466,7 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
       "Tensor? slot_mapping, Tensor? index_slot_mapping, "
       "Tensor!? kv_cache, Tensor!? index_cache, "
       "int block_size, Tensor!? q_out, Tensor!? index_q_out, "
-      "str kv_cache_dtype) -> ()");
+      "str kv_cache_dtype, bool skip_index_branch=False) -> ()");
 
   // Apply repetition penalties to logits in-place.
   ops.def(
@@ -539,6 +539,9 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
   // Quick GELU implementation.
   ops.def("gelu_quick(Tensor! out, Tensor input) -> ()");
 
+  // relu(x)^2 activation from https://arxiv.org/abs/2109.08668v2
+  ops.def("relu_squared(Tensor! out, Tensor input) -> ()");
+
   // Compute int8 quantized tensor for given scaling factor.
   ops.def(
       "static_scaled_int8_quant(Tensor! result, Tensor input, Tensor scale,"
@@ -598,9 +601,22 @@ STABLE_TORCH_LIBRARY_FRAGMENT(_C, ops) {
       "Tensor? initial_state_idx,"
       "Tensor? cu_chunk_seqlen,"
       "Tensor? last_chunk_indices) -> ()");
+
+  // LongCat n-gram embedding index kernel. All tensor args are marked mutable
+  // to match the (non-const) stable-Tensor& C++ signature; only ne_token_table
+  // and n_gram_ids are actually written in place.
+  ops.def(
+      "ngram_compute_n_gram_ids(int ne_n, int ne_k, Tensor(a!) ne_weights, "
+      "Tensor(b!) ne_mods, Tensor(c!) exclusive_ne_embedder_size_sums, "
+      "Tensor(d!) exclusive_req_len_sums, Tensor(e!) ne_token_table, "
+      "Tensor(f!) row_indices, Tensor(g!) column_starts, "
+      "Tensor(h!) n_gram_ids) -> ()");
 }
 
 STABLE_TORCH_LIBRARY_IMPL(_C, CUDA, ops) {
+  // LongCat n-gram embedding index kernel.
+  ops.impl("ngram_compute_n_gram_ids", TORCH_BOX(&ngram_compute_n_gram_ids));
+
   // Per-token group quantization
   ops.impl("per_token_group_fp8_quant", TORCH_BOX(&per_token_group_quant_fp8));
   ops.impl("per_token_group_fp8_quant_packed",
@@ -702,6 +718,7 @@ STABLE_TORCH_LIBRARY_IMPL(_C, CUDA, ops) {
   ops.impl("gelu_new", TORCH_BOX(&gelu_new));
   ops.impl("gelu_fast", TORCH_BOX(&gelu_fast));
   ops.impl("gelu_quick", TORCH_BOX(&gelu_quick));
+  ops.impl("relu_squared", TORCH_BOX(&relu_squared));
   ops.impl("silu_and_mul_with_clamp", TORCH_BOX(&silu_and_mul_clamp));
 
   // INT8 quantization kernels

csrc/ops.h

@@ -43,6 +43,8 @@ void gelu_fast(torch::Tensor& out, torch::Tensor& input);
 
 void gelu_quick(torch::Tensor& out, torch::Tensor& input);
 
+void relu_squared(torch::Tensor& out, torch::Tensor& input);
+
 void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,
                               torch::Tensor const& scale,
                               std::optional<torch::Tensor> const& azp);

docker/Dockerfile.ppc64le

@@ -1,275 +1,80 @@
+# Base UBI image
 ARG BASE_UBI_IMAGE_TAG=9.6-1754584681
 
 ###############################################################
-# Stage to build openblas
+#                       BUILDER  STAGE                        #
 ###############################################################
 
-FROM registry.access.redhat.com/ubi9/ubi-minimal:${BASE_UBI_IMAGE_TAG} AS openblas-builder
-
-ARG MAX_JOBS
-ARG OPENBLAS_VERSION=0.3.30
-RUN microdnf install -y dnf && dnf install -y gcc-toolset-14 make wget unzip \
-    && source /opt/rh/gcc-toolset-14/enable \
-    && wget https://github.com/OpenMathLib/OpenBLAS/releases/download/v$OPENBLAS_VERSION/OpenBLAS-$OPENBLAS_VERSION.zip \
-    && unzip OpenBLAS-$OPENBLAS_VERSION.zip \
-    && cd OpenBLAS-$OPENBLAS_VERSION \
-    &&  make -j${MAX_JOBS} TARGET=POWER9 BINARY=64 USE_OPENMP=1 USE_THREAD=1 NUM_THREADS=120 DYNAMIC_ARCH=1 INTERFACE64=0 \
-    && cd /tmp && touch control
-
-
-###############################################################
-# base stage with dependencies coming from centos mirrors
-###############################################################
-FROM registry.access.redhat.com/ubi9/ubi-minimal:${BASE_UBI_IMAGE_TAG} AS centos-deps-builder
-RUN  microdnf install -y dnf && \ 
-     dnf install -y https://mirror.stream.centos.org/9-stream/BaseOS/`arch`/os/Packages/centos-gpg-keys-9.0-26.el9.noarch.rpm \
-        https://mirror.stream.centos.org/9-stream/BaseOS/`arch`/os/Packages/centos-stream-repos-9.0-26.el9.noarch.rpm \
-        https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm && \
-        dnf config-manager --set-enabled crb
-
-RUN dnf install -y openjpeg2-devel lcms2-devel tcl-devel tk-devel fribidi-devel yajl-devel && \
-    dnf remove -y centos-gpg-keys-9.0-24.el9.noarch centos-stream-repos-9.0-26.el9.noarch 
-
-
-###############################################################
-# base stage with basic dependencies
-###############################################################
-
-FROM centos-deps-builder AS base-builder
+FROM registry.access.redhat.com/ubi9/ubi-minimal:${BASE_UBI_IMAGE_TAG} AS builder-base
 
+ARG VLLM_VERSION="0.22.1"
 ARG PYTHON_VERSION=3.12
-ARG OPENBLAS_VERSION=0.3.30
-
-# Set Environment Variables for venv, cargo & openblas
-ENV VIRTUAL_ENV=/opt/vllm
-ENV PATH=${VIRTUAL_ENV}/bin:/root/.cargo/bin:$PATH
-ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig/
-ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib64:/usr/local/lib:/usr/lib64:/usr/lib
-ENV UV_LINK_MODE=copy
-
-# install gcc-13, python, rust, openblas
-# Note: A symlink for libatomic.so is created for gcc-13 (linker fails to find libatomic otherwise - reqd. for sentencepiece)
-# Note: A dummy file 'control' is created in /tmp/ to artificially create dependencies between stages when building stages in parallel
-#       when `--jobs=<N>` is passed with podman build command
-
-COPY --from=openblas-builder /tmp/control /dev/null
-
-RUN --mount=type=bind,from=openblas-builder,source=/OpenBLAS-$OPENBLAS_VERSION/,target=/openblas/,rw \
-    dnf install -y openssl-devel \
-    && dnf install -y \
-       git tar gcc-toolset-14 automake libtool \
-       pkgconfig xsimd zeromq-devel kmod findutils protobuf* \
-       libtiff-devel libjpeg-devel zlib-devel freetype-devel libwebp-devel \
-       harfbuzz-devel libraqm-devel libimagequant-devel libxcb-devel \
-       python${PYTHON_VERSION}-devel python${PYTHON_VERSION}-pip clang-devel \
-    && dnf clean all \
-    && PREFIX=/usr/local make -C /openblas install \
-    && ln -sf /usr/lib64/libatomic.so.1 /usr/lib64/libatomic.so \
-    && python${PYTHON_VERSION} -m venv ${VIRTUAL_ENV} \
-    && python -m pip install -U pip uv \
-    && uv pip install wheel build "setuptools<70" setuptools_scm setuptools_rust meson-python 'cmake<4' ninja cython scikit_build_core scikit_build \
-    && curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y \
-    && cd /tmp && touch control
-
-
-###############################################################
-# Stage to build torch family
-###############################################################
-
-FROM base-builder AS torch-builder
-
-ARG MAX_JOBS
-ARG TORCH_VERSION=2.7.0
-ARG _GLIBCXX_USE_CXX11_ABI=1
-ARG OPENBLAS_VERSION=0.3.30
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    source /opt/rh/gcc-toolset-14/enable &&  \
-    git clone --recursive https://github.com/pytorch/pytorch.git -b v${TORCH_VERSION} && \
-    cd pytorch && \
-    uv pip install -r requirements.txt && \
-    python setup.py develop && \
-    rm -f dist/torch*+git*whl && \
-    MAX_JOBS=${MAX_JOBS:-$(nproc)} \
-    PYTORCH_BUILD_VERSION=${TORCH_VERSION} PYTORCH_BUILD_NUMBER=1 uv build --wheel --out-dir /torchwheels/
-
-ARG TORCHVISION_VERSION=0.22.0
-ARG TORCHVISION_USE_NVJPEG=0
-ARG TORCHVISION_USE_FFMPEG=0
-RUN --mount=type=cache,target=/root/.cache/uv \
-    source /opt/rh/gcc-toolset-14/enable && \
-    git clone --recursive https://github.com/pytorch/vision.git -b v${TORCHVISION_VERSION} && \
-    cd vision && \
-    MAX_JOBS=${MAX_JOBS:-$(nproc)} \
-    BUILD_VERSION=${TORCHVISION_VERSION} \
-    uv build --wheel --out-dir /torchwheels/ --no-build-isolation
-
-ARG TORCHAUDIO_VERSION=2.7.0
-ARG BUILD_SOX=1
-ARG BUILD_KALDI=1
-ARG BUILD_RNNT=1
-ARG USE_FFMPEG=0
-ARG USE_ROCM=0
-ARG USE_CUDA=0
-ARG TORCHAUDIO_TEST_ALLOW_SKIP_IF_NO_FFMPEG=1
-RUN --mount=type=cache,target=/root/.cache/uv \
-    source /opt/rh/gcc-toolset-14/enable && \
-    git clone --recursive https://github.com/pytorch/audio.git -b v${TORCHAUDIO_VERSION} && \
-    cd audio && \
-    MAX_JOBS=${MAX_JOBS:-$(nproc)} \
-    BUILD_VERSION=${TORCHAUDIO_VERSION} \
-    uv build --wheel --out-dir /torchwheels/ --no-build-isolation
-
-###############################################################
-# Stage to build pyarrow
-###############################################################
-
-FROM base-builder AS arrow-builder
-
-ARG MAX_JOBS
-ARG PYARROW_PARALLEL
-ARG PYARROW_VERSION=21.0.0
-RUN --mount=type=cache,target=/root/.cache/uv \
-    source /opt/rh/gcc-toolset-14/enable && \
-    git clone --recursive https://github.com/apache/arrow.git -b apache-arrow-${PYARROW_VERSION} && \
-    cd arrow/cpp && \
-    mkdir build && cd build && \
-    cmake -DCMAKE_BUILD_TYPE=release \
-        -DCMAKE_INSTALL_PREFIX=/usr/local \
-        -DARROW_PYTHON=ON \
-        -DARROW_BUILD_TESTS=OFF \
-        -DARROW_JEMALLOC=ON \
-        -DARROW_BUILD_STATIC="OFF" \
-        -DARROW_PARQUET=ON \
-        .. && \
-    make install -j ${MAX_JOBS:-$(nproc)} && \
-    cd ../../python/ && \
-    uv pip install -v -r requirements-build.txt && uv pip install numpy==2.1.3 && \
-    PYARROW_PARALLEL=${PYARROW_PARALLEL:-$(nproc)} \
-    python setup.py build_ext \
-    --build-type=release --bundle-arrow-cpp \
-    bdist_wheel --dist-dir /arrowwheels/
-
-###############################################################
-# Stage to build opencv
-###############################################################
-
-FROM base-builder AS cv-builder
-
-ARG MAX_JOBS
-ARG OPENCV_VERSION=86
-# patch for version 4.11.0.86
-ARG OPENCV_PATCH=97f3f39
-ARG ENABLE_HEADLESS=1
-RUN --mount=type=cache,target=/root/.cache/uv \
-    source /opt/rh/gcc-toolset-14/enable && \
-    git clone --recursive https://github.com/opencv/opencv-python.git -b ${OPENCV_VERSION} && \
-    cd opencv-python && \
-    sed -i -E -e 's/"setuptools.+",/"setuptools",/g' pyproject.toml && \
-    cd opencv && git cherry-pick --no-commit $OPENCV_PATCH && cd .. && \
-    uv pip install scikit-build && \    
-    python -m build --wheel --installer=uv --outdir /opencvwheels/
-
-###############################################################
-# Stage to build numactl
-###############################################################
-
-FROM base-builder AS numa-builder
-
-# Note: Building numactl with gcc-11. Compiling with gcc-13 in this builder stage will
-# trigger recompilation with gcc-11 (and require libtool) in the final stage where we do not have gcc-13
-ARG MAX_JOBS
-ARG NUMACTL_VERSION=2.0.19
-RUN git clone --recursive https://github.com/numactl/numactl.git -b v${NUMACTL_VERSION} \
-    && cd numactl \
-    && autoreconf -i && ./configure \
-    && make -j ${MAX_JOBS:-$(nproc)}
-
-
-###############################################################
-# Stage to build numba 
-###############################################################
-
-FROM base-builder AS numba-builder
-
-ARG MAX_JOBS
-ARG NUMBA_VERSION=0.61.2
-
-# Clone all required dependencies
-RUN dnf install ninja-build llvm15 llvm15-devel -y && source /opt/rh/gcc-toolset-14/enable && export PATH=$PATH:/usr/lib64/llvm15/bin && \
-    git clone --recursive https://github.com/numba/numba.git -b ${NUMBA_VERSION} && \
-    cd ./numba && \
-    if ! grep '#include "dynamic_annotations.h"' numba/_dispatcher.cpp; then \
-       sed -i '/#include "internal\/pycore_atomic.h"/i\#include "dynamic_annotations.h"' numba/_dispatcher.cpp; \
-    fi && python -m build --wheel --installer=uv --outdir /numbawheels/
-
-###############################################################
-# Stage to build vllm - this stage builds and installs
-# vllm, tensorizer and vllm-tgis-adapter and builds uv cache
-# for transitive dependencies - eg. grpcio
-###############################################################
-
-FROM base-builder AS vllmcache-builder
-
-ENV LLVM_CONFIG=/usr/lib64/llvm15/bin/llvm-config
-ENV PATH=/usr/lib64/llvm15/bin:$PATH
-
-COPY --from=torch-builder /tmp/control /dev/null
-COPY --from=arrow-builder /tmp/control /dev/null
-COPY --from=cv-builder /tmp/control /dev/null
-COPY --from=numa-builder /tmp/control /dev/null
-COPY --from=numba-builder /tmp/control /dev/null
-
 ARG VLLM_TARGET_DEVICE=cpu
-ARG GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1
 
