From bf4baa74309f07471b7cfbb74c5ed5c60431a502 Mon Sep 17 00:00:00 2001
From: zeryx <1892175+zeryx@users.noreply.github.com>
Date: Tue, 5 May 2026 09:47:58 -0400
Subject: [PATCH 1/3] [Docker] Add SM120 Blackwell Dockerfile for NVFP4 tensor
 core support

The upstream Dockerfile fails to build on SM120 (Blackwell) workstations
in firewalled networks because:
- deadsnakes PPA is unreachable, blocking Python 3.12 installation
- gcc-10/g++-10 unavailable on Ubuntu 24.04

This adds Dockerfile.sm120 which:
- Skips deadsnakes when python3.12 is already in base repos (Ubuntu 24.04)
- Makes gcc-10 installation optional, falling back to system GCC
- Enables universe repo for libxext6/libgl1 on minimal base images

Build with: torch_cuda_arch_list=12.0a to compile NVFP4 CUTLASS kernels
(nvfp4_scaled_mm_sm120, nvfp4_blockwise_moe, nvfp4_kv_cache) for SM120.

Verified on 2x RTX PRO 6000 BWE with Nemotron-3 Nano 30B (single GPU)
and Super 120B (TP=2) NVFP4 models. All 146 NVFP4 kernel tests passing.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Signed-off-by: zeryx <1892175+zeryx@users.noreply.github.com>
---
 docker/Dockerfile.sm120 | 872 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 872 insertions(+)
 create mode 100644 docker/Dockerfile.sm120

diff --git a/docker/Dockerfile.sm120 b/docker/Dockerfile.sm120
new file mode 100644
index 000000000000..3ac42997932f
--- /dev/null
+++ b/docker/Dockerfile.sm120
@@ -0,0 +1,872 @@
+# The vLLM Dockerfile is used to construct vLLM image that can be directly used
+# to run the OpenAI compatible server.
+
+# Please update any changes made here to
+# docs/contributing/dockerfile/dockerfile.md and
+# docs/assets/contributing/dockerfile-stages-dependency.png
+
+# =============================================================================
+# VERSION MANAGEMENT
+# =============================================================================
+# ARG defaults in this Dockerfile are the source of truth for pinned versions.
+# docker/versions.json is auto-generated for use with docker buildx bake.
+#
+# When updating versions:
+# 1. Edit the ARG defaults below
+# 2. Run: python tools/generate_versions_json.py
+#
+# To query versions programmatically:
+#   jq -r '.variable.CUDA_VERSION.default' docker/versions.json
+#
+# To build with bake:
+#   docker buildx bake -f docker/docker-bake.hcl -f docker/versions.json
+# =============================================================================
+
+ARG CUDA_VERSION=13.0.2
+ARG PYTHON_VERSION=3.12
+ARG UBUNTU_VERSION=22.04
+
+# By parameterizing the base images, we allow third-party to use their own
+# base images. One use case is hermetic builds with base images stored in
+# private registries that use a different repository naming conventions.
+#
+# Example:
+# docker build --build-arg BUILD_BASE_IMAGE=registry.acme.org/mirror/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04
+
+# Important: We build with an old version of Ubuntu to maintain broad
+# compatibility with other Linux OSes. The main reason for this is that the
+# glibc version is baked into the distro, and binaries built with one glibc
+# version are not backwards compatible with OSes that use an earlier version.
+ARG BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04
+# Using cuda base image with minimal dependencies necessary for JIT compilation (FlashInfer, DeepGEMM, EP kernels)
+ARG FINAL_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-base-ubuntu${UBUNTU_VERSION}
+
+# OS family of BUILD_BASE_IMAGE. Controls package manager (apt vs dnf) and
+# Python bootstrap. Set to "manylinux" alongside a manylinux build base such
+# as pytorch/manylinux2_28-builder:cuda13.0 to produce wheels with a glibc
+# 2.28 floor (matches PyTorch's own published wheels). Default stays on
+# Ubuntu for backwards compatibility.
+ARG BUILD_OS=ubuntu
+
+# By parameterizing the Deadsnakes repository URL, we allow third-party to use
+# their own mirror. When doing so, we don't benefit from the transparent
+# installation of the GPG key of the PPA, as done by add-apt-repository, so we
+# also need a URL for the GPG key.
+ARG DEADSNAKES_MIRROR_URL
+ARG DEADSNAKES_GPGKEY_URL
+
+# The PyPA get-pip.py script is a self contained script+zip file, that provides
+# both the installer script and the pip base85-encoded zip archive. This allows
+# bootstrapping pip in environment where a distribution package does not exist.
+#
+# By parameterizing the URL for get-pip.py installation script, we allow
+# third-party to use their own copy of the script stored in a private mirror.
+# We set the default value to the PyPA owned get-pip.py script.
+#
+# Reference: https://pip.pypa.io/en/stable/installation/#get-pip-py
+ARG GET_PIP_URL="https://bootstrap.pypa.io/get-pip.py"
+
+# PIP supports fetching the packages from custom indexes, allowing third-party
+# to host the packages in private mirrors. The PIP_INDEX_URL and
+# PIP_EXTRA_INDEX_URL are standard PIP environment variables to override the
+# default indexes. By letting them empty by default, PIP will use its default
+# indexes if the build process doesn't override the indexes.
+#
+# Uv uses different variables. We set them by default to the same values as
+# PIP, but they can be overridden.
+ARG PIP_INDEX_URL
+ARG PIP_EXTRA_INDEX_URL
+ARG UV_INDEX_URL=${PIP_INDEX_URL}
+ARG UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
+
+# PyTorch provides its own indexes for standard and nightly builds
+ARG PYTORCH_CUDA_INDEX_BASE_URL=https://download.pytorch.org/whl
+
+# PIP supports multiple authentication schemes, including keyring
+# By parameterizing the PIP_KEYRING_PROVIDER variable and setting it to
+# disabled by default, we allow third-party to use keyring authentication for
+# their private Python indexes, while not changing the default behavior which
+# is no authentication.
+#
+# Reference: https://pip.pypa.io/en/stable/topics/authentication/#keyring-support
+ARG PIP_KEYRING_PROVIDER=disabled
+ARG UV_KEYRING_PROVIDER=${PIP_KEYRING_PROVIDER}
+
+# Flag enables built-in KV-connector dependency libs into docker images
+ARG INSTALL_KV_CONNECTORS=false
+
+#################### BASE BUILD IMAGE ####################
+# prepare basic build environment
+FROM ${BUILD_BASE_IMAGE} AS base
+
+ARG CUDA_VERSION
+ARG PYTHON_VERSION
+ARG BUILD_OS
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install system dependencies including build tools.
+# The Ubuntu path uses apt + deadsnakes-via-uv for Python; the manylinux path
+# (AlmaLinux 8, e.g. pytorch/manylinux2_28-builder) uses dnf and the Python
+# interpreters pre-installed at /opt/python/cpXY-cpXY/.
+RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
+        # rdma-core-devel provides libibverbs headers; ccache lives in EPEL,
+        # which the pytorch manylinux image already enables. git/curl/sudo
+        # are typically pre-installed but listed defensively.
+        dnf install -y --setopt=install_weak_deps=False \
+            ccache \
+            git \
+            curl \
+            sudo \
+            rdma-core-devel \
+        && dnf clean all \
+        && rm -rf /var/cache/dnf; \
+    else \
+        apt-get update -y \
+        && apt-get install -y --no-install-recommends \
+            ccache \
+            software-properties-common \
+            git \
+            curl \
+            sudo \
+            python3-pip \
+            libibverbs-dev \
+        && (apt-get install -y --no-install-recommends gcc-10 g++-10 \
+            && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
+            || true) \
+        # Install python dev headers if available (needed for cmake FindPython on Ubuntu 24.04
+        # which ships cmake 3.28 and requires Development.SABIModule; silently skipped on
+        # Ubuntu 20.04/22.04 where python3.x-dev is not available without a PPA)
+        && (apt-get install -y --no-install-recommends python${PYTHON_VERSION}-dev 2>/dev/null || true) \
+        && rm -rf /var/lib/apt/lists/*; \
+    fi
+
+# Install uv and bootstrap /opt/venv. Both paths converge on /opt/venv so all
+# downstream stages stay distro-agnostic.
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
+    && if [ "${BUILD_OS}" = "manylinux" ]; then \
+           # manylinux images ship Python at /opt/python/cpXY-cpXY/; point uv
+           # at the matching interpreter rather than letting it fetch one.
+           PYV_NODOT=$(echo ${PYTHON_VERSION} | tr -d '.') \
+           && MANYLINUX_PY=/opt/python/cp${PYV_NODOT}-cp${PYV_NODOT}/bin/python${PYTHON_VERSION} \
+           && $HOME/.local/bin/uv venv /opt/venv --python "$MANYLINUX_PY"; \
+       else \
+           $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION}; \
+       fi \
+    && rm -f /usr/bin/python3 /usr/bin/python3-config /usr/bin/pip \
+    && ln -sf /opt/venv/bin/python3 /usr/bin/python3 \
+    && ln -sf /opt/venv/bin/python3-config /usr/bin/python3-config \
+    && ln -sf /opt/venv/bin/pip /usr/bin/pip \
+    && python3 --version && python3 -m pip --version
+
+# Activate virtual environment and add uv to PATH
+ENV PATH="/opt/venv/bin:/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+
+# Environment for uv
+ENV UV_HTTP_TIMEOUT=500
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+ENV UV_LINK_MODE=copy
+
+# Verify GCC version
+RUN gcc --version
+
+# Enable CUDA forward compatibility by setting '-e VLLM_ENABLE_CUDA_COMPATIBILITY=1'
+# Only needed for datacenter/professional GPUs with older drivers.
+# See: https://docs.nvidia.com/deploy/cuda-compatibility/
+ENV VLLM_ENABLE_CUDA_COMPATIBILITY=0
+
+# ============================================================
+# SLOW-CHANGING DEPENDENCIES BELOW
+# These are the expensive layers that we want to cache
+# ============================================================
+
+# Install PyTorch and core CUDA dependencies
+# This is ~2GB and rarely changes
+ARG PYTORCH_CUDA_INDEX_BASE_URL
+
+WORKDIR /workspace
+
+# We can specify the standard or nightly build of PyTorch
+ARG PYTORCH_NIGHTLY
+
+# Install build and runtime dependencies, including PyTorch
+# Check whether to install torch nightly instead of release for this build
+COPY requirements/common.txt requirements/common.txt
+COPY requirements/cuda.txt requirements/cuda.txt
+COPY use_existing_torch.py use_existing_torch.py
+COPY pyproject.toml pyproject.toml
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+        echo "Installing torch nightly..." \