-# this step installs vllm and populates uv cache
-# with all the transitive dependencies
+USER root
+WORKDIR /root
+
+ENV HOME=/root \
+    WHEEL_DIR=/wheelsdir \
+    VIRTUAL_ENV=/opt/vllm \
+    GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=1 \
+    CARGO_HOME=/root/.cargo \
+    RUSTUP_HOME=/root/.rustup \
+    UV_CACHE_DIR=$HOME/.cache/uv \
+    PATH=/root/.cargo/bin:/root/.rustup/bin:${VIRTUAL_ENV}/bin:$PATH
+
+RUN echo "DEBUG: VLLM_VERSION=${VLLM_VERSION}"
+RUN --mount=type=cache,target=/var/cache/dnf \
+    microdnf install -y \
+    python${PYTHON_VERSION}-devel python${PYTHON_VERSION}-pip \
+    && python${PYTHON_VERSION} -m venv ${VIRTUAL_ENV} \
+    && python${PYTHON_VERSION} -m pip install -U pip uv --no-cache
+
+# Important: Copy only bare minimum required for the script to run
+COPY requirements/ requirements/
+COPY pyproject.toml ./
+
+# The script is expected to install whatever python dependencies are missing
+# as well as whatever system libraries need to be installed from source
+COPY  build_vllm_*.sh ./
+
 RUN --mount=type=cache,target=/root/.cache/uv \
-    dnf install llvm15 llvm15-devel -y && \
-    rpm -ivh --nodeps https://mirror.stream.centos.org/9-stream/CRB/ppc64le/os/Packages/protobuf-lite-devel-3.14.0-16.el9.ppc64le.rpm && \
-    source /opt/rh/gcc-toolset-14/enable && \
-    git clone https://github.com/huggingface/xet-core.git && cd xet-core/hf_xet/ && \
-    uv pip install maturin && \
-    uv build --wheel --out-dir /hf_wheels/
+    sh ./build_vllm_$(uname -m).sh
+
+# copy vllm source code to build cache
+COPY . .
 
-ENV CXXFLAGS="-fno-lto -Wno-error=free-nonheap-object" \
-    CFLAGS="-fno-lto"
 RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,from=torch-builder,source=/torchwheels/,target=/torchwheels/,ro \
-    --mount=type=bind,from=arrow-builder,source=/arrowwheels/,target=/arrowwheels/,ro \
-    --mount=type=bind,from=cv-builder,source=/opencvwheels/,target=/opencvwheels/,ro \
-    --mount=type=bind,from=numa-builder,source=/numactl/,target=/numactl/,rw \
-    --mount=type=bind,from=numba-builder,source=/numbawheels/,target=/numbawheels/,ro \
-    --mount=type=bind,src=.,dst=/src/,rw \
     source /opt/rh/gcc-toolset-14/enable && \
-    export PATH=$PATH:/usr/lib64/llvm15/bin && \
-    uv pip install /opencvwheels/*.whl /arrowwheels/*.whl /torchwheels/*.whl /numbawheels/*.whl && \
-    sed -i -e 's/.*torch.*//g' /src/pyproject.toml /src/requirements/*.txt && \
-    sed -i -e 's/.*sentencepiece.*//g' /src/pyproject.toml /src/requirements/*.txt && \
-    uv pip install sentencepiece==0.2.0 pandas pythran nanobind pybind11 /hf_wheels/*.whl && \
-    make -C /numactl install && \
-    # sentencepiece.pc is in some pkgconfig inside uv cache
-    export PKG_CONFIG_PATH=$(find / -type d -name "pkgconfig" 2>/dev/null | tr '\n' ':') && \
-    nanobind_DIR=$(uv pip show nanobind | grep Location | sed 's/^Location: //;s/$/\/nanobind\/cmake/') && uv pip install -r /src/requirements/common.txt -r /src/requirements/cpu.txt -r /src/requirements/build/cuda.txt --no-build-isolation && \
-    cd /src/ && \
-    uv build --wheel --out-dir /vllmwheel/ --no-build-isolation && \
-    uv pip install /vllmwheel/*.whl
+    pip install -U uv
 
+# build & install vLLM so that all transitive dependencies are build/downloaded into the uv cache
+RUN --mount=type=cache,target=/root/.cache/uv \
+    source /opt/rh/gcc-toolset-14/enable && \
+    export PATH=/opt/rh/gcc-toolset-14/root/usr/bin:$PATH && \
+    export CC=/opt/rh/gcc-toolset-14/root/usr/bin/gcc && \
+    export CXX=/opt/rh/gcc-toolset-14/root/usr/bin/g++ && \
+    export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:/usr/lib64/pkgconfig:$PKG_CONFIG_PATH && \
+    export CMAKE_PREFIX_PATH=/usr/local:/usr:$CMAKE_PREFIX_PATH && \
+    export Protobuf_PROTOC_EXECUTABLE=/usr/bin/protoc && \
+    export CFLAGS="-mcpu=power10 -mtune=power10" && \
+    export CXXFLAGS="-mcpu=power10 -mtune=power10" && \
+    export DNNL_ARCH_OPT_FLAGS="-mcpu=power10 -mtune=power10" && \
+    export C_INCLUDE_PATH=/usr/local/include:$C_INCLUDE_PATH && \
+    export CPLUS_INCLUDE_PATH=/usr/local/include:$CPLUS_INCLUDE_PATH && \
+    uv pip install 'setuptools>=78.1.1' && \
+    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/OpenBLAS/lib/:/usr/local/lib64:/usr/local/lib && \
+    export LIBGOMP=/opt/rh/gcc-toolset-14/root/usr/lib/gcc/ppc64le-redhat-linux/14/libgomp.so && \
 
-###############################################################
-# Stage to build lapack
-###############################################################
-
-FROM base-builder AS lapack-builder
-
-ARG MAX_JOBS
-ARG LAPACK_VERSION=3.12.1
-RUN git clone --recursive https://github.com/Reference-LAPACK/lapack.git -b v${LAPACK_VERSION} \
-    && cd lapack && source /opt/rh/gcc-toolset-14/enable \
-    && cmake -B build -S . \
-    && cmake --build build -j ${MAX_JOBS:-$(nproc)}
+    export CMAKE_LIBRARY_PATH=$(dirname $LIBGOMP):${CMAKE_LIBRARY_PATH} && \
+    export LIBRARY_PATH=$(dirname $LIBGOMP):${LIBRARY_PATH} && \
+    export LD_LIBRARY_PATH=$(dirname $LIBGOMP):${LD_LIBRARY_PATH} && \
 
+    echo "LIBGOMP=${LIBGOMP}" && \
+    find /root/.cache/uv -name "*.whl" && \
+    SETUPTOOLS_SCM_PRETEND_VERSION="$VLLM_VERSION" uv build \
+      --wheel --out-dir ${WHEEL_DIR} --no-build-isolation && \
+    uv pip install "$(echo ${WHEEL_DIR}/vllm*.whl)[tensorizer]" --refresh
 
 ###############################################################
 #                   FINAL VLLM IMAGE STAGE                    #
@@ -278,72 +83,74 @@ RUN git clone --recursive https://github.com/Reference-LAPACK/lapack.git -b v${L
 FROM registry.access.redhat.com/ubi9/ubi-minimal:${BASE_UBI_IMAGE_TAG} AS vllm-openai
 
 ARG PYTHON_VERSION=3.12
-ARG OPENBLAS_VERSION=0.3.30
+ENV VLLM_NO_USAGE_STATS=1
 
 # Set Environment Variables for venv & openblas
 ENV VIRTUAL_ENV=/opt/vllm
-ENV PATH=${VIRTUAL_ENV}/bin:$PATH
-ENV PKG_CONFIG_PATH=/usr/local/lib/pkgconfig/
-ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib64:/usr/local/lib:/usr/lib64:/usr/lib
+ENV PCP_DIR=/opt/rh/gcc-toolset-14/root
+ENV PATH=${VIRTUAL_ENV}/bin:${PCP_DIR}/usr/bin:/usr/local/bin:$PATH
+ENV PKG_CONFIG_PATH=${PCP_DIR}/usr/lib64/pkgconfig:/usr/local/lib/pkgconfig/
+ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
+ENV LD_LIBRARY_PATH=${PCP_DIR}/usr/lib64:${PCP_DIR}/usr/lib:${VIRTUAL_ENV}/lib64/python${PYTHON_VERSION}/site-packages/torch/lib:/usr/local/lib:$LD_LIBRARY_PATH:/usr/local/lib64:/usr/lib64:/usr/lib
 ENV UV_LINK_MODE=copy
-ENV OMP_NUM_THREADS=16
+ARG VLLM_VERSION="0.22.1"
+ARG UV_EXTRA_INDEX_URL="https://wheels.developerfirst.ibm.com/ppc64le/linux/+simple/"
+ENV UV_EXTRA_INDEX_URL=${UV_EXTRA_INDEX_URL}
+ENV UV_INDEX_STRATEGY=first-match
 
-# create artificial dependencies between stages for independent stages to build in parallel
-COPY --from=torch-builder /tmp/control /dev/null
-COPY --from=arrow-builder /tmp/control /dev/null
-COPY --from=cv-builder /tmp/control /dev/null
-COPY --from=vllmcache-builder /tmp/control /dev/null
-COPY --from=numa-builder /tmp/control /dev/null
-COPY --from=lapack-builder /tmp/control /dev/null
-COPY --from=openblas-builder /tmp/control /dev/null
-COPY --from=numba-builder /tmp/control /dev/null
 
-# install gcc-11, python, openblas, numactl, lapack
 RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,from=numa-builder,source=/numactl/,target=/numactl/,rw \
-    --mount=type=bind,from=lapack-builder,source=/lapack/,target=/lapack/,rw \
-    --mount=type=bind,from=openblas-builder,source=/OpenBLAS-$OPENBLAS_VERSION/,target=/openblas/,rw \
     rpm -ivh https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm && \
     microdnf install --nodocs -y \
-    libomp libicu tar findutils openssl llvm15 llvm15-devel \
-    pkgconfig xsimd g++ gcc-fortran libsndfile \
+    libomp libicu tar autoconf automake libtool findutils openssl numactl numactl-devel \
+    pkgconfig xsimd gcc-toolset-14 libsndfile \
     libtiff libjpeg openjpeg2 zlib zeromq \
     freetype lcms2 libwebp tcl tk utf8proc \
-    harfbuzz fribidi libraqm libimagequant libxcb util-linux \
+    harfbuzz fribidi libraqm libimagequant libxcb util-linux gperftools-libs \
     python${PYTHON_VERSION}-devel python${PYTHON_VERSION}-pip \
-    && export PATH=$PATH:/usr/lib64/llvm15/bin && microdnf clean all \
-    && python${PYTHON_VERSION} -m venv ${VIRTUAL_ENV} \
-    && python -m pip install -U pip uv --no-cache \
-    && make -C /numactl install \
-    && PREFIX=/usr/local make -C /openblas install \
-    && uv pip install 'cmake<4' \
-    && cmake --install /lapack/build \
-    && uv pip uninstall cmake
+    && source /opt/rh/gcc-toolset-14/enable \
+    && microdnf update -y \
+    && microdnf clean all
 