+        && uv pip install --python /opt/venv/bin/python3 torch torchaudio torchvision --pre \
+        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
+        && echo "Installing other requirements..." \
+        && /opt/venv/bin/python3 use_existing_torch.py --prefix \
+        && uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    else \
+        uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    fi
+
+# Track PyTorch lib versions used during build and match in downstream instances.
+# We do this for both nightly and release so we can strip dependencies/*.txt as needed.
+# Otherwise library dependencies can upgrade/downgrade torch incorrectly.
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip freeze | grep -i "^torch=\|^torchvision=\|^torchaudio=" > torch_lib_versions.txt \
+    && TORCH_LIB_VERSIONS=$(cat torch_lib_versions.txt | xargs) \
+    && echo "Installed torch libs: ${TORCH_LIB_VERSIONS}"
+
+# CUDA arch list used by torch
+# Explicitly set the list to avoid issues with torch 2.2
+# See https://github.com/pytorch/pytorch/pull/123243
+# From versions.json: .torch.cuda_arch_list
+ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
+ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
+#################### BUILD BASE IMAGE ####################
+
+#################### CSRC BUILD IMAGE ####################
+FROM base AS csrc-build
+ARG TARGETPLATFORM
+
+ARG PIP_INDEX_URL UV_INDEX_URL
+ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
+ARG PYTORCH_CUDA_INDEX_BASE_URL
+
+# We can specify the standard or nightly build of PyTorch
+ARG PYTORCH_NIGHTLY
+
+# Install build dependencies
+COPY requirements/build/cuda.txt requirements/build/cuda.txt
+COPY use_existing_torch.py use_existing_torch.py
+COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+# Use copy mode to avoid hardlink failures with Docker cache mounts
+ENV UV_LINK_MODE=copy
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+        echo "Installing build requirements without torch..." \
+        && python3 use_existing_torch.py --prefix \
+        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
+        && echo "Installing torch nightly..." \
+        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | grep -i "^torch=" | xargs) --pre \
+        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    else \
+        echo "Installing build requirements..." \
+        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    fi
+
+WORKDIR /workspace
+
+COPY pyproject.toml setup.py CMakeLists.txt ./
+COPY cmake cmake/
+COPY csrc csrc/
+COPY vllm/envs.py vllm/envs.py
+COPY vllm/__init__.py vllm/__init__.py
+
+# max jobs used by Ninja to build extensions
+ARG max_jobs=2
+ENV MAX_JOBS=${max_jobs}
+# number of threads used by nvcc
+ARG nvcc_threads=8
+ENV NVCC_THREADS=$nvcc_threads
+
+ARG USE_SCCACHE
+ARG SCCACHE_DOWNLOAD_URL
+ARG SCCACHE_ENDPOINT
+ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
+ARG SCCACHE_REGION_NAME=us-west-2
+ARG SCCACHE_S3_NO_CREDENTIALS=0
+
+# Flag to control whether to use pre-built vLLM wheels
+ARG VLLM_USE_PRECOMPILED=""
+ARG VLLM_MERGE_BASE_COMMIT=""
+ARG VLLM_MAIN_CUDA_VERSION=""
+
+# Use dummy version for csrc-build wheel (only .so files are extracted, version doesn't matter)
+ENV SETUPTOOLS_SCM_PRETEND_VERSION="0.0.0+csrc.build"
+
+# Use existing torch for nightly builds
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+        python3 use_existing_torch.py --prefix; \
+    fi
+
+# Build the vLLM wheel
+# if USE_SCCACHE is set, use sccache to speed up compilation
+# AWS credentials mounted at ~/.aws/credentials for sccache S3 auth (optional)
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=secret,id=aws-credentials,target=/root/.aws/credentials,required=false \
+    if [ "$USE_SCCACHE" = "1" ]; then \
+        echo "Installing sccache..." \
+        && case "${TARGETPLATFORM}" in \
+          linux/arm64) SCCACHE_ARCH="aarch64" ;; \
+          linux/amd64) SCCACHE_ARCH="x86_64" ;; \
+          *) echo "Unsupported TARGETPLATFORM for sccache: ${TARGETPLATFORM}" >&2; exit 1 ;; \
+        esac \
+        && export SCCACHE_DOWNLOAD_URL="${SCCACHE_DOWNLOAD_URL:-https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-${SCCACHE_ARCH}-unknown-linux-musl.tar.gz}" \
+        && curl -L -o sccache.tar.gz ${SCCACHE_DOWNLOAD_URL} \
+        && tar -xzf sccache.tar.gz \
+        && sudo mv sccache-v0.8.1-${SCCACHE_ARCH}-unknown-linux-musl/sccache /usr/bin/sccache \
+        && rm -rf sccache.tar.gz sccache-v0.8.1-${SCCACHE_ARCH}-unknown-linux-musl \
+        && if [ ! -z ${SCCACHE_ENDPOINT} ] ; then export SCCACHE_ENDPOINT=${SCCACHE_ENDPOINT} ; fi \
+        && export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
+        && export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
+        && export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
+        && export SCCACHE_IDLE_TIMEOUT=0 \
+        && export CMAKE_BUILD_TYPE=Release \
+        && export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" \
+        && export VLLM_PRECOMPILED_WHEEL_COMMIT="${VLLM_MERGE_BASE_COMMIT}" \
+        && export VLLM_MAIN_CUDA_VERSION="${VLLM_MAIN_CUDA_VERSION}" \
+        && export VLLM_DOCKER_BUILD_CONTEXT=1 \
+        && sccache --show-stats \
+        && python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
+        && sccache --show-stats; \
+    fi
+
+ARG vllm_target_device="cuda"
+ENV VLLM_TARGET_DEVICE=${vllm_target_device}
+ENV CCACHE_DIR=/root/.cache/ccache
+RUN --mount=type=cache,target=/root/.cache/ccache \
+    --mount=type=cache,target=/root/.cache/uv \
+    if [ "$USE_SCCACHE" != "1" ]; then \
+        # Clean any existing CMake artifacts
+        rm -rf .deps && \
+        mkdir -p .deps && \
+        export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" && \
+        export VLLM_PRECOMPILED_WHEEL_COMMIT="${VLLM_MERGE_BASE_COMMIT}" && \
+        export VLLM_DOCKER_BUILD_CONTEXT=1 && \
+        python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
+    fi
+
+#################### CSRC BUILD IMAGE ####################
+
+#################### EXTENSIONS BUILD IMAGE ####################
+# Build DeepEP - runs in PARALLEL with csrc-build
+# This stage is independent and doesn't affect csrc cache
+FROM base AS extensions-build
+ARG CUDA_VERSION
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+ENV UV_HTTP_TIMEOUT=500
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+ENV UV_LINK_MODE=copy
+
+WORKDIR /workspace
+
+# Build DeepEP wheels
+COPY tools/ep_kernels/install_python_libraries.sh /tmp/install_python_libraries.sh
+# Defaults moved here from tools/ep_kernels/install_python_libraries.sh for centralized version management
+ARG DEEPEP_COMMIT_HASH=73b6ea4
+ARG NVSHMEM_VER
+RUN --mount=type=cache,target=/root/.cache/uv \
+    mkdir -p /tmp/ep_kernels_workspace/dist && \
+    export TORCH_CUDA_ARCH_LIST='9.0a 10.0a' && \
+    /tmp/install_python_libraries.sh \
+        --workspace /tmp/ep_kernels_workspace \
+        --mode wheel \
+        ${DEEPEP_COMMIT_HASH:+--deepep-ref "$DEEPEP_COMMIT_HASH"} \
+        ${NVSHMEM_VER:+--nvshmem-ver "$NVSHMEM_VER"} && \
+    find /tmp/ep_kernels_workspace/nvshmem -name '*.a' -delete
+#################### EXTENSIONS BUILD IMAGE ####################
+
+#################### WHEEL BUILD IMAGE ####################
+FROM base AS build
+ARG TARGETPLATFORM
+
+ARG PIP_INDEX_URL UV_INDEX_URL
+ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
+ARG PYTORCH_CUDA_INDEX_BASE_URL
+
+# We can specify the standard or nightly build of PyTorch
+ARG PYTORCH_NIGHTLY
+
+# Install build dependencies
+COPY requirements/build/cuda.txt requirements/build/cuda.txt
+COPY use_existing_torch.py use_existing_torch.py
+COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+# Use copy mode to avoid hardlink failures with Docker cache mounts
+ENV UV_LINK_MODE=copy
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+        echo "Installing build requirements without torch..." \
+        && python3 use_existing_torch.py --prefix \
+        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
+        && echo "Installing torch nightly..." \
+        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | grep -i "^torch=" | xargs) --pre \
+        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    else \
+        echo "Installing build requirements..." \
+        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    fi
+
+WORKDIR /workspace
+
+# Copy pre-built csrc wheel directly
+COPY --from=csrc-build /workspace/dist /precompiled-wheels
+COPY . .
+
+ARG GIT_REPO_CHECK=0
+RUN --mount=type=bind,source=.git,target=.git \
+    if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
+
+ARG vllm_target_device="cuda"
+ENV VLLM_TARGET_DEVICE=${vllm_target_device}
+
+# Skip adding +precompiled suffix to version (preserves git-derived version)
+ENV VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX=1
+
+# Use existing torch for nightly builds
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+        python3 use_existing_torch.py --prefix; \
+    fi
+
+# Build the vLLM wheel
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=.git,target=.git \
+    if [ "${vllm_target_device}" = "cuda" ]; then \
+        export VLLM_PRECOMPILED_WHEEL_LOCATION=$(ls /precompiled-wheels/*.whl); \
+    fi && \
+    python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38
+
+# Copy extension wheels from extensions-build stage for later use
+COPY --from=extensions-build /tmp/ep_kernels_workspace/dist /tmp/ep_kernels_workspace/dist
+
+# Check the size of the wheel if RUN_WHEEL_CHECK is true
+COPY .buildkite/check-wheel-size.py check-wheel-size.py
+# sync the default value with .buildkite/check-wheel-size.py
+ARG VLLM_MAX_SIZE_MB=500
+ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
+ARG RUN_WHEEL_CHECK=true
+RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
+        python3 check-wheel-size.py dist; \
+    else \
+        echo "Skipping wheel size check."; \
+    fi
+
+#################### WHEEL BUILD IMAGE ####################
+
+#################### DEV IMAGE ####################
+FROM base AS dev
+
+ARG PIP_INDEX_URL UV_INDEX_URL
+ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
+ARG PYTORCH_CUDA_INDEX_BASE_URL
+ARG BUILD_OS
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+# Use copy mode to avoid hardlink failures with Docker cache mounts
+ENV UV_LINK_MODE=copy
+
+# Install libnuma-dev, required by fastsafetensors (fixes #20384)
+RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
+        dnf install -y numactl-devel && dnf clean all && rm -rf /var/cache/dnf; \
+    else \
+        apt-get update && apt-get install -y --no-install-recommends libnuma-dev && rm -rf /var/lib/apt/lists/*; \
+    fi
+
+
+# We can specify the standard or nightly build of PyTorch
+ARG PYTORCH_NIGHTLY
+
+# Install development dependencies
+COPY requirements/lint.txt requirements/lint.txt
+COPY requirements/test/cuda.in requirements/test/cuda.in
+COPY requirements/test/cuda.txt requirements/test/cuda.txt
+COPY requirements/dev.txt requirements/dev.txt
+COPY use_existing_torch.py use_existing_torch.py
+COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+        echo "Installing dev requirements plus torch nightly..." \
+        && python3 use_existing_torch.py --prefix \
+        && cat torch_lib_versions.txt >> requirements/test/cuda.in \
+        && uv pip compile requirements/test/cuda.in -o requirements/test/cuda.txt --index-strategy unsafe-best-match \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
+        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | xargs) --pre \
+        -r requirements/dev.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    else \
+        echo "Installing dev requirements..." \
+        && uv pip install --python /opt/venv/bin/python3 -r requirements/dev.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    fi
+
+#################### DEV IMAGE ####################
+#################### vLLM installation IMAGE ####################
+# image with vLLM installed
+FROM ${FINAL_BASE_IMAGE} AS vllm-base
+
+ARG CUDA_VERSION
+ARG PYTHON_VERSION
+ARG DEADSNAKES_MIRROR_URL
+ARG DEADSNAKES_GPGKEY_URL
+ARG GET_PIP_URL
+
+ENV DEBIAN_FRONTEND=noninteractive
+WORKDIR /vllm-workspace
+
+
+# Python version string for paths (e.g., "312" for 3.12)
+RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
+    echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
+
+# Install Python and system dependencies
+RUN apt-get update -y \
+    && (add-apt-repository -y universe 2>/dev/null || true) \
+    && apt-get update -y \
+    && apt-get install -y --no-install-recommends \
+        software-properties-common \
+        curl \
+        sudo \
+        ffmpeg \
+        libsm6 \
+        libxext6 \
+        libgl1 \
+    && if apt-cache show python${PYTHON_VERSION} >/dev/null 2>&1 ; then \
+        echo "python${PYTHON_VERSION} available in base repos, skipping deadsnakes" ; \
+    elif [ ! -z ${DEADSNAKES_MIRROR_URL} ] ; then \
+        if [ ! -z "${DEADSNAKES_GPGKEY_URL}" ] ; then \
+            mkdir -p -m 0755 /etc/apt/keyrings ; \
+            curl -L ${DEADSNAKES_GPGKEY_URL} | gpg --dearmor > /etc/apt/keyrings/deadsnakes.gpg ; \
+            sudo chmod 644 /etc/apt/keyrings/deadsnakes.gpg ; \
+            echo "deb [signed-by=/etc/apt/keyrings/deadsnakes.gpg] ${DEADSNAKES_MIRROR_URL} $(lsb_release -cs) main" > /etc/apt/sources.list.d/deadsnakes.list ; \
+        fi ; \
+    else \
+        for i in 1 2 3; do \
+            add-apt-repository -y ppa:deadsnakes/ppa && break || \
+            { echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
+        done ; \
+    fi \
+    && apt-get update -y \
+    && apt-get install -y --no-install-recommends \
+        python${PYTHON_VERSION} \
+        python${PYTHON_VERSION}-dev \
+        python${PYTHON_VERSION}-venv \
+        libibverbs-dev \
+    && rm -rf /var/lib/apt/lists/* \
+    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
+    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
+    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
+    && rm -f /usr/lib/python${PYTHON_VERSION}/EXTERNALLY-MANAGED \
+    && curl -sS ${GET_PIP_URL} | python${PYTHON_VERSION} \
+    && python3 --version && python3 -m pip --version
+
+# Install CUDA development tools for runtime JIT compilation
+# (FlashInfer, DeepGEMM, EP kernels all require compilation at runtime)
+RUN CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-') && \
+    CUDA_VERSION_SHORT=$(echo $CUDA_VERSION | cut -d. -f1,2) && \
+    apt-get update -y && \
+    apt-get install -y --no-install-recommends --allow-change-held-packages \
+        cuda-nvcc-${CUDA_VERSION_DASH} \
+        cuda-cudart-${CUDA_VERSION_DASH} \
+        cuda-nvrtc-${CUDA_VERSION_DASH} \
+        cuda-cuobjdump-${CUDA_VERSION_DASH} \
+        libcurand-dev-${CUDA_VERSION_DASH} \
+        libcublas-dev-${CUDA_VERSION_DASH} \
+        # Required by fastsafetensors (fixes #20384)
+        libnuma-dev \
+        # numactl CLI for NUMA binding at runtime
+        numactl && \
+    # Fixes nccl_allocator requiring nccl.h at runtime
+    # https://github.com/vllm-project/vllm/blob/1336a1ea244fa8bfd7e72751cabbdb5b68a0c11a/vllm/distributed/device_communicators/pynccl_allocator.py#L22
+    # NCCL packages don't use the cuda-MAJOR-MINOR naming convention,
+    # so we pin the version to match our CUDA version
+    NCCL_VER=$(apt-cache madison libnccl-dev | grep "+cuda${CUDA_VERSION_SHORT}" | head -1 | awk -F'|' '{gsub(/^ +| +$/, "", $2); print $2}') && \
+    apt-get install -y --no-install-recommends --allow-change-held-packages libnccl-dev=${NCCL_VER} libnccl2=${NCCL_VER} && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install uv for faster pip installs
+RUN python3 -m pip install uv
+
+# Environment for uv
+ENV UV_HTTP_TIMEOUT=500
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+ENV UV_LINK_MODE=copy
+
+# Enable CUDA forward compatibility by setting '-e VLLM_ENABLE_CUDA_COMPATIBILITY=1'
+# Only needed for datacenter/professional GPUs with older drivers.
+# See: https://docs.nvidia.com/deploy/cuda-compatibility/
+ENV VLLM_ENABLE_CUDA_COMPATIBILITY=0
+
+# ============================================================
+# SLOW-CHANGING DEPENDENCIES BELOW
+# These are the expensive layers that we want to cache
+# ============================================================
+
+# Install PyTorch and core CUDA dependencies
+# This is ~2GB and rarely changes
+ARG PYTORCH_CUDA_INDEX_BASE_URL
+COPY requirements/common.txt /tmp/common.txt
+COPY requirements/cuda.txt /tmp/requirements-cuda.txt
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install --system -r /tmp/requirements-cuda.txt \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') && \
+    rm /tmp/requirements-cuda.txt /tmp/common.txt
+
+# Install FlashInfer JIT cache (requires CUDA-version-specific index URL)
+# https://docs.flashinfer.ai/installation.html
+# From versions.json: .flashinfer.version
+ARG FLASHINFER_VERSION=0.6.8.post1
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
+        --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
+
+# ============================================================
+# OPENAI API SERVER DEPENDENCIES
+# Pre-install these to avoid reinstalling on every vLLM wheel rebuild
+# ============================================================
+
+# Install gdrcopy (saves ~6s per build)
+# TODO (huydhn): There is no prebuilt gdrcopy package on 12.9 at the moment
+ARG GDRCOPY_CUDA_VERSION=12.8
+ARG GDRCOPY_OS_VERSION=Ubuntu22_04
+ARG TARGETPLATFORM
+COPY tools/install_gdrcopy.sh /tmp/install_gdrcopy.sh
+RUN set -eux; \
+    case "${TARGETPLATFORM}" in \
+      linux/arm64) UUARCH="aarch64" ;; \
+      linux/amd64) UUARCH="x64" ;; \
+      *) echo "Unsupported TARGETPLATFORM: ${TARGETPLATFORM}" >&2; exit 1 ;; \
+    esac; \
+    /tmp/install_gdrcopy.sh "${GDRCOPY_OS_VERSION}" "${GDRCOPY_CUDA_VERSION}" "${UUARCH}" && \
+    rm /tmp/install_gdrcopy.sh
+
+# Install vllm-openai dependencies (saves ~2.6s per build)
+# These are stable packages that don't depend on vLLM itself
+# From versions.json: .bitsandbytes.x86_64, .bitsandbytes.arm64