-# consume previously built wheels (including vllm)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,from=torch-builder,source=/torchwheels/,target=/torchwheels/,ro \
-    --mount=type=bind,from=arrow-builder,source=/arrowwheels/,target=/arrowwheels/,ro \
-    --mount=type=bind,from=cv-builder,source=/opencvwheels/,target=/opencvwheels/,ro \
-    --mount=type=bind,from=vllmcache-builder,source=/hf_wheels/,target=/hf_wheels/,ro \
-    --mount=type=bind,from=vllmcache-builder,source=/vllmwheel/,target=/vllmwheel/,ro \
-    --mount=type=bind,from=numba-builder,source=/numbawheels/,target=/numbawheels/,ro \
-    export PKG_CONFIG_PATH=$(find / -type d -name "pkgconfig" 2>/dev/null | tr '\n' ':') && uv pip install sentencepiece==0.2.0 && \
-    HOME=/root uv pip install /opencvwheels/*.whl /arrowwheels/*.whl /torchwheels/*.whl /numbawheels/*.whl /hf_wheels/*.whl /vllmwheel/*.whl
+# The `lscpu` command was added as a requirement in part of https://github.com/vllm-project/vllm/pull/21032, so installing it.
+RUN microdnf install --nodocs -y util-linux && \
+    microdnf clean all
 
+COPY --from=builder-base /usr/lib64/libprotobuf.so.25 /usr/lib64/
+COPY --from=builder-base /usr/lib64/libprotobuf.so.25.0.0 /usr/lib64/
 
-COPY ./ /workspace/vllm
-WORKDIR /workspace/vllm
-ARG GIT_REPO_CHECK=0
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
+# Use builder venv in final stage instead of wheel reinstallation
+COPY --from=builder-base /opt/vllm /opt/vllm
 
-# install development dependencies (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install -e tests/vllm_test_utils
+ENV LD_PRELOAD=/usr/lib64/libtcmalloc.so.4
 
-WORKDIR /workspace/
+WORKDIR /home/vllm
 
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
+# setup non-root user for OpenShift
+RUN umask 002 && \
+    useradd --uid 2000 --gid 0 vllm && \
+    mkdir -p /home/vllm && \
+    chmod g+rwx /home/vllm
+
+ENV HOME=/home/vllm
+
+# Add labels to document build configuration
+LABEL org.opencontainers.image.title="vLLM CPU"
+LABEL org.opencontainers.image.description="vLLM inference engine for CPU platforms"
+LABEL org.opencontainers.image.vendor="vLLM Project"
+LABEL org.opencontainers.image.source="https://github.com/vllm-project/vllm"
+
+# Build configuration labels
+ARG TARGETARCH
+ARG VLLM_CPU_PPC64LE
+ARG PYTHON_VERSION
+
+LABEL ai.vllm.build.target-arch="${TARGETARCH}"
+LABEL ai.vllm.build.cpu-ppc64le="${VLLM_CPU_PPC64LE:-false}"
+LABEL ai.vllm.build.python-version="${PYTHON_VERSION:-3.12}"
+
+USER 2000
 
 ENTRYPOINT ["vllm", "serve"]
+
+ 

docs/cli/README.md

@@ -50,6 +50,21 @@ vllm serve --help=max-num-seqs
 vllm serve --help=max
 ```
 
+!!! tip "Human-readable integer arguments"
+    Many integer arguments accept human-readable suffixes for convenience. For example:
+
+    - `1k` = 1,000 (decimal kilo)
+    - `1K` = 1,024 (binary kibibyte)
+    - `1m` = 1,000,000 (decimal mega)
+    - `1M` = 1,048,576 (binary mebibyte)
+    - `1g` / `1G` = 1 billion / 1 gibibyte
+    - `1t` / `1T` = 1 trillion / 1 tebibyte
+    
+    Decimal suffixes (`k`, `m`, `g`, `t`) also accept floating point: `25.6k` = 25,600.
+    Binary suffixes (`K`, `M`, `G`, `T`) require integers: `32K` = 32,768.
+    
+    Supported arguments include: `--max-model-len`, `--max-num-batched-tokens`, `--max-num-scheduled-tokens`, `--kv-cache-memory-bytes`, `--safetensors-prefetch-block-size`.
+
 See [vllm serve](./serve.md) for the full reference of all available arguments.
 
 ## launch

docs/configuration/conserving_memory.md

@@ -42,7 +42,7 @@ and the maximum batch size (`max_num_seqs` option).
 ```python
 from vllm import LLM
 
-llm = LLM(model="adept/fuyu-8b", max_model_len=2048, max_num_seqs=2)
+llm = LLM(model="Qwen/Qwen2.5-VL-3B-Instruct", max_model_len=2048, max_num_seqs=2)
 ```
 
 ## Reduce CUDA Graphs

docs/configuration/optimization.md

@@ -16,6 +16,14 @@ vLLM provides 4 optimization levels (`-O0`, `-O1`, `-O2`, `-O3`) that allow user
 
 For more information, see the [optimization level documentation](../design/optimization_levels.md).
 
+## Faster Startup
+
+Beyond the optimization levels, three mechanisms reduce time-to-first-token on repeated boots of the same (model, config, hardware) combination:
+
+- **Reuse the compile cache.** vLLM persists `torch.compile` artifacts under `VLLM_CACHE_ROOT` (default `~/.cache/vllm`), and the cache directory can be copied between machines or baked into a container image; see the [torch.compile design doc](../design/torch_compile.md). Set `VLLM_FORCE_AOT_LOAD=1` to fail loudly instead of silently recompiling when the cache misses (any change to the model, config, relevant `VLLM_*` environment variables, torch build, or GPU model invalidates it).
+- **Skip memory profiling with `--kv-cache-memory`.** On startup, vLLM logs the exact `--kv-cache-memory` value that reproduces the current allocation. Passing it back on the next boot skips the memory-profiling measurement and the CUDA-graph memory estimation pass. Note that this has performance implications: the KV cache is sized to exactly the given value instead of being measured, so a conservative value caps batch concurrency (and therefore throughput), while an optimistic one fails at allocation time. The value is only valid on the same GPU with the same initial free memory; if a boot OOMs after hardware or co-tenant changes, remove the flag to re-profile.
+- **Serve without CUDA graphs using `--enforce-eager`.** Skips both compilation and CUDA-graph capture for the fastest possible startup, at the cost of steady-state decode performance. Useful for development loops and for measuring how much of a boot is compile/capture.
+
 ## Preemption
 
 Due to the autoregressive nature of transformer architecture, there are times when KV cache space is insufficient to handle all batched requests.

docs/contributing/ci/nightly_builds.md

@@ -14,7 +14,7 @@ Wheels are built in the `Release` pipeline (`.buildkite/release-pipeline.yaml`)
 Each build step:
 
 1. Builds the wheel in a Docker container.
-2. Renames the wheel filename to use the correct manylinux tag (currently `manylinux_2_31`) for PEP 600 compliance.
+2. Renames the wheel filename to use the correct manylinux tag (currently `manylinux_2_28`) for PEP 600 compliance.
 3. Uploads the wheel to S3 bucket `vllm-wheels` under `/{commit_hash}/`.
 
 ### Index Generation

docs/contributing/ci/update_pytorch_version.md

@@ -93,6 +93,28 @@ To address this, manually trigger a build on Buildkite to accomplish two objecti
 <img width="60%" alt="Buildkite new build popup" src="https://github.com/user-attachments/assets/3b07f71b-bb18-4ca3-aeaf-da0fe79d315f" />
 </p>
 
+You can also trigger this build from the command line with
+[`.buildkite/scripts/trigger-ci-build.sh`](../../../.buildkite/scripts/trigger-ci-build.sh)
+(dry-run by default; pass `--execute` to actually trigger it).
+
+## Test against PyTorch nightly
+
+The steps above test against a specific PyTorch RC/stable wheel pinned in the
+requirements files. To instead build and run the CI suite against the latest
+PyTorch **nightly** wheels, set the `TORCH_NIGHTLY=1` environment variable on
+the build (or apply the `ready-torch-nightly` label to the PR).
+
+When `TORCH_NIGHTLY=1`, the base CI image is built against PyTorch nightly
+(`image_build_torch_nightly.sh`, `PYTORCH_NIGHTLY=1`, CUDA 13.0) and tagged at
+the normal image tag, so the entire existing pipeline runs on nightly torch --
+there is no separate pipeline section to trigger. Combine it with `RUN_ALL=1`
+to run the full suite (the `ready-torch-nightly` label and
+`trigger-ci-build.sh --torch-nightly` both set this for you). This is the
+configuration to use for a scheduled "vLLM vs PyTorch nightly" run.
+
+Use `.buildkite/scripts/trigger-ci-build.sh --torch-nightly` to trigger it from
+the command line.
+
 ## Update all the different vLLM platforms
 
 Rather than attempting to update all vLLM platforms in a single pull request, it's more manageable

docs/contributing/model/multimodal.md

@@ -324,154 +324,44 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
         return image_token * num_images
     ```
 
-=== "No input placeholders: Fuyu"
+=== "No input placeholders: PaliGemma"
 
-    Looking at the code of HF's `FuyuForCausalLM`:
-
-    ??? code
-
-        ```python
-        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/modeling_fuyu.py#L311-L322
-        if image_patches is not None and past_key_values is None:
-            patch_embeddings = [
-                self.vision_embed_tokens(patch.to(self.vision_embed_tokens.weight.dtype))
-                .squeeze(0)
-                .to(inputs_embeds.device)
-                for patch in image_patches
-            ]
-            inputs_embeds = self.gather_continuous_embeddings(
-                word_embeddings=inputs_embeds,
-                continuous_embeddings=patch_embeddings,
-                image_patch_input_indices=image_patches_indices,
-            )
-        ```
-
-    The number of placeholder feature tokens for the `i`th item in the batch is `patch_embeddings[i].shape[0]`,
-    which is the same as `image_patches[i].shape[0]`, i.e. `num_total_patches`.
-
-    Unlike LLaVA, Fuyu does not define the number of patches inside the modeling file. Where can we get more information?
-    Considering that the model input comes from the output of `FuyuProcessor`, let's **look at the preprocessing files**.
-
-    The image outputs are obtained by calling `FuyuImageProcessor.preprocess` and then
-    `FuyuImageProcessor.preprocess_with_tokenizer_info` inside `FuyuProcessor`.
-
-    In `FuyuImageProcessor.preprocess`, the images are resized and padded to the target `FuyuImageProcessor.size`,
-    returning the dimensions after resizing (but before padding) as metadata.
-
-    ??? code
-
-        ```python
-        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L541-L544
-        image_encoding = self.image_processor.preprocess(images, **output_kwargs["images_kwargs"])
-        batch_images = image_encoding["images"]
-        image_unpadded_heights = image_encoding["image_unpadded_heights"]
-        image_unpadded_widths = image_encoding["image_unpadded_widths"]
-
-        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L480-L
-        if do_resize:
-            batch_images = [
-                [self.resize(image, size=size, input_data_format=input_data_format) for image in images]
-                for images in batch_images
-            ]
-
-        image_sizes = [get_image_size(images[0], channel_dim=input_data_format) for images in batch_images]
-        image_unpadded_heights = [[image_size[0]] for image_size in image_sizes]
-        image_unpadded_widths = [[image_size[1]] for image_size in image_sizes]
-
-        if do_pad:
-            batch_images = [
-                [
-                    self.pad_image(
-                        image,
-                        size=size,
-                        mode=padding_mode,
-                        constant_values=padding_value,
-                        input_data_format=input_data_format,
-                    )
-                    for image in images
-                ]
-                for images in batch_images
-            ]
-        ```
-
-    In `FuyuImageProcessor.preprocess_with_tokenizer_info`, the images are split into patches based on this metadata:
-
-    ??? code
-
-        ```python
-        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L417-L425
-        model_image_input = self.image_processor.preprocess_with_tokenizer_info(
-            image_input=tensor_batch_images,
-            image_present=image_present,
-            image_unpadded_h=image_unpadded_heights,
-            image_unpadded_w=image_unpadded_widths,
-            image_placeholder_id=image_placeholder_id,
-            image_newline_id=image_newline_id,
-            variable_sized=True,
-        )
-
-        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L638-L658
-        image_height, image_width = image.shape[1], image.shape[2]
-        if variable_sized:  # variable_sized=True
-            new_h = min(
-                image_height,
-                math.ceil(image_unpadded_h[batch_index, subseq_index] / patch_height) * patch_height,
-            )
-            new_w = min(
-                image_width,
-                math.ceil(image_unpadded_w[batch_index, subseq_index] / patch_width) * patch_width,
-            )
-            image = image[:, :new_h, :new_w]
-            image_height, image_width = new_h, new_w
-
-        num_patches = self.get_num_patches(image_height=image_height, image_width=image_width)
-        tensor_of_image_ids = torch.full(
-            [num_patches], image_placeholder_id, dtype=torch.int32, device=image_input.device
-        )
-        patches = self.patchify_image(image=image.unsqueeze(0)).squeeze(0)
-        assert num_patches == patches.shape[0]
-        ```
-
-    The number of patches is in turn defined by `FuyuImageProcessor.get_num_patches`:
-
-    ??? code
-
-        ```python
-        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L552-L562
-        patch_size = patch_size if patch_size is not None else self.patch_size
-        patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
-
-        if image_height % patch_height != 0:
-            raise ValueError(f"{image_height=} must be divisible by {patch_height}")
-        if image_width % patch_width != 0:
-            raise ValueError(f"{image_width=} must be divisible by {patch_width}")
-
-        num_patches_per_dim_h = image_height // patch_height
-        num_patches_per_dim_w = image_width // patch_width
-        num_patches = num_patches_per_dim_h * num_patches_per_dim_w
-        ```
-
-    These image patches correspond to placeholder tokens (`|SPEAKER|`). So, we just need to maximize the number of image patches. Since input images are first resized
-    to fit within `image_processor.size`, we can maximize the number of image patches by inputting an image with size equal to `image_processor.size`.
-
-    ```python
-    def get_image_size_with_most_features(self) -> ImageSize:
-        image_processor = self.get_image_processor()
-        return ImageSize(
-            width=image_processor.size["width"],
-            height=image_processor.size["height"],
-        )
-    ```
-
-    Fuyu does not expect image placeholders in the inputs to HF processor, so
-    the dummy prompt text is empty regardless of the number of images.
+    Unlike LLaVA, PaliGemma's HF processor does not expect image placeholder
+    tokens in the input prompt; the placeholder feature tokens are instead
+    inserted afterwards (see [Prompt updates](#prompt-updates)). So the dummy
+    prompt text is empty regardless of the number of images:
 
     ```python
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
         return ""
     ```
 
-    For the multimodal image profiling data, the logic is very similar to LLaVA:
+    PaliGemma resizes every image to a square of `vision_config.image_size`, so
+    the number of placeholder feature tokens per image is fixed at
+    `(image_size // patch_size) ** 2`. This is computed by the SigLIP vision
+    encoder that PaliGemma uses:
+
+    ??? code
+
+        ```python
+        # vllm/model_executor/models/siglip.py
+        class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
+            def get_num_image_tokens(
+                self,
+                *,
+                image_width: int,
+                image_height: int,
+            ) -> int:
+                return self.get_patch_grid_length() ** 2
+
+            def get_patch_grid_length(self) -> int:
+                image_size, patch_size = self.get_image_size(), self.get_patch_size()
+                return image_size // patch_size
+        ```
+
+    Since the number of image tokens doesn't depend on the input image dimensions,
+    we can simply use a dummy image of the model's expected input size for the
+    multimodal profiling data:
 
     ??? code
 
@@ -482,16 +372,18 @@ Assuming that the memory usage increases with the number of tokens, the dummy in
             mm_counts: Mapping[str, int],
             mm_options: Mapping[str, BaseDummyOptions],
         ) -> MultiModalDataDict:
-            target_width, target_height = \
-                self.info.get_image_size_with_most_features()
+            hf_config = self.info.get_hf_config()
+            vision_config = hf_config.vision_config
+            max_image_size = vision_config.image_size
+
             num_images = mm_counts.get("image", 0)
 
             image_overrides = mm_options.get("image")
 
             return {
                 "image": self._get_dummy_images(
-                    width=target_width,
-                    height=target_height,
+                    width=max_image_size,
+                    height=max_image_size,
                     num_images=num_images,
                     overrides=image_overrides,
                 )
@@ -545,28 +437,15 @@ return a schema of the tensors outputted by the HF processor that are related to
         Our [actual code](../../../vllm/model_executor/models/llava.py) additionally supports
         pre-computed image embeddings, which can be passed to be model via the `image_embeds` argument.
 
-=== "With postprocessing: Fuyu"
+=== "With postprocessing: Mistral3"
 
-    The `image_patches` output of `FuyuImageProcessor.preprocess_with_tokenizer_info` concatenates
-    the patches from each image belonging to an item in the batch:
+    The `pixel_values` output of Mistral3's HF processor pads every image in the
+    batch to a common size, so that they can be stacked into a single tensor.
 