+# From versions.json: .openai_server_extras.timm, .openai_server_extras.runai_model_streamer
+ARG BITSANDBYTES_VERSION_X86=0.46.1
+ARG BITSANDBYTES_VERSION_ARM64=0.42.0
+ARG TIMM_VERSION=">=1.0.17"
+ARG RUNAI_MODEL_STREAMER_VERSION=">=0.15.7"
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
+        BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_ARM64}"; \
+    else \
+        BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_X86}"; \
+    fi; \
+    uv pip install --system accelerate modelscope \
+        "bitsandbytes>=${BITSANDBYTES_VERSION}" "timm${TIMM_VERSION}" "runai-model-streamer[s3,gcs,azure]${RUNAI_MODEL_STREAMER_VERSION}"
+
+# ============================================================
+# VLLM INSTALLATION (depends on build stage)
+# ============================================================
+
+ARG PIP_INDEX_URL UV_INDEX_URL
+ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
+ARG PYTORCH_CUDA_INDEX_BASE_URL
+ARG PIP_KEYRING_PROVIDER UV_KEYRING_PROVIDER
+
+# We can specify the standard or nightly build of PyTorch
+ARG PYTORCH_NIGHTLY
+
+# Install vLLM wheel first, so that torch etc will be installed.
+# Check whether to install torch nightly instead of release for this build.
+COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
+RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
+    --mount=type=cache,target=/root/.cache/uv \
+    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+        echo "Installing torch nightly..." \
+        && uv pip install --system $(cat torch_lib_versions.txt | xargs) --pre \
+        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
+        && echo "Installing vLLM..." \
+        && uv pip install --system dist/*.whl --verbose \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    else \
+        echo "Installing vLLM..." \
+        && uv pip install --system dist/*.whl --verbose \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+    fi
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+. /etc/environment && \
+uv pip list
+
+# Pytorch now installs NVSHMEM, setting LD_LIBRARY_PATH
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+
+# Install EP kernels wheels (DeepEP) that have been built in the `build` stage
+RUN --mount=type=bind,from=build,src=/tmp/ep_kernels_workspace/dist,target=/vllm-workspace/ep_kernels/dist \
+    --mount=type=cache,target=/root/.cache/uv \
+    uv pip install --system ep_kernels/dist/*.whl --verbose \
+        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
+
+# Download FlashInfer precompiled cubins AFTER all pip installs are done.
+# This must run after the vLLM wheel and EP kernels installs above, because
+# those can reinstall/touch flashinfer packages. Downloading cubins earlier
+# (in the flashinfer-jit-cache layer) causes ~2.5 GB of layer duplication
+# when a later pip install overwrites flashinfer package files.
+RUN flashinfer show-config && flashinfer download-cubin
+
+# CUDA image changed from /usr/local/nvidia to /usr/local/cuda in 12.8 but will
+# return to /usr/local/nvidia in 13.0 to allow container providers to mount drivers
+# consistently from the host (see https://github.com/vllm-project/vllm/issues/18859).
+# Until then, add /usr/local/nvidia/lib64 before the image cuda path to allow override.
+ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:${LD_LIBRARY_PATH}
+
+# Copy examples and benchmarks at the end to minimize cache invalidation
+COPY examples examples
+COPY benchmarks benchmarks
+COPY ./vllm/collect_env.py .
+#################### vLLM installation IMAGE ####################
+#################### TEST IMAGE ####################
+# image to run unit testing suite
+# note that this uses vllm installed by `pip`
+FROM vllm-base AS test
+
+ADD . /vllm-workspace/
+
+ARG PYTHON_VERSION
+
+ARG PIP_INDEX_URL UV_INDEX_URL
+ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
+ARG PYTORCH_CUDA_INDEX_BASE_URL
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
+ENV UV_INDEX_STRATEGY="unsafe-best-match"
+# Use copy mode to avoid hardlink failures with Docker cache mounts
+ENV UV_LINK_MODE=copy
+
+RUN apt-get update -y \
+    && apt-get install -y git
+
+# We can specify the standard or nightly build of PyTorch
+ARG PYTORCH_NIGHTLY
+
+# Install development dependencies (for testing)
+COPY requirements/lint.txt requirements/lint.txt
+COPY requirements/test/cuda.in requirements/test/cuda.in
+COPY requirements/test/cuda.txt requirements/test/cuda.txt
+COPY requirements/dev.txt requirements/dev.txt
+COPY use_existing_torch.py use_existing_torch.py
+COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
+RUN --mount=type=cache,target=/root/.cache/uv \
+    CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
+    if [ "$CUDA_MAJOR" -ge 12 ]; then \
+        if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
+            echo "Installing dev requirements plus torch nightly..." \
+            && python3 use_existing_torch.py --prefix \
+            && cat torch_lib_versions.txt >> requirements/test/cuda.in \
+            && uv pip compile requirements/test/cuda.in -o requirements/test/cuda.txt --index-strategy unsafe-best-match \
+            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
+            && uv pip install --system $(cat torch_lib_versions.txt | xargs) --pre \
+            -r requirements/dev.txt \
+            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+        else \
+            echo "Installing dev requirements..." \
+            && uv pip install --system -r requirements/dev.txt \
+            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
+        fi \
+    fi
+
+# install development dependencies (for testing)
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install --system -e tests/vllm_test_utils
+
+# enable fast downloads from hf (for testing)
+ENV HF_XET_HIGH_PERFORMANCE 1
+
+# increase timeout for hf downloads (for testing)
+ENV HF_HUB_DOWNLOAD_TIMEOUT 60
+
+# Copy in the v1 package for testing (it isn't distributed yet)
+COPY vllm/v1 /usr/local/lib/python${PYTHON_VERSION}/dist-packages/vllm/v1
+
+# Source code is used in the `python_only_compile.sh` test
+# We hide it inside `src/` so that this source code
+# will not be imported by other tests
+RUN mkdir src
+RUN mv vllm src/vllm
+#################### TEST IMAGE ####################
+
+#################### OPENAI API SERVER ####################
+# base openai image with additional requirements, for any subsequent openai-style images
+FROM vllm-base AS vllm-openai-base
+ARG TARGETPLATFORM
+ARG INSTALL_KV_CONNECTORS=false
+ARG CUDA_VERSION
+ARG VLLM_BUILD_COMMIT
+ARG VLLM_BUILD_PIPELINE
+ARG VLLM_BUILD_URL
+ARG VLLM_IMAGE_TAG
+
+ARG PIP_INDEX_URL UV_INDEX_URL
+ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
+
+# install kv_connectors if requested
+ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
+ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=requirements/kv_connectors.txt,target=/tmp/kv_connectors.txt,ro \
+    CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
+    CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-'); \
+    CUDA_HOME=/usr/local/cuda; \
+    # lmcache requires explicit specifying CUDA_HOME
+    BUILD_PKGS="libcusparse-dev-${CUDA_VERSION_DASH} \
+                libcublas-dev-${CUDA_VERSION_DASH} \
+                libcusolver-dev-${CUDA_VERSION_DASH}"; \
+    if [ "$INSTALL_KV_CONNECTORS" = "true" ]; then \
+        if [ "$CUDA_MAJOR" -ge 13 ]; then \
+            uv pip install --system nixl-cu13; \
+        fi; \
+        uv pip install --system -r /tmp/kv_connectors.txt --no-build || ( \
+            # if the above fails, install from source
+            apt-get update -y && \
+            apt-get install -y --no-install-recommends --allow-change-held-packages ${BUILD_PKGS} && \
+            uv pip install --system -r /tmp/kv_connectors.txt --no-build-isolation && \
+            apt-get purge -y ${BUILD_PKGS} && \
+            # clean up -dev packages, keep runtime libraries
+            rm -rf /var/lib/apt/lists/* \
+        ); \
+    fi
+
+ENV VLLM_USAGE_SOURCE production-docker-image
+ENV VLLM_BUILD_COMMIT=${VLLM_BUILD_COMMIT:-unknown} \
+    VLLM_BUILD_PIPELINE=${VLLM_BUILD_PIPELINE:-local} \
+    VLLM_BUILD_URL=${VLLM_BUILD_URL:-} \
+    VLLM_IMAGE_TAG=${VLLM_IMAGE_TAG:-local/vllm-openai:dev}
+LABEL org.opencontainers.image.source="https://github.com/vllm-project/vllm" \
+      org.opencontainers.image.revision="${VLLM_BUILD_COMMIT}" \
+      org.opencontainers.image.version="${VLLM_IMAGE_TAG}" \
+      org.opencontainers.image.url="${VLLM_BUILD_URL}" \
+      ai.vllm.build.commit="${VLLM_BUILD_COMMIT}" \
+      ai.vllm.build.pipeline="${VLLM_BUILD_PIPELINE}" \
+      ai.vllm.build.url="${VLLM_BUILD_URL}" \
+      ai.vllm.image.tag="${VLLM_IMAGE_TAG}"
+
+# define sagemaker first, so it is not default from `docker build`
+FROM vllm-openai-base AS vllm-sagemaker
+
+COPY examples/online_serving/sagemaker-entrypoint.sh .
+RUN chmod +x sagemaker-entrypoint.sh
+ENTRYPOINT ["./sagemaker-entrypoint.sh"]
+
+FROM vllm-openai-base AS vllm-openai
+
+ENTRYPOINT ["vllm", "serve"]
+#################### OPENAI API SERVER ####################

From f59929f5911113976c5b719ee566dc32d0f52956 Mon Sep 17 00:00:00 2001
From: zeryx <1892175+zeryx@users.noreply.github.com>
Date: Tue, 5 May 2026 10:27:25 -0400
Subject: [PATCH 2/3] [Bugfix] Add LoRA mixin to CutlassExpertsFp4 to prevent
 Marlin fallback

When --enable-lora is set, all MoE layers have is_lora_enabled=True
regardless of whether LoRA adapters target expert weights. CutlassExpertsFp4
lacked LoRAExpertsMixin, causing is_supported_config() to reject it and
fall through to MarlinExperts (weight-only FP4 decompression instead of
native FP4 tensor core math). This is especially impactful on SM120
(Blackwell) where NVFP4 CUTLASS MoE kernels are the primary compute path.

Co-authored-by: Claude <noreply@anthropic.com>
Signed-off-by: Josh Sutton <jsutton@nvidia.com>

Signed-off-by: zeryx <1892175+zeryx@users.noreply.github.com>
---
 vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py b/vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
index fdd802e7da3a..ce5193ed0ceb 100644
--- a/vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/cutlass_moe.py
@@ -16,6 +16,9 @@
     FusedMoEParallelConfig,
     FusedMoEQuantConfig,
 )
+from vllm.model_executor.layers.fused_moe.lora_experts_mixin import (
+    LoRAExpertsMixin,
+)
 from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
     moe_permute,
     moe_unpermute,
@@ -665,7 +668,7 @@ def run_cutlass_moe_fp4(
     return
 
 
-class CutlassExpertsFp4(mk.FusedMoEExpertsModular):
+class CutlassExpertsFp4(LoRAExpertsMixin, mk.FusedMoEExpertsModular):
     """CUTLASS FP4 fused MoE expert implementation."""
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:

From e0857265321119a5608b76ae5041d12a8eac622a Mon Sep 17 00:00:00 2001
From: zeryx <1892175+zeryx@users.noreply.github.com>
Date: Tue, 5 May 2026 10:55:05 -0400
Subject: [PATCH 3/3] [Docker] Enable SM120 NVFP4 CUTLASS kernels in default
 Dockerfile

Change `TORCH_CUDA_ARCH_LIST` from `12.0+PTX` to `12.0` in the
Dockerfile, versions.json, and the openai-base stage. The `+PTX` suffix
generates virtual-only gencode (`compute_120`) which does not satisfy
CMake's `cuda_archs_loose_intersection("12.0f")` check, silently
skipping all NVFP4 SM120 CUTLASS kernels (nvfp4_scaled_mm,
nvfp4_blockwise_moe, nvfp4_experts_quant, nvfp4_kv_cache,
activation_nvfp4_quant_fusion). Bare `12.0` produces real SM code
(`sm_120`) which matches the intersection. nvcc still embeds PTX for
the highest real arch, so forward compatibility is preserved.

Also incorporates robustness fixes from Dockerfile.sm120:
- GCC-10 made optional for Ubuntu 24.04+ (ships gcc-13/14)
- Universe repo added for libxext6/libgl1 on minimal base images
- Deadsnakes PPA bypassed when Python is in base repos

Removes the separate Dockerfile.sm120 as its changes are now in the
main Dockerfile.