-    ```python
-    # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L673-L679
-            image_input_ids.append(tensor_of_image_ids)
-            image_patches.append(patches)
-        else:
-            image_input_ids.append(torch.tensor([], dtype=torch.int32, device=image_input.device))
-
-    batch_image_input_ids.append(image_input_ids)
-    batch_image_patches.append(image_patches)
-    ```
-
-    The shape of `image_patches` outputted by `FuyuImageProcessor` is therefore
-    `(1, num_images, num_patches, patch_width * patch_height * num_channels)`.
-
-    In order to support the use of
-    [MultiModalFieldConfig.batched][vllm.multimodal.inputs.MultiModalFieldConfig.batched]
-    like in LLaVA, we remove the extra batch dimension by overriding
+    To use [MultiModalFieldConfig.batched][vllm.multimodal.inputs.MultiModalFieldConfig.batched]
+    like in LLaVA, each image's features must be independent of the others (which
+    is also required for prefix caching to work correctly). So, we un-pad each image
+    back to its own size by overriding
     [BaseMultiModalProcessor._call_hf_processor][vllm.multimodal.processing.BaseMultiModalProcessor._call_hf_processor]:
 
     ??? code
@@ -586,33 +465,27 @@ return a schema of the tensors outputted by the HF processor that are related to
                 tok_kwargs=tok_kwargs,
             )
 
-            image_patches = processed_outputs.get("image_patches")
-            if image_patches is not None:
-                images = mm_data["images"]
-                assert isinstance(images, list)
+            pixel_values = processed_outputs.get("pixel_values")
+            if pixel_values is not None:
+                # Avoid padding since we need the output for each image to be
+                # independent of other images for the cache to work correctly
+                image_sizes = processed_outputs["image_sizes"]
+                assert len(pixel_values) == len(image_sizes)
 
-                # Original output: (1, num_images, Pn, Px * Py * C)
-                # New output: (num_images, Pn, Px * Py * C)
-                assert (isinstance(image_patches, list)
-                        and len(image_patches) == 1)
-                assert (isinstance(image_patches[0], torch.Tensor)
-                        and len(image_patches[0]) == len(images))
-
-                processed_outputs["image_patches"] = image_patches[0]
+                processed_outputs["pixel_values"] = [
+                    p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
+                ]
 
             return processed_outputs
         ```
 
-    !!! note
-        Our [actual code](../../../vllm/model_executor/models/fuyu.py) has special handling
-        for text-only inputs to prevent unnecessary warnings from HF processor.
-
     !!! note
         The `_call_hf_processor` method specifies both `mm_kwargs` and `tok_kwargs` for
         processing. `mm_kwargs` is used to both initialize and call the huggingface
         processor, whereas `tok_kwargs` is only used to call the huggingface processor.
 
-    This lets us override [_get_mm_fields_config][vllm.multimodal.processing.BaseMultiModalProcessor._get_mm_fields_config] as follows:
+    Since `pixel_values` is now a list with one tensor per image, we can override
+    [_get_mm_fields_config][vllm.multimodal.processing.BaseMultiModalProcessor._get_mm_fields_config] as follows:
 
     ```python
     def _get_mm_fields_config(
@@ -620,9 +493,15 @@ return a schema of the tensors outputted by the HF processor that are related to
         hf_inputs: BatchFeature,
         hf_processor_mm_kwargs: Mapping[str, object],
     ) -> Mapping[str, MultiModalFieldConfig]:
-        return dict(image_patches=MultiModalFieldConfig.batched("image"))
+        return dict(
+            pixel_values=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
     ```
 
+    !!! note
+        See our [actual code](../../../vllm/model_executor/models/mistral3.py) for the full implementation.
+
 ### Prompt updates
 
 Override [_get_prompt_updates][vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates] to
@@ -678,121 +557,53 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
             ]
         ```
 
-=== "Handling additional tokens: Fuyu"
+=== "Handling additional tokens: PaliGemma"
 
-    Recall the layout of feature tokens from Step 2:
-
-    ```
-    |SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
-    |SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
-    ...
-    |SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
-    ```
-
-    We define a helper function to return `ncols` and `nrows` directly:
+    PaliGemma's HF processor inserts, after the prompt's leading `<bos>` token, a
+    run of image tokens followed by a second `<bos>` token that marks the start of
+    the text prompt. We start by building the run of image tokens, one per
+    placeholder feature token:
 
     ??? code
 
         ```python
-        def get_image_feature_grid_size(
-            self,
-            *,
-            image_width: int,
-            image_height: int,
-        ) -> tuple[int, int]:
-            image_processor = self.get_image_processor()
-            target_width = image_processor.size["width"]
-            target_height = image_processor.size["height"]
-            patch_width = image_processor.patch_size["width"]
-            patch_height = image_processor.patch_size["height"]
-
-            if not (image_width <= target_width and image_height <= target_height):
-                height_scale_factor = target_height / image_height
-                width_scale_factor = target_width / image_width
-                optimal_scale_factor = min(height_scale_factor, width_scale_factor)
-
-                image_height = int(image_height * optimal_scale_factor)
-                image_width = int(image_width * optimal_scale_factor)
-
-            ncols = math.ceil(image_width / patch_width)
-            nrows = math.ceil(image_height / patch_height)
-            return ncols, nrows
-        ```
-
-    Based on this, we can initially define our replacement tokens as:
-
-    ??? code
-
-        ```python
-        def get_replacement(item_idx: int):
-            images = mm_items.get_items("image", ImageProcessorItems)
-            image_size = images.get_image_size(item_idx)
-
-            ncols, nrows = self.info.get_image_feature_grid_size(
-                image_width=image_size.width,
-                image_height=image_size.height,
+        def get_insertion(item_idx: int):
+            images = mm_items.get_items(
+                "image", (ImageEmbeddingItems, ImageProcessorItems)
             )
 
-            # `_IMAGE_TOKEN_ID` corresponds to `|SPEAKER|`
-            # `_NEWLINE_TOKEN_ID` corresponds to `|NEWLINE|`
-            return ([_IMAGE_TOKEN_ID] * ncols + [_NEWLINE_TOKEN_ID]) * nrows
+            if isinstance(images, ImageEmbeddingItems):
+                num_image_tokens = images.get_feature_size(item_idx)
+            else:
+                image_size = images.get_image_size(item_idx)
+                num_image_tokens = self.info.get_num_image_tokens(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                )
+
+            image_tokens = [image_token_id] * num_image_tokens
+            ...
         ```
 
-    However, this is not entirely correct. After `FuyuImageProcessor.preprocess_with_tokenizer_info` is called,
-    a BOS token (`<s>`) is also added to the prompt:
+    The trailing `<bos>` token is an additional token that must **not** receive a
+    vision embedding. To assign the vision embeddings to only the image tokens,
+    instead of returning the token ids directly you can return an instance of
+    [PromptUpdateDetails][vllm.multimodal.processing.PromptUpdateDetails] and mark
+    the embedding tokens with `embed_token_id`:
 
     ??? code
 
         ```python
-        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/processing_fuyu.py#L417-L435
-        model_image_input = self.image_processor.preprocess_with_tokenizer_info(
-            image_input=tensor_batch_images,
-            image_present=image_present,
-            image_unpadded_h=image_unpadded_heights,
-            image_unpadded_w=image_unpadded_widths,
-            image_placeholder_id=image_placeholder_id,
-            image_newline_id=image_newline_id,
-            variable_sized=True,
-        )
-        prompt_tokens, prompts_length = _tokenize_prompts_with_image_and_batch(
-            tokenizer=self.tokenizer,
-            prompts=prompts,
-            scale_factors=scale_factors,
-            max_tokens_to_generate=self.max_tokens_to_generate,
-            max_position_embeddings=self.max_position_embeddings,
-            add_BOS=True,
-            add_beginning_of_answer_token=True,
+        return PromptUpdateDetails.select_token_id(
+            image_tokens + [bos_token_id],
+            embed_token_id=image_token_id,
         )
         ```
 
-    To assign the vision embeddings to only the image tokens, instead of a string
-    you can return an instance of [PromptUpdateDetails][vllm.multimodal.processing.PromptUpdateDetails]:
-
-    ??? code
-
-        ```python
-        hf_config = self.info.get_hf_config()
-        bos_token_id = hf_config.bos_token_id  # `<s>`
-        assert isinstance(bos_token_id, int)
-
-        def get_replacement_fuyu(item_idx: int):
-            images = mm_items.get_items("image", ImageProcessorItems)
-            image_size = images.get_image_size(item_idx)
-
-            ncols, nrows = self.info.get_image_feature_grid_size(
-                image_width=image_size.width,
-                image_height=image_size.height,
-            )
-            image_tokens = ([_IMAGE_TOKEN_ID] * ncols + [_NEWLINE_TOKEN_ID]) * nrows
-
-            return PromptUpdateDetails.select_token_id(
-                image_tokens + [bos_token_id],
-                embed_token_id=_IMAGE_TOKEN_ID,
-            )
-        ```
-
-    Finally, noticing that the HF processor removes the `|ENDOFTEXT|` token from the tokenized prompt,
-    we can search for it to conduct the replacement at the start of the string:
+    Putting it together, we override [_get_prompt_updates][vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates].
+    Since these tokens are inserted (rather than replacing an existing placeholder)
+    after the prompt's leading `<bos>`, we use [PromptInsertion][vllm.multimodal.processing.PromptInsertion]
+    with a prefix target:
 
     ??? code
 
@@ -804,33 +615,41 @@ Each [PromptUpdate][vllm.multimodal.processing.PromptUpdate] instance specifies
             out_mm_kwargs: MultiModalKwargsItems,
         ) -> Sequence[PromptUpdate]:
             hf_config = self.info.get_hf_config()
-            bos_token_id = hf_config.bos_token_id
-            assert isinstance(bos_token_id, int)
+            image_token_id = hf_config.image_token_index
 
             tokenizer = self.info.get_tokenizer()
-            eot_token_id = tokenizer.bos_token_id
-            assert isinstance(eot_token_id, int)
 
-            def get_replacement_fuyu(item_idx: int):
-                images = mm_items.get_items("image", ImageProcessorItems)
-                image_size = images.get_image_size(item_idx)
+            bos_token_id = tokenizer.bos_token_id
+            assert isinstance(bos_token_id, int)
 
-                ncols, nrows = self.info.get_image_feature_grid_size(
-                    image_width=image_size.width,
-                    image_height=image_size.height,
+            def get_insertion(item_idx: int):
+                images = mm_items.get_items(
+                    "image", (ImageEmbeddingItems, ImageProcessorItems)
                 )
-                image_tokens = ([_IMAGE_TOKEN_ID] * ncols + [_NEWLINE_TOKEN_ID]) * nrows
+
+                if isinstance(images, ImageEmbeddingItems):
+                    num_image_tokens = images.get_feature_size(item_idx)
+                else:
+                    image_size = images.get_image_size(item_idx)
+                    num_image_tokens = self.info.get_num_image_tokens(
+                        image_width=image_size.width,
+                        image_height=image_size.height,
+                    )
+
+                image_tokens = [image_token_id] * num_image_tokens
 
                 return PromptUpdateDetails.select_token_id(
                     image_tokens + [bos_token_id],
-                    embed_token_id=_IMAGE_TOKEN_ID,
+                    embed_token_id=image_token_id,
                 )
 
             return [
-                PromptReplacement(
+                PromptInsertion(
                     modality="image",
-                    target=[eot_token_id],
-                    replacement=get_replacement_fuyu,
+                    target=PromptIndexTargets.prefix(
+                        [bos_token_id] if tokenizer.add_bos_token else []
+                    ),
+                    insertion=get_insertion,
                 )
             ]
         ```
@@ -873,7 +692,7 @@ Examples:
 Examples:
 
 - Chameleon (appends `sep_token`): [vllm/model_executor/models/chameleon.py](../../../vllm/model_executor/models/chameleon.py)
-- Fuyu (appends `boa_token`): [vllm/model_executor/models/fuyu.py](../../../vllm/model_executor/models/fuyu.py)
+- Molmo2 (prepends `bos_token`): [vllm/model_executor/models/molmo2.py](../../../vllm/model_executor/models/molmo2.py)
 - Molmo (applies chat template which is not defined elsewhere): [vllm/model_executor/models/molmo.py](../../../vllm/model_executor/models/molmo.py)
 
 ### Custom HF processor

docs/design/endpoint_plugins.md

@@ -0,0 +1,136 @@
+# Endpoint Plugins
+
+Endpoint plugins let out-of-tree packages add HTTP routes to the OpenAI compatible API server without editing `vllm/entrypoints/openai/api_server.py`. Their scope is
+the **HTTP surface only** registering routes and optionally per app state used by those routes. A plugin reaches the engine the same way an in-tree serving handler does, through the `EngineClient` it is handed at startup (e.g. `engine_client.collective_rpc(...)`). No new engine access path is introduced.
+
+!!! warning "Security"
+    Endpoint plugins are **not loaded by default** and must be explicitly allowlisted. Read [Endpoint Plugins security posture](../usage/security.md#endpoint-plugins) before enabling one, especially the route shadowing warning.
+
+## The `EndpointPlugin` protocol
+
+Endpoint plugins implement the [`EndpointPlugin`][vllm.plugins.endpoint_plugins.interface.EndpointPlugin] runtime checkable `Protocol`:
+
+```python
+class EndpointPlugin(Protocol):
+    name: str
+    required_tasks: tuple[SupportedTask, ...] | None
+
+    def attach_router(self, app: FastAPI) -> None: ...
+
+    async def init_state(
+        self, engine_client: EngineClient | None, state: State, args: Namespace
+    ) -> None: ...
+```
+
+- `name`: a unique identifier used in logs and for `VLLM_PLUGINS` allowlisting
+- `required_tasks`: the tasks the server must support for this plugin to load. `None` means the plugin has no task requirement
+- `attach_router`: registers routes on `app`
+- `init_state`: initializes per app state the routes read at request time
+
+## The two phase lifecycle
+
+Routes are registered before the engine exists. This means the interface has to expose two hooks that run at two different points in server startup:
+
+| Phase | Called from | `engine_client` available? | Work |
+| --- | --- | --- | --- |
+| A. Route registration | `build_app()` | No | `attach_router(app)` add routes. Do not touch the engine here. |
+| B. State init | `init_app_state()` | Usually but `None` on the CPU only render server | `init_state(engine_client, state, args)` build a serving handler holding `engine_client` and store it on `state`. |
+
+Because `app.state` *is* the `state` object passed to `init_app_state()`, an object stored during phase A is visible in phase B and an object stored in phase B is visible to route handlers at request time via `request.app.state`. This is the same pattern in-tree endpoints already use.
+
+### Engine less servers (the render server)
+
+The CPU only render server (`init_render_app_state()`) has no `EngineClient`. It still runs both phases for any plugin eligible for the `render` task (`required_tasks` is `None` or includes `"render"`). `attach_router` is called as usual but `init_state` is called with `engine_client=None`.
+
+A plugin that needs an engine to function has two options:
+
+- Exclude `"render"` from `required_tasks` so it is never loaded on the render server in the first place
+- Accept being loaded on `render` and check for `None` in `init_state` or in the route handler returning an error response (e.g. HTTP 503) instead of dereferencing a client that doesn't exist
+
+`tests/plugins/vllm_add_dummy_endpoint_plugin` demonstrates the second option. Its route handler returns a 503 when `state.dummy_engine_client` is `None`.
+
+### Reaching the engine from a route handler
+
+`init_state` is where a plugin captures `engine_client` into a small serving handler and stashes it on `state`. The route added in `attach_router` reads that handler off `request.app.state` at request time and calls the engine through it, typically via `engine_client.collective_rpc(...)`.
+
+This minimal example omits the `None` check from the previous section for brevity since `required_tasks` is `None` here. It is in fact eligible for `render` and should handle `engine_client=None` the way `tests/plugins/vllm_add_dummy_endpoint_plugin` does before shipping it:
+
+```python
+from fastapi import FastAPI, Request
+
+
+class MyAdminEndpointPlugin:
+    name = "my_admin_endpoint_plugin"
+    required_tasks: tuple[str, ...] | None = None
+
+    def attach_router(self, app: FastAPI) -> None:
+        @app.get("/plugins/my_admin_endpoint_plugin/scheduler_config")
+        async def scheduler_config(raw_request: Request):
+            engine_client = raw_request.app.state.my_engine_client
+            results = await engine_client.collective_rpc("get_scheduler_config")
+            return {"scheduler_config": results}
+
+    async def init_state(self, engine_client, state, args) -> None:
+        state.my_engine_client = engine_client
+```
+
+A complete and tested version of this example is in-repo as `tests/plugins/vllm_add_dummy_endpoint_plugin` and is exercised e2e (including a real HTTP request) in `tests/plugins_tests/test_endpoint_plugins.py`.
+
+## Registering the entry point
+
+Register a zero argument factory (a class or function) under the `vllm.endpoint_plugins` group. The factory must return an object satisfying `EndpointPlugin`:
+
+```toml
+# pyproject.toml
+[project.entry-points."vllm.endpoint_plugins"]
+my_admin_api = "my_pkg.endpoints:MyAdminEndpointPlugin"
+```
+
+```python
+# setup.py equivalent
+setup(
+    name="my_pkg",
+    entry_points={
+        "vllm.endpoint_plugins": [
+            "my_admin_api = my_pkg.endpoints:MyAdminEndpointPlugin"
+        ]
+    },
+)
+```
+
+The entry point name (`my_admin_api` above) is independent of the plugin's `name` attribute. `VLLM_PLUGINS` allowlisting matches on the **entry point name** following the same convention as `vllm.general_plugins` (see [Plugin System](plugin_system.md)).
+
+## Gating: `VLLM_PLUGINS` and `required_tasks`
+
+Endpoint plugins are discovered and gated by [`load_endpoint_plugins`][vllm.plugins.load_endpoint_plugins] which is stricter than the loader used for other plugin groups:
+
+- **Nothing loads unless `VLLM_PLUGINS` is set and names the plugin.** Other plugin groups load everything unless `VLLM_PLUGINS` narrows the set. Endpoint plugins invert that default because they add network exposed surface. See [Security](../usage/security.md#endpoint-plugins).
+- **`required_tasks` must intersect the server's supported tasks** unless it is `None`. Use this to keep a plugin from attaching routes on a server that can't service them (e.g. a pooling only deployment).
+- A factory that raises an issue during instantiation is logged and skipped. It does not abort server startup.
+
+Only the front end API server process loads endpoint plugins. There is no need to guard for worker or engine core processes.
+
+## Pairing with `vllm.general_plugins`
+
+Endpoint plugins cover the HTTP surface only. If a plugin also needs new engine side behavior (a new worker-side RPC method, a custom stat) that half ships separately through the existing `vllm.general_plugins` group which loads in worker processes (see [Plugin System](plugin_system.md)). The two entry points are registered and loaded **independently**. Neither implies the other. The recommended distribution shape is a single package exposing both:
+
+```toml
+[project.entry-points."vllm.general_plugins"]
+my_admin_engine = "my_pkg.engine:register"      # adds the worker side method
+
+[project.entry-points."vllm.endpoint_plugins"]
+my_admin_api = "my_pkg.endpoints:MyAdminEndpointPlugin"  # adds the HTTP route
+```
+
+Do not expect a single endpoint plugin to also mutate engine/worker state. If your route needs a worker side method that doesn't already exist then add it via a paired `general_plugins` entry point.
+
+## Path-prefix convention
+
+There is currently no route conflict enforcement (tracked as a follow-up to RFC [#46565](https://github.com/vllm-project/vllm/issues/46565)). A plugin's `attach_router` can register a path that collides with a core route and routes attached later win. To avoid surprising operators:
+
+- Namespace your routes under a distinct prefix, e.g. `/plugins/<plugin-name>/...`, rather than reusing `/v1/...` or other core prefixes
+- Only register routes under a core prefix (like the worked example's `/v1/admin/scheduler_config`) if you specifically intend to override or extend existing behavior and document that clearly for operators allowlisting your plugin
+
+## Compatibility
+
+`state`/serving handler internals (e.g. the shape of in-tree `OpenAIServing*` classes) are not a stable public contract yet. Treat them as use-at-your-own-risk and expect them to change between vLLM versions. `FastAPI`, `EngineClient` and the `EndpointPlugin` protocol itself are the supported surface.

docs/design/plugin_system.md

@@ -53,6 +53,8 @@ Every plugin has three parts:
 
 - **Stat logger plugins** (with group name `vllm.stat_logger_plugins`): The primary use case for these plugins is to register custom, out-of-the-tree loggers into vLLM. The entry point should be a class that subclasses StatLoggerBase.
 
+- **Endpoint plugins** (with group name `vllm.endpoint_plugins`): The primary use case for these plugins is to register custom, out-of-the-tree HTTP routes on the OpenAI compatible API server. Unlike the other plugin groups above, endpoint plugins are loaded only in the API server front end process and are **not loaded by default**. See [Endpoint Plugins](endpoint_plugins.md) for the interface and [Security](../usage/security.md#endpoint-plugins) for the opt-in and trust model.
+
 ## Guidelines for Writing Plugins
 
 - **Being re-entrant**: The function specified in the entry point should be re-entrant, meaning it can be called multiple times without causing issues. This is necessary because the function might be called multiple times in some processes.

docs/features/kv_offloading_usage.md

@@ -81,6 +81,8 @@ vllm serve <model> \
 
 Each entry in `secondary_tiers` is a dict with a required `type` field plus tier-specific fields.
 
+The filesystem and object-store tiers can publish hash-only `BlockStored` KV events for blocks they successfully store, tagged with a stable per-tier `medium` (`FS` for the filesystem tier, `OBJ` for the object-store tier). Set `enable_kv_events: true` in the tier's entry to opt in; events are published only when KV cache events are also enabled globally via `--kv-events-config`.
+
 ### Filesystem (FS)
 
 The filesystem tier (`type: "fs"`) writes blocks to a directory on local storage.
@@ -91,6 +93,7 @@ The filesystem tier (`type: "fs"`) writes blocks to a directory on local storage
 | `root_dir` | yes | — | Base directory; vLLM creates subdirectories beneath it (see [On-Disk Layout](#on-disk-layout)). |
 | `n_read_threads` | no | `16` | Read-priority I/O threads (load path). |
 | `n_write_threads` | no | `16` | Write-priority I/O threads (store path). |
+| `enable_kv_events` | no | `false` | Publish `BlockStored` KV events (medium `FS`) for successfully stored blocks. Requires KV cache events to be enabled globally. |
 
 Each thread group prefers its own queue but pulls from the other when its primary queue is empty, so a write-heavy or read-heavy burst won't leave the off-priority queue waiting. Size the totals to your storage's effective concurrency.
 
@@ -120,6 +123,31 @@ To enable KV cache sharing between multiple vLLM instances using the same `root_
 PYTHONHASHSEED=0 vllm serve ...
 ```
 
+### Object Store (OBJ)
+
+The object-store tier (`type: "obj"`) offloads blocks to an S3-compatible object store through the NIXL OBJ backend.
+
+| Key | Required | Default | Notes |
+| --- | --- | --- | --- |
+| `type` | yes | — | Must be `obj`. |
+| `store_config` | yes | — | Object store connection parameters (see below). |
+| `prefix` | no | `""` | Key prefix prepended to all object keys. |
+| `io_threads` | no | `4` | Number of NIXL OBJ backend I/O threads. |
+| `enable_kv_events` | no | `false` | Publish `BlockStored` KV events (medium `OBJ`) for successfully stored blocks. Requires KV cache events to be enabled globally. |
+
+`store_config` fields:
+
+| Key | Required | Default | Notes |
+| --- | --- | --- | --- |
+| `bucket` | yes | — | Bucket name. |
+| `endpoint_override` | yes | — | Object store endpoint host; the URL scheme is set separately via `scheme`. |
+| `scheme` | no | `http` | `http` or `https`. |
+| `access_key`, `secret_key`, `session_token` | no | `""` | Explicit credentials. When left empty, the NIXL OBJ plugin falls back to the AWS SDK default credential provider chain (IAM roles, environment variables, credential files), which enables workload-identity auth on Kubernetes. |
+| `region` | no | `""` | Bucket region, if the endpoint requires one. |
+| `ca_bundle` | no | `""` | CA bundle path for TLS verification. |
+
+Object keys follow the same run-configuration digest scheme as the filesystem tier (see [On-Disk Layout](#on-disk-layout)) and are stored under the optional `prefix`. The [Cross-Process Sharing](#cross-process-sharing) requirement (`PYTHONHASHSEED`) applies to shared buckets as well, so instances sharing a bucket produce identical keys for identical content. At startup the tier probes object store connectivity and fails fast with a configuration error if the bucket is unreachable.
+
 ### P2P (Including P/D)
 