Co-authored-by: Claude <noreply@anthropic.com>
Signed-off-by: Josh Sutton <jsutton@nvidia.com>

Signed-off-by: zeryx <1892175+zeryx@users.noreply.github.com>
---
 docker/Dockerfile       |  18 +-
 docker/Dockerfile.sm120 | 872 ----------------------------------------
 docker/versions.json    |   2 +-
 3 files changed, 11 insertions(+), 881 deletions(-)
 delete mode 100644 docker/Dockerfile.sm120

diff --git a/docker/Dockerfile b/docker/Dockerfile
index fd0622e2416a..fd7af2d53b60 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -131,11 +131,9 @@ RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
             sudo \
             python3-pip \
             libibverbs-dev \
-            # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
-            # as it was causing spam when compiling the CUTLASS kernels
-            gcc-10 \
-            g++-10 \
-        && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
+        && (apt-get install -y --no-install-recommends gcc-10 g++-10 \
+            && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
+            || true) \
         # Install python dev headers if available (needed for cmake FindPython on Ubuntu 24.04
         # which ships cmake 3.28 and requires Development.SABIModule; silently skipped on
         # Ubuntu 20.04/22.04 where python3.x-dev is not available without a PPA)
@@ -224,7 +222,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # Explicitly set the list to avoid issues with torch 2.2
 # See https://github.com/pytorch/pytorch/pull/123243
 # From versions.json: .torch.cuda_arch_list
-ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
+ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 #################### BUILD BASE IMAGE ####################
 
@@ -533,6 +531,8 @@ RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
 
 # Install Python and system dependencies
 RUN apt-get update -y \
+    && (add-apt-repository -y universe 2>/dev/null || true) \
+    && apt-get update -y \
     && apt-get install -y --no-install-recommends \
         software-properties-common \
         curl \
@@ -541,7 +541,9 @@ RUN apt-get update -y \
         libsm6 \
         libxext6 \
         libgl1 \
-    && if [ ! -z ${DEADSNAKES_MIRROR_URL} ] ; then \
+    && if apt-cache show python${PYTHON_VERSION} >/dev/null 2>&1 ; then \
+        echo "python${PYTHON_VERSION} available in base repos, skipping deadsnakes" ; \
+    elif [ ! -z ${DEADSNAKES_MIRROR_URL} ] ; then \
         if [ ! -z "${DEADSNAKES_GPGKEY_URL}" ] ; then \
             mkdir -p -m 0755 /etc/apt/keyrings ; \
             curl -L ${DEADSNAKES_GPGKEY_URL} | gpg --dearmor > /etc/apt/keyrings/deadsnakes.gpg ; \
@@ -817,7 +819,7 @@ ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
 ENV UV_HTTP_TIMEOUT=500
 