 The P2P tier (`type: "p2p"`) shares completed KV blocks between vLLM instances over RDMA via NIXL. Each instance binds a control socket on `host:port` and exchanges blocks directly with peers — no shared filesystem required.

docs/features/multimodal_inputs.md

@@ -879,6 +879,55 @@ vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
 
 Works with common video formats like MP4 when using OpenCV backends.
 
+#### GPU Video Decoding with DeepStream (NVDEC)
+
+By default vLLM decodes video on the CPU. On NVIDIA GPUs you can instead decode
+directly on the hardware video engine (NVDEC) with the DeepStream backend, which
+keeps decoding off the CPU and can significantly increase video throughput.
+
+Install the backend (Linux x86-64 only):
+
+```bash
+pip install vllm[deepstream]
+```
+
+The pip wheel bundles the DeepStream libraries but still relies on a few system
+packages that pip cannot install. On Ubuntu:
+
+```bash
+apt-get install -y \
+  gstreamer1.0-tools gstreamer1.0-plugins-base gstreamer1.0-plugins-good \
+  gstreamer1.0-plugins-bad gstreamer1.0-libav \
+  python3-gi python3-gst-1.0 libv4l-0 cuda-libraries-13-0
+```
+
+Select the backend either with an environment variable:
+
+```bash
+export VLLM_VIDEO_LOADER_BACKEND=deepstream
+vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct
+```
+
+or per request via `--media-io-kwargs`:
+
+```bash
+vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
+  --media-io-kwargs '{"video": {"backend": "deepstream"}}'
+```
+
+**Parameters:**
+
+- `pool_size`: Number of GPU decode workers in the process-wide decode pool
+  (clamped to `[1, 16]`). When unset it defaults to
+  `VLLM_MEDIA_LOADING_THREAD_COUNT` (default `8`). The pool is a singleton, so
+  the first request's value wins.
+
+```bash
+# Example: 12 decode workers
+vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
+  --media-io-kwargs '{"video": {"backend": "deepstream", "pool_size": 12}}'
+```
+
 #### Pre-extracted Frame Sequences with `media_io_kwargs`
 
 When you extract video frames on the client side and send them as `video/jpeg` (base64-concatenated JPEG frames), you can preserve the original video metadata by using `media_io_kwargs` in your request. This enables more accurate video understanding by preserving temporal information that would otherwise be lost during client-side frame extraction.

docs/features/reasoning_outputs.md

@@ -351,6 +351,69 @@ print(response.choices[0].message.reasoning)
 print(response.choices[0].message.content)
 ```
 
+## Suppressing Reasoning Output
+
+You can suppress reasoning content from API responses using the `include_reasoning` parameter. When set to `false`, reasoning tokens are still generated (so model quality is unaffected) but excluded from the response. This reduces network traffic without changing inference behavior.
+
+The parameter is supported in both the Chat Completions API and the Responses API, for streaming and non-streaming requests.
+
+When `include_reasoning=false`, vLLM also suppresses per-token metadata (logprobs and token IDs) to prevent leaking reasoning content through decoded token text in logprob entries or raw token IDs.
+
+### Chat Completions API
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+model = client.models.list().data[0].id
+
+# Reasoning is included by default (include_reasoning=True)
+response = client.chat.completions.create(
+    model=model,
+    messages=[{"role": "user", "content": "What is 15 * 37?"}],
+    extra_body={"include_reasoning": False},
+)
+
+msg = response.choices[0].message
+assert msg.content  # Content is still present
+assert not getattr(msg, "reasoning", None)  # Reasoning is suppressed
+```
+
+Streaming works the same way, reasoning deltas are omitted from chunks:
+
+```python
+stream = client.chat.completions.create(
+    model=model,
+    messages=[{"role": "user", "content": "What is 15 * 37?"}],
+    stream=True,
+    extra_body={"include_reasoning": False},
+)
+
+for chunk in stream:
+    delta = chunk.choices[0].delta
+    # delta.reasoning will always be None
+    if delta.content:
+        print(delta.content, end="", flush=True)
+```
+
+### Responses API
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+
+response = client.responses.create(
+    model=client.models.list().data[0].id,
+    input="What is 15 * 37?",
+    include_reasoning=False,
+)
+
+# No "reasoning" items in output
+types = [item.type for item in response.output]
+assert "reasoning" not in types
+```
+
 ## Limitations
 
 - The reasoning content is only available for online serving's chat completion endpoint (`/v1/chat/completions`), Anthropic Messages API (`/v1/messages`) and the Responses API (`/v1/responses`).

docs/getting_started/installation/cpu.arm.inc.md

@@ -20,12 +20,12 @@ Pre-built vLLM wheels for Arm are available since version 0.11.2. These wheels c
 
 ```bash
 export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
-uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl --torch-backend cpu
+uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_34_aarch64.whl --torch-backend cpu
 ```
 
 ??? console "pip"
     ```bash
-    pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl --extra-index-url https://download.pytorch.org/whl/cpu
+    pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_34_aarch64.whl --extra-index-url https://download.pytorch.org/whl/cpu
     ```
 
 !!! warning "set `LD_PRELOAD`"
@@ -63,7 +63,7 @@ uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly/cpu --index
     If you insist on using `pip`, you have to specify the full URL (link address) of the wheel file (which can be obtained from https://wheels.vllm.ai/nightly/cpu/vllm).
 
     ```bash
-    pip install https://wheels.vllm.ai/4fa7ce46f31cbd97b4651694caf9991cc395a259/vllm-0.13.0rc2.dev104%2Bg4fa7ce46f.cpu-cp38-abi3-manylinux_2_35_aarch64.whl --extra-index-url https://download.pytorch.org/whl/cpu # current nightly build (the filename will change!)
+    pip install https://wheels.vllm.ai/2f3f441f84bd5b35ec8aa9fcfffb540f107da8a7/vllm-0.23.1rc1.dev901%2Bg2f3f441f8.cpu-cp38-abi3-manylinux_2_34_aarch64.whl --extra-index-url https://download.pytorch.org/whl/cpu # current nightly build (the filename will change!)
     ```
 
 #### Install specific revisions

docs/getting_started/installation/cpu.x86.inc.md

@@ -24,13 +24,13 @@ Pre-built vLLM wheels for x86 with AVX512/AVX2 are available since version 0.17.
 export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
 
 # use uv
-uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl --torch-backend cpu
+uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_34_x86_64.whl --torch-backend cpu
 ```
 
 ??? console "pip"
     ```bash
     # use pip
-    pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_35_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cpu
+    pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cpu-cp38-abi3-manylinux_2_34_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cpu
     ```
 !!! warning "set `LD_PRELOAD`"
     Before use vLLM CPU installed via wheels, make sure TCMalloc and Intel OpenMP are installed and added to `LD_PRELOAD`:

docs/getting_started/installation/gpu.cuda.inc.md

@@ -43,7 +43,7 @@ As of now, vLLM's binaries are compiled with CUDA 12.9 and public PyTorch releas
 export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
 export CUDA_VERSION=130 # or other
 export CPU_ARCH=$(uname -m) # x86_64 or aarch64
-uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu${CUDA_VERSION}-cp38-abi3-manylinux_2_35_${CPU_ARCH}.whl --extra-index-url https://download.pytorch.org/whl/cu${CUDA_VERSION}
+uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu${CUDA_VERSION}-cp38-abi3-manylinux_2_28_${CPU_ARCH}.whl --extra-index-url https://download.pytorch.org/whl/cu${CUDA_VERSION}
 ```
 
 #### Install the latest code
@@ -68,8 +68,8 @@ uv pip install -U vllm \
     If you insist on using `pip`, you have to specify the full URL of the wheel file (which can be obtained from the web page).
 
     ```bash
-    pip install -U https://wheels.vllm.ai/nightly/vllm-0.11.2.dev399%2Bg3c7461c18-cp38-abi3-manylinux_2_31_x86_64.whl # current nightly build (the filename will change!)
-    pip install -U https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-0.11.2.dev399%2Bg3c7461c18-cp38-abi3-manylinux_2_31_x86_64.whl # from specific commit
+    pip install -U https://wheels.vllm.ai/2f3f441f84bd5b35ec8aa9fcfffb540f107da8a7/vllm-0.23.1rc1.dev901%2Bg2f3f441f8-cp38-abi3-manylinux_2_28_x86_64.whl # current nightly build (the filename will change!)
+    pip install -U https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-0.23.1rc1.dev901%2Bg2f3f441f8-cp38-abi3-manylinux_2_28_x86_64.whl # from specific commit
     ```
 
 ##### Install specific revisions

docs/models/supported_models.md

@@ -421,8 +421,6 @@ th {
 | `MPTForCausalLM` | MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter | `mosaicml/mpt-7b`, `mosaicml/mpt-7b-storywriter`, `mosaicml/mpt-30b`, etc. | | ✅︎ |
 | `NemotronForCausalLM` | Nemotron-3, Nemotron-4, Minitron | `nvidia/Minitron-8B-Base`, `mgoin/Nemotron-4-340B-Base-hf-FP8`, etc. | ✅︎ | ✅︎ |
 | `NemotronHForCausalLM` | Nemotron-H | `nvidia/Nemotron-H-8B-Base-8K`, `nvidia/Nemotron-H-47B-Base-8K`, `nvidia/Nemotron-H-56B-Base-8K`, etc. | ✅︎ | ✅︎ |
-| `OlmoForCausalLM` | OLMo | `allenai/OLMo-1B-hf`, `allenai/OLMo-7B-hf`, etc. | ✅︎ | ✅︎ |
-| `Olmo2ForCausalLM` | OLMo2 | `allenai/OLMo-2-0425-1B`, etc. | ✅︎ | ✅︎ |
 | `Olmo3ForCausalLM` | OLMo3 | `allenai/Olmo-3-7B-Instruct`, `allenai/Olmo-3-32B-Think`, etc. | ✅︎ | ✅︎ |
 | `OlmoHybridForCausalLM` | OLMo Hybrid | `allenai/Olmo-Hybrid-7B` | ✅︎ | ✅︎ |
 | `OlmoeForCausalLM` | OLMoE | `allenai/OLMoE-1B-7B-0924`, `allenai/OLMoE-1B-7B-0924-Instruct`, etc. | | ✅︎ |
@@ -436,7 +434,6 @@ th {
 | `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ |
 | `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ |
 | `PhiMoEForCausalLM` | Phi-3.5-MoE | `microsoft/Phi-3.5-MoE-instruct`, etc. | ✅︎ | ✅︎ |
-| `PersimmonForCausalLM` | Persimmon | `adept/persimmon-8b-base`, `adept/persimmon-8b-chat`, etc. | | ✅︎ |
 | `Plamo2ForCausalLM` | PLaMo2 | `pfnet/plamo-2-1b`, `pfnet/plamo-2-8b`, etc. | ✅ | ✅︎ |
 | `Plamo3ForCausalLM` | PLaMo3 | `pfnet/plamo-3-nict-2b-base`, `pfnet/plamo-3-nict-8b-base`, etc. | ✅ | ✅︎ |
 | `Qwen2ForCausalLM` | QwQ, Qwen2 | `Qwen/QwQ-32B-Preview`, `Qwen/Qwen2-7B-Instruct`, `Qwen/Qwen2-7B`, etc. | ✅︎ | ✅︎ |
@@ -454,7 +451,6 @@ th {
 | `StableLMEpochForCausalLM` | StableLM Epoch | `stabilityai/stablelm-zephyr-3b`, etc. | | ✅︎ |
 | `Step1ForCausalLM` | Step-Audio | `stepfun-ai/Step-Audio-EditX`, etc. | ✅︎ | ✅︎ |
 | `Step3p5ForCausalLM` | Step-3.5-flash | `stepfun-ai/Step-3.5-Flash`, etc. | | ✅︎ |
-| `TeleChatForCausalLM` | TeleChat | `chuhac/TeleChat2-35B`, etc. | ✅︎ | ✅︎ |
 | `TeleChat2ForCausalLM` | TeleChat2 | `Tele-AI/TeleChat2-3B`, `Tele-AI/TeleChat2-7B`, `Tele-AI/TeleChat2-35B`, etc. | ✅︎ | ✅︎ |
 | `TeleChat3ForCausalLM` | TeleChat3 | `Tele-AI/TeleChat3-36B-Thinking`, `Tele-AI/TeleChat3-Coder-36B-Thinking`, etc. | ✅︎ | ✅︎ |
 | `TeleFLMForCausalLM` | TeleFLM | `CofeAI/FLM-2-52B-Instruct-2407`, `CofeAI/Tele-FLM`, etc. | ✅︎ | ✅︎ |
@@ -465,6 +461,8 @@ Some models are supported only via the [Transformers modeling backend](#transfor
 | Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
 | ------------ | ------ | ----------------- | -------------------- | ------------------------- |
 | `GPTBigCodeForCausalLM` | StarCoder, SantaCoder, WizardCoder | `bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, `WizardLM/WizardCoder-15B-V1.0`, etc. | ✅︎ | |
+| `OlmoForCausalLM` | OLMo | `allenai/OLMo-1B-hf`, `allenai/OLMo-7B-hf`, etc. | ✅︎ | ✅︎ |
+| `Olmo2ForCausalLM` | OLMo2 | `allenai/OLMo-2-0425-1B`, etc. | ✅︎ | ✅︎ |
 | `SmolLM3ForCausalLM` | SmolLM3 | `HuggingFaceTB/SmolLM3-3B` | ✅︎ | ✅︎ |
 | `Starcoder2ForCausalLM` | Starcoder2 | `bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc. | ✅︎ | ✅︎ |
 
@@ -514,14 +512,13 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `ChameleonForConditionalGeneration` | Chameleon | T + I | `facebook/chameleon-7b`, etc. | | ✅︎ |
 | `CheersForConditionalGeneration` | Cheers | T + I | `ai9stars/Cheers` | | ✅︎ |
 | `Cohere2VisionForConditionalGeneration` | Command A Vision, Command-A+ | T + I<sup>+</sup> | `CohereLabs/command-a-vision-07-2025`, `CohereLabs/command-a-plus-05-2026`, etc. | | ✅︎ |
-| `Cosmos3ForConditionalGeneration` | Cosmos3 (understanding tower) | T + I<sup>E+</sup> + V<sup>E+</sup> | `nvidia/Cosmos3-Nano` | | ✅︎ |
+| `Cosmos3ForConditionalGeneration` | Cosmos3 (understanding tower) | T + I<sup>E+</sup> + V<sup>E+</sup> | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | | ✅︎ |
 | `DeepseekVLV2ForCausalLM` | DeepSeek-VL2 | T + I<sup>+</sup> | `deepseek-ai/deepseek-vl2-tiny`, `deepseek-ai/deepseek-vl2-small`, `deepseek-ai/deepseek-vl2`, etc. | | ✅︎ |
 | `DeepseekOCRForCausalLM` | DeepSeek-OCR | T + I<sup>+</sup> | `deepseek-ai/DeepSeek-OCR`, etc. | ✅︎ | ✅︎ |
 | `DeepseekOCR2ForCausalLM` | DeepSeek-OCR-2 | T + I<sup>+</sup> | `deepseek-ai/DeepSeek-OCR-2`, etc. | ✅︎ | ✅︎ |
 | `Eagle2_5_VLForConditionalGeneration` | Eagle2.5-VL | T + I<sup>E+</sup> | `nvidia/Eagle2.5-8B`, etc. | ✅︎ | ✅︎ |
 | `Ernie4_5_VLMoeForConditionalGeneration` | Ernie4.5-VL | T + I<sup>+</sup>/ V<sup>+</sup> | `baidu/ERNIE-4.5-VL-28B-A3B-PT`, `baidu/ERNIE-4.5-VL-424B-A47B-PT` | | ✅︎ |
 | `Exaone4_5_ForConditionalGeneration` | EXAONE-4.5 | T + I<sup>E+</sup> | `LGAI-EXAONE/EXAONE-4.5-33B`, etc. | ✅︎ | ✅︎ |
-| `FuyuForCausalLM` | Fuyu | T + I | `adept/fuyu-8b`, etc. | | ✅︎ |
 | `Gemma3ForConditionalGeneration` | Gemma 3 | T + I<sup>E+</sup> | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc. | ✅︎ | ✅︎ |
 | `Gemma3nForConditionalGeneration` | Gemma 3n | T + I + A | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | |
 | `Gemma4ForConditionalGeneration` | Gemma 4 | T + I<sup>+</sup> + V + A<sup>*</sup> | `google/gemma-4-E2B-it`, etc. | | ✅︎ |
@@ -569,6 +566,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `MolmoForCausalLM` | Molmo | T + I<sup>+</sup> | `allenai/Molmo-7B-D-0924`, `allenai/Molmo-7B-O-0924`, etc. | ✅︎ | ✅︎ |
 | `Molmo2ForConditionalGeneration` | Molmo2 | T + I<sup>+</sup> / V | `allenai/Molmo2-4B`, `allenai/Molmo2-8B`, `allenai/Molmo2-O-7B`, `allenai/MolmoWeb-4B`<sup>^</sup>, `allenai/MolmoWeb-8B`<sup>^</sup> | ✅︎ | ✅︎ |
 | `MossAudioModel` | MOSS-Audio | T + A<sup>+</sup> | `OpenMOSS-Team/MOSS-Audio-4B-Instruct`, `OpenMOSS-Team/MOSS-Audio-4B-Thinking`, `OpenMOSS-Team/MOSS-Audio-8B-Instruct`, `OpenMOSS-Team/MOSS-Audio-8B-Thinking` | ✅︎ | ✅︎ |
+| `MossTranscribeDiarizeForConditionalGeneration` | MOSS-Transcribe-Diarize | T + A | `OpenMOSS-Team/MOSS-Transcribe-Diarize` | | ✅︎ |
 | `Moondream3ForCausalLM` | Moondream3 | T + I | `moondream/moondream3-preview` | | ✅︎ |
 | `NVLM_D_Model` | NVLM-D 1.0 | T + I<sup>+</sup> | `nvidia/NVLM-D-72B`, etc. | | ✅︎ |
 | `OpenCUAForConditionalGeneration` | OpenCUA-7B | T + I<sup>E+</sup> | `xlangai/OpenCUA-7B` | ✅︎ | ✅︎ |
@@ -671,6 +669,7 @@ Speech2Text models trained specifically for Automatic Speech Recognition.
 | `GlmAsrForConditionalGeneration` | GLM-ASR | `zai-org/GLM-ASR-Nano-2512` | ✅︎ | ✅︎ |
 | `GraniteSpeechForConditionalGeneration` | Granite Speech | `ibm-granite/granite-4.0-1b-speech`, `ibm-granite/granite-speech-3.3-2b`, etc. | ✅︎ | ✅︎ |
 | `GraniteSpeechPlusForConditionalGeneration` | Granite Speech Plus | `ibm-granite/granite-speech-4.1-2b-plus` | ✅︎ | ✅︎ |
+| `MossTranscribeDiarizeForConditionalGeneration` | MOSS-Transcribe-Diarize | `OpenMOSS-Team/MOSS-Transcribe-Diarize` | | ✅︎ |
 | `Qwen3ASRForConditionalGeneration` | Qwen3-ASR | `Qwen/Qwen3-ASR-1.7B`, etc. | ✅︎ | ✅︎ |
 | `Qwen3OmniMoeThinkerForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct`, etc. | | ✅︎ |
 | `VoxtralForConditionalGeneration` | Voxtral (Mistral format) | `mistralai/Voxtral-Mini-3B-2507`, `mistralai/Voxtral-Small-24B-2507`, etc. | ✅︎ | ✅︎ |

docs/serving/online_serving/README.md

@@ -170,6 +170,7 @@ For further details on Weight Transfer, please refer to [this page](../../traini
 - `/pause` - Pause generation (causes denial of service)
 - `/resume` - Resume generation
 - `/is_paused` - Check if generation is paused
+- `/abort_requests` - Abort in-flight requests (all in-flight, or the given `request_ids`) without pausing the scheduler
 - `/init_weight_transfer_engine` - Initialize weight transfer engine for RLHF
 - `/start_weight_update` - Prepares the inference engine for a weight update.
 - `/update_weights` - Update model weights (can alter model behavior)

docs/training/async_rl.md

@@ -42,6 +42,7 @@ When using the vLLM HTTP server, the same functionality is available via:
 
 - `POST /pause?mode=keep` - Pause generation
 - `POST /resume` - Resume generation
+- `POST /abort_requests` - Abort in-flight requests without pausing the scheduler (send `{}` to abort all, or `{"request_ids": [...]}`)
 
 !!! note "Data Parallelism"
     When using data parallelism with vLLM's **internal load balancer** (i.e. `data_parallel_backend="ray"`), pause and resume are handled automatically across all DP ranks -- a single call is sufficient. When using an **external load balancer** (i.e. multiple independent vLLM instances behind a proxy), you must send pause and resume requests to **every** engine instance individually before and after the weight update.

docs/training/weight_transfer/base.md

@@ -49,6 +49,10 @@ update_request = WeightTransferUpdateRequest(
 )
 ```
 
+At the LLM/API layer, call `start_draft_weight_update()` instead of
+`start_weight_update()` to target the speculative draft model;
+`update_weights` / `finish_weight_update` are unchanged.
+
 ### WeightTransferUpdateInfo
 
 The base `WeightTransferUpdateInfo` is a marker class for backend-specific update info:

docs/usage/security.md

@@ -191,6 +191,7 @@ The following endpoints **do not require authentication** even when `--api-key`
 - `/pause` - Pause generation (causes denial of service)
 - `/resume` - Resume generation
 - `/is_paused` - Check if generation is paused
+- `/abort_requests` - Abort in-flight requests (causes loss of in-flight work)
 - `/scale_elastic_ep` - Trigger scaling operations
 - `/is_scaling_elastic_ep` - Check if scaling is in progress
 - `/init_weight_transfer_engine` - Initialize weight transfer engine for RLHF
@@ -326,6 +327,19 @@ vLLM supports dynamically loading and unloading LoRA adapters at runtime via the
 
 **Warning:** Dynamic LoRA loading is not a secure operation and should not be enabled in deployments exposed to untrusted clients. If you must enable dynamic LoRA loading, restrict access to the `/v1/load_lora_adapter` and `/v1/unload_lora_adapter` endpoints to trusted administrators only, using a reverse proxy or network-level access controls. Do not expose these endpoints to end users. For details on configuring LoRA adapters, see the [LoRA Adapters documentation](../features/lora.md).
 
+## Endpoint Plugins
+
+vLLM supports loading out-of-tree HTTP routes via the `vllm.endpoint_plugins` entry point group (see [Endpoint Plugins](../design/endpoint_plugins.md) for how to write one). An endpoint plugin can register arbitrary FastAPI routes, including routes that reach the engine via `EngineClient.collective_rpc`, so it must be treated as part of the server's trusted code base and not as sandboxed or reviewed input.
+
+**Endpoint plugins are not loaded by default.** Unlike other vLLM plugin groups (`vllm.general_plugins`, `vllm.platform_plugins`, etc.), which load every discovered plugin unless `VLLM_PLUGINS` narrows the set, endpoint plugins load **none** unless `VLLM_PLUGINS` is set and explicitly names them. This mirrors the "off by default in production" posture used for development endpoints gated behind `VLLM_SERVER_DEV_MODE`. Both surfaces are only present when an operator has explicitly opted in.
+
+### Recommended Security Practices
+
+1. **Only allowlist plugins you trust.** Set `VLLM_PLUGINS` to the exact plugin names you intend to run and never wildcard or copy an allowlist between deployments without reviewing what each named plugin does.
+2. **Audit routes before deploying.** A plugin's `attach_router` can add routes under any path, including ones that duplicate existing `/v1/*` paths. There is currently no route conflict enforcement (tracked as a follow-up to RFC [#46565](https://github.com/vllm-project/vllm/issues/46565)), so a malicious or buggy plugin can **shadow a core route** and silently replace its behavior. Prefer plugins that namespace their routes under a distinct prefix (e.g. `/plugins/<plugin-name>/...`) instead of reusing `/v1/...` and review `app.routes` after startup if you need certainty about what is actually being served.
+3. **Treat plugin routes like any other unauthenticated by default surface.** `--api-key` only protects the `/v1`, `/v2`, and `/inference` path prefixes (see [API Key Authentication Limitations](#api-key-authentication-limitations)). A plugin route outside those prefixes is unauthenticated unless the plugin implements its own authentication. Deploy behind a reverse proxy that allowlists only the plugin routes you intend to expose externally.
+4. **Remember the `vllm.general_plugins` pairing.** A plugin that also needs new engine side behavior ships that half separately via `vllm.general_plugins` which loads in every worker process under the default (load all unless restricted) posture. Allowlisting the endpoint plugin does not by itself restrict its paired engine side plugin. Need to review both.
+
 ## gRPC Interface
 
 vLLM provides an optional gRPC Generate service on a separate TCP port, enabled via the `--grpc-port` flag. When not specified, no gRPC server is started. The gRPC listener binds to the same host address as the HTTP server.

examples/disaggregated/disaggregated_serving/disagg_proxy_pushconnector_demo.py

@@ -9,9 +9,9 @@ the same; the difference is in how P and D coordinate the KV transfer:
 * Pull mode: proxy forwards P's ``kv_transfer_params`` (including
   ``remote_block_ids``) to D, and D pulls KV from P via NIXL READ.
 * Push mode: proxy hands D **only** P's coordinates
-  (``remote_engine_id``, ``remote_host``, ``remote_port``, ``tp_size``)
-  and the shared ``remote_request_id``. D registers its locally allocated
-  blocks with P over a NIXL notification; P then pushes the KV to D via
+  (``remote_engine_id``, ``remote_host``, ``remote_port``, ``tp_size``,
+  ``pp_size``) and the shared ``remote_request_id``. D registers its locally
+  allocated blocks with P over a NIXL notification; P then pushes the KV to D via
   NIXL WRITE.
 
 Launch multiple vLLM instances configured with ``NixlPushConnector`` and
@@ -26,6 +26,7 @@ disagg_proxy_pushconnector_demo.py \
        --prefill-kv-host  10.0.0.1 \
        --prefill-side-channel-port 5600 \
        --prefill-tp-size 1 \
+       --prefill-pp-size 1 \
        --port 8000
 """
 
@@ -89,6 +90,7 @@ class PushProxy:
         prefill_kv_host: str,
         prefill_side_channel_port: int,
         prefill_tp_size: int,
+        prefill_pp_size: int,
         custom_create_completion: Callable[[Request], StreamingResponse] | None = None,
         custom_create_chat_completion: Callable[[Request], StreamingResponse]
         | None = None,
@@ -110,6 +112,7 @@ class PushProxy:
             "remote_host": prefill_kv_host,
             "remote_port": prefill_side_channel_port,
             "tp_size": prefill_tp_size,
+            "pp_size": prefill_pp_size,
         }
 
         self.custom_create_completion = custom_create_completion
@@ -198,6 +201,7 @@ class PushProxy:
             "prefill_kv_host": self.push_metadata["remote_host"],
             "prefill_side_channel_port": self.push_metadata["remote_port"],
             "prefill_tp_size": self.push_metadata["tp_size"],
+            "prefill_pp_size": self.push_metadata["pp_size"],
         }
 
     # ── push-mode request handling ──────────────────────────────────── #
@@ -265,7 +269,7 @@ class PushProxy:
         generator = self.forward_request(
             f"http://{decode_instance}{path}", decode_request, headers
         )
-        return StreamingResponse(generator)
+        return StreamingResponse(generator, media_type="application/json")
 
     async def create_completion(self, raw_request: Request):
         try:
@@ -316,6 +320,7 @@ class PushProxyServer:
             prefill_kv_host=args.prefill_kv_host,
             prefill_side_channel_port=args.prefill_side_channel_port,
             prefill_tp_size=args.prefill_tp_size,
+            prefill_pp_size=args.prefill_pp_size,
             custom_create_completion=create_completion,
             custom_create_chat_completion=create_chat_completion,
         )
@@ -420,6 +425,12 @@ def parse_args():
         default=1,
         help="Tensor parallel size of the prefill vLLM instance",
     )
+    parser.add_argument(
+        "--prefill-pp-size",
+        type=int,
+        default=1,
+        help="Pipeline parallel size of the prefill vLLM instance",
+    )
     return parser.parse_args()
 
 

examples/features/kv_events/kv_events_subscriber.py

@@ -99,7 +99,7 @@ def main():
                     replay.send((last_seq + 1).to_bytes(8, "big"))
 
                     while poller.poll(timeout=200):
-                        seq_bytes, replay_payload = replay.recv_multipart()
+                        _, seq_bytes, replay_payload = replay.recv_multipart()
                         if not replay_payload:
                             # End of replay marker is sent as an empty frame
                             # for the payload

examples/generate/multimodal/vision_language_offline.py

@@ -448,24 +448,6 @@ def run_exaone4_5(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
-# Fuyu
-def run_fuyu(questions: list[str], modality: str) -> ModelRequestData:
-    assert modality == "image"
-
-    prompts = [f"{question}\n" for question in questions]
-    engine_args = EngineArgs(
-        model="adept/fuyu-8b",
-        max_model_len=2048,
-        max_num_seqs=2,
-        limit_mm_per_prompt={modality: 1},
-    )
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompts=prompts,
-    )
-
-
 # Gemma 3
 def run_gemma3(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -2318,7 +2300,6 @@ model_example_map = {
     "eagle2_5": run_eagle2_5,
     "ernie45_vl": run_ernie45_vl,
     "exaone4_5": run_exaone4_5,
-    "fuyu": run_fuyu,
     "gemma3": run_gemma3,
     "gemma3n": run_gemma3n,
     "glm4v": run_glm4v,

requirements/cuda.txt

@@ -9,7 +9,7 @@ torchaudio==2.11.0
 # These must be updated alongside torch
 torchvision==0.26.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 torchcodec >= 0.14
-PyNvVideoCodec==2.1.0
+PyNvVideoCodec==2.0.4
 # FlashInfer should be updated together with the Dockerfile
 flashinfer-python==0.6.13
 flashinfer-cubin==0.6.13

requirements/test/cpu.txt

@@ -962,7 +962,7 @@ s3transfer==0.10.3
     # via boto3
 sacrebleu==2.4.3
     # via lm-eval
-safetensors==0.7.0
+safetensors==0.8.0
     # via
     #   -r requirements/test/../common.txt
     #   accelerate
@@ -1159,7 +1159,7 @@ tqdm==4.67.3
     #   segmentation-models-pytorch
     #   sentence-transformers
     #   transformers
-transformers==5.10.4
+transformers==5.13.1
     # via
     #   -r requirements/test/../common.txt
     #   -r requirements/test/cuda.in

requirements/test/cuda.in

@@ -39,7 +39,7 @@ open_clip_torch==2.32.0 # Required for nemotron_vl test, Nemotron Parse in test_
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.12 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==5.10.4
+transformers==5.13.1
 tokenizers==0.22.2
 schemathesis>=4.0.0 # Required for openai schema test.
 # quantization

requirements/test/cuda.txt

@@ -1053,7 +1053,7 @@ s3transfer==0.10.3
     # via boto3
 sacrebleu==2.4.3
     # via lm-eval
-safetensors==0.7.0
+safetensors==0.8.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -1261,7 +1261,7 @@ tqdm==4.67.3
     #   segmentation-models-pytorch
     #   sentence-transformers
     #   transformers
-transformers==5.10.4
+transformers==5.13.1
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt

requirements/test/nightly-torch.txt

@@ -29,7 +29,7 @@ opencv-python-headless >= 4.13.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.12 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==5.10.4
+transformers==5.13.1
 tokenizers==0.22.2
 schemathesis>=4.0.0 # Required for openai schema test.
 # quantization

requirements/test/rocm.in

@@ -35,7 +35,7 @@ open_clip_torch==2.32.0 # Required for nemotron_vl test, Nemotron Parse in test_
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.12 # required for model evaluation test
 mteb[bm25s]>=2, <3 # required for mteb test
-transformers==5.10.4
+transformers==5.13.1
 tokenizers==0.22.2
 schemathesis>=4.0.0 # Required for openai schema test
 # quantization

requirements/test/rocm.txt

@@ -1035,7 +1035,7 @@ s3transfer==0.16.0
     # via boto3
 sacrebleu==2.6.0
     # via lm-eval
-safetensors==0.7.0
+safetensors==0.8.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -1218,7 +1218,7 @@ tqdm==4.67.3
     #   sentence-transformers
     #   tilelang
     #   transformers
-transformers==5.10.4
+transformers==5.13.1
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt

requirements/test/xpu.in

@@ -17,7 +17,7 @@ accelerate
 arctic-inference
 lm_eval[api]>=0.4.12
 modelscope<1.38
-transformers==5.10.4
+transformers==5.13.1
 
 # --- Audio Processing ---
 librosa

requirements/test/xpu.txt

@@ -779,7 +779,7 @@ rpds-py==0.30.0
     #   referencing
 sacrebleu==2.6.0
     # via lm-eval
-safetensors==0.7.0
+safetensors==0.8.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt
@@ -940,7 +940,7 @@ tqdm==4.67.3
     #   pqdm
     #   sentence-transformers
     #   transformers
-transformers==5.10.4
+transformers==5.13.1
     # via
     #   -c requirements/common.txt
     #   -r requirements/test/../common.txt

requirements/xpu.txt

@@ -18,4 +18,4 @@ torchvision
 torchcodec >= 0.14 # Required for the torchcodec video decoding backend
 
 auto_round_lib>=0.14.0
-vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.10.1/vllm_xpu_kernels-0.1.10.1-cp38-abi3-manylinux_2_28_x86_64.whl
+vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.11/vllm_xpu_kernels-0.1.11-cp38-abi3-manylinux_2_28_x86_64.whl

rust/Cargo.lock

@@ -2167,7 +2167,7 @@ checksum = "11d3d7f243d5c5a8b9bb5d6dd2b1602c0cb0b9db1621bafc7ed66e35ff9fe092"
 [[package]]
 name = "llm-multimodal"
 version = "1.7.1"
-source = "git+https://github.com/smg-project/llm-multimodal?rev=7d74582aeaf0e4086a44964382655d22f1af0686#7d74582aeaf0e4086a44964382655d22f1af0686"
+source = "git+https://github.com/smg-project/llm-multimodal?rev=c8a29dcc755139fdc26185f400ea48c6d6d48273#c8a29dcc755139fdc26185f400ea48c6d6d48273"
 dependencies = [
  "anyhow",
  "base64 0.22.1",
@@ -2473,6 +2473,7 @@ dependencies = [
  "portable-atomic",
  "portable-atomic-util",
  "rawpointer",
+ "serde",
 ]
 
 [[package]]
@@ -5093,6 +5094,7 @@ dependencies = [
  "llm-multimodal",
  "minijinja",
  "minijinja-contrib",
+ "ndarray 0.17.2",
  "oss-harmony",
  "paste",
  "reqwest",