 # install kv_connectors if requested
-ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
+ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=requirements/kv_connectors.txt,target=/tmp/kv_connectors.txt,ro \
diff --git a/docker/Dockerfile.sm120 b/docker/Dockerfile.sm120
deleted file mode 100644
index 3ac42997932f..000000000000
--- a/docker/Dockerfile.sm120
+++ /dev/null
@@ -1,872 +0,0 @@
-# The vLLM Dockerfile is used to construct vLLM image that can be directly used
-# to run the OpenAI compatible server.
-
-# Please update any changes made here to
-# docs/contributing/dockerfile/dockerfile.md and
-# docs/assets/contributing/dockerfile-stages-dependency.png
-
-# =============================================================================
-# VERSION MANAGEMENT
-# =============================================================================
-# ARG defaults in this Dockerfile are the source of truth for pinned versions.
-# docker/versions.json is auto-generated for use with docker buildx bake.
-#
-# When updating versions:
-# 1. Edit the ARG defaults below
-# 2. Run: python tools/generate_versions_json.py
-#
-# To query versions programmatically:
-#   jq -r '.variable.CUDA_VERSION.default' docker/versions.json
-#
-# To build with bake:
-#   docker buildx bake -f docker/docker-bake.hcl -f docker/versions.json
-# =============================================================================
-
-ARG CUDA_VERSION=13.0.2
-ARG PYTHON_VERSION=3.12
-ARG UBUNTU_VERSION=22.04
-
-# By parameterizing the base images, we allow third-party to use their own
-# base images. One use case is hermetic builds with base images stored in
-# private registries that use a different repository naming conventions.
-#
-# Example:
-# docker build --build-arg BUILD_BASE_IMAGE=registry.acme.org/mirror/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04
-
-# Important: We build with an old version of Ubuntu to maintain broad
-# compatibility with other Linux OSes. The main reason for this is that the
-# glibc version is baked into the distro, and binaries built with one glibc
-# version are not backwards compatible with OSes that use an earlier version.
-ARG BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04
-# Using cuda base image with minimal dependencies necessary for JIT compilation (FlashInfer, DeepGEMM, EP kernels)
-ARG FINAL_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-base-ubuntu${UBUNTU_VERSION}
-
-# OS family of BUILD_BASE_IMAGE. Controls package manager (apt vs dnf) and
-# Python bootstrap. Set to "manylinux" alongside a manylinux build base such
-# as pytorch/manylinux2_28-builder:cuda13.0 to produce wheels with a glibc
-# 2.28 floor (matches PyTorch's own published wheels). Default stays on
-# Ubuntu for backwards compatibility.
-ARG BUILD_OS=ubuntu
-
-# By parameterizing the Deadsnakes repository URL, we allow third-party to use
-# their own mirror. When doing so, we don't benefit from the transparent
-# installation of the GPG key of the PPA, as done by add-apt-repository, so we
-# also need a URL for the GPG key.
-ARG DEADSNAKES_MIRROR_URL
-ARG DEADSNAKES_GPGKEY_URL
-
-# The PyPA get-pip.py script is a self contained script+zip file, that provides
-# both the installer script and the pip base85-encoded zip archive. This allows
-# bootstrapping pip in environment where a distribution package does not exist.
-#
-# By parameterizing the URL for get-pip.py installation script, we allow
-# third-party to use their own copy of the script stored in a private mirror.
-# We set the default value to the PyPA owned get-pip.py script.
-#
-# Reference: https://pip.pypa.io/en/stable/installation/#get-pip-py
-ARG GET_PIP_URL="https://bootstrap.pypa.io/get-pip.py"
-
-# PIP supports fetching the packages from custom indexes, allowing third-party
-# to host the packages in private mirrors. The PIP_INDEX_URL and
-# PIP_EXTRA_INDEX_URL are standard PIP environment variables to override the
-# default indexes. By letting them empty by default, PIP will use its default
-# indexes if the build process doesn't override the indexes.
-#
-# Uv uses different variables. We set them by default to the same values as
-# PIP, but they can be overridden.
-ARG PIP_INDEX_URL
-ARG PIP_EXTRA_INDEX_URL
-ARG UV_INDEX_URL=${PIP_INDEX_URL}
-ARG UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-
-# PyTorch provides its own indexes for standard and nightly builds
-ARG PYTORCH_CUDA_INDEX_BASE_URL=https://download.pytorch.org/whl
-
-# PIP supports multiple authentication schemes, including keyring
-# By parameterizing the PIP_KEYRING_PROVIDER variable and setting it to
-# disabled by default, we allow third-party to use keyring authentication for
-# their private Python indexes, while not changing the default behavior which
-# is no authentication.
-#
-# Reference: https://pip.pypa.io/en/stable/topics/authentication/#keyring-support
-ARG PIP_KEYRING_PROVIDER=disabled
-ARG UV_KEYRING_PROVIDER=${PIP_KEYRING_PROVIDER}
-
-# Flag enables built-in KV-connector dependency libs into docker images
-ARG INSTALL_KV_CONNECTORS=false
-
-#################### BASE BUILD IMAGE ####################
-# prepare basic build environment
-FROM ${BUILD_BASE_IMAGE} AS base
-
-ARG CUDA_VERSION
-ARG PYTHON_VERSION
-ARG BUILD_OS
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-# Install system dependencies including build tools.
-# The Ubuntu path uses apt + deadsnakes-via-uv for Python; the manylinux path
-# (AlmaLinux 8, e.g. pytorch/manylinux2_28-builder) uses dnf and the Python
-# interpreters pre-installed at /opt/python/cpXY-cpXY/.
-RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
-        # rdma-core-devel provides libibverbs headers; ccache lives in EPEL,
-        # which the pytorch manylinux image already enables. git/curl/sudo
-        # are typically pre-installed but listed defensively.
-        dnf install -y --setopt=install_weak_deps=False \
-            ccache \
-            git \
-            curl \
-            sudo \
-            rdma-core-devel \
-        && dnf clean all \
-        && rm -rf /var/cache/dnf; \
-    else \
-        apt-get update -y \
-        && apt-get install -y --no-install-recommends \
-            ccache \
-            software-properties-common \
-            git \
-            curl \
-            sudo \
-            python3-pip \
-            libibverbs-dev \
-        && (apt-get install -y --no-install-recommends gcc-10 g++-10 \
-            && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10 \
-            || true) \
-        # Install python dev headers if available (needed for cmake FindPython on Ubuntu 24.04
-        # which ships cmake 3.28 and requires Development.SABIModule; silently skipped on
-        # Ubuntu 20.04/22.04 where python3.x-dev is not available without a PPA)
-        && (apt-get install -y --no-install-recommends python${PYTHON_VERSION}-dev 2>/dev/null || true) \
-        && rm -rf /var/lib/apt/lists/*; \
-    fi
-
-# Install uv and bootstrap /opt/venv. Both paths converge on /opt/venv so all
-# downstream stages stay distro-agnostic.
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
-    && if [ "${BUILD_OS}" = "manylinux" ]; then \
-           # manylinux images ship Python at /opt/python/cpXY-cpXY/; point uv
-           # at the matching interpreter rather than letting it fetch one.
-           PYV_NODOT=$(echo ${PYTHON_VERSION} | tr -d '.') \
-           && MANYLINUX_PY=/opt/python/cp${PYV_NODOT}-cp${PYV_NODOT}/bin/python${PYTHON_VERSION} \
-           && $HOME/.local/bin/uv venv /opt/venv --python "$MANYLINUX_PY"; \
-       else \
-           $HOME/.local/bin/uv venv /opt/venv --python ${PYTHON_VERSION}; \
-       fi \
-    && rm -f /usr/bin/python3 /usr/bin/python3-config /usr/bin/pip \
-    && ln -sf /opt/venv/bin/python3 /usr/bin/python3 \
-    && ln -sf /opt/venv/bin/python3-config /usr/bin/python3-config \
-    && ln -sf /opt/venv/bin/pip /usr/bin/pip \
-    && python3 --version && python3 -m pip --version
-
-# Activate virtual environment and add uv to PATH
-ENV PATH="/opt/venv/bin:/root/.local/bin:$PATH"
-ENV VIRTUAL_ENV="/opt/venv"
-
-# Environment for uv
-ENV UV_HTTP_TIMEOUT=500
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-ENV UV_LINK_MODE=copy
-
-# Verify GCC version
-RUN gcc --version
-
-# Enable CUDA forward compatibility by setting '-e VLLM_ENABLE_CUDA_COMPATIBILITY=1'
-# Only needed for datacenter/professional GPUs with older drivers.
-# See: https://docs.nvidia.com/deploy/cuda-compatibility/
-ENV VLLM_ENABLE_CUDA_COMPATIBILITY=0
-
-# ============================================================
-# SLOW-CHANGING DEPENDENCIES BELOW
-# These are the expensive layers that we want to cache
-# ============================================================
-
-# Install PyTorch and core CUDA dependencies
-# This is ~2GB and rarely changes
-ARG PYTORCH_CUDA_INDEX_BASE_URL
-
-WORKDIR /workspace
-
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install build and runtime dependencies, including PyTorch
-# Check whether to install torch nightly instead of release for this build
-COPY requirements/common.txt requirements/common.txt
-COPY requirements/cuda.txt requirements/cuda.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY pyproject.toml pyproject.toml
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing torch nightly..." \
-        && uv pip install --python /opt/venv/bin/python3 torch torchaudio torchvision --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-        && echo "Installing other requirements..." \
-        && /opt/venv/bin/python3 use_existing_torch.py --prefix \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        uv pip install --python /opt/venv/bin/python3 -r requirements/cuda.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
-
-# Track PyTorch lib versions used during build and match in downstream instances.
-# We do this for both nightly and release so we can strip dependencies/*.txt as needed.
-# Otherwise library dependencies can upgrade/downgrade torch incorrectly.
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip freeze | grep -i "^torch=\|^torchvision=\|^torchaudio=" > torch_lib_versions.txt \
-    && TORCH_LIB_VERSIONS=$(cat torch_lib_versions.txt | xargs) \
-    && echo "Installed torch libs: ${TORCH_LIB_VERSIONS}"
-
-# CUDA arch list used by torch
-# Explicitly set the list to avoid issues with torch 2.2
-# See https://github.com/pytorch/pytorch/pull/123243
-# From versions.json: .torch.cuda_arch_list
-ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
-ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
-#################### BUILD BASE IMAGE ####################
-
-#################### CSRC BUILD IMAGE ####################
-FROM base AS csrc-build
-ARG TARGETPLATFORM
-
-ARG PIP_INDEX_URL UV_INDEX_URL
-ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
-ARG PYTORCH_CUDA_INDEX_BASE_URL
-
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install build dependencies
-COPY requirements/build/cuda.txt requirements/build/cuda.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
-
-# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
-# Reference: https://github.com/astral-sh/uv/pull/1694
-ENV UV_HTTP_TIMEOUT=500
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-# Use copy mode to avoid hardlink failures with Docker cache mounts
-ENV UV_LINK_MODE=copy
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing build requirements without torch..." \
-        && python3 use_existing_torch.py --prefix \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
-        && echo "Installing torch nightly..." \
-        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | grep -i "^torch=" | xargs) --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing build requirements..." \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
-
-WORKDIR /workspace
-
-COPY pyproject.toml setup.py CMakeLists.txt ./
-COPY cmake cmake/
-COPY csrc csrc/
-COPY vllm/envs.py vllm/envs.py
-COPY vllm/__init__.py vllm/__init__.py
-
-# max jobs used by Ninja to build extensions
-ARG max_jobs=2
-ENV MAX_JOBS=${max_jobs}
-# number of threads used by nvcc
-ARG nvcc_threads=8
-ENV NVCC_THREADS=$nvcc_threads
-
-ARG USE_SCCACHE
-ARG SCCACHE_DOWNLOAD_URL
-ARG SCCACHE_ENDPOINT
-ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
-ARG SCCACHE_REGION_NAME=us-west-2
-ARG SCCACHE_S3_NO_CREDENTIALS=0
-
-# Flag to control whether to use pre-built vLLM wheels
-ARG VLLM_USE_PRECOMPILED=""
-ARG VLLM_MERGE_BASE_COMMIT=""
-ARG VLLM_MAIN_CUDA_VERSION=""
-
-# Use dummy version for csrc-build wheel (only .so files are extracted, version doesn't matter)
-ENV SETUPTOOLS_SCM_PRETEND_VERSION="0.0.0+csrc.build"
-
-# Use existing torch for nightly builds
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        python3 use_existing_torch.py --prefix; \
-    fi
-
-# Build the vLLM wheel
-# if USE_SCCACHE is set, use sccache to speed up compilation
-# AWS credentials mounted at ~/.aws/credentials for sccache S3 auth (optional)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=secret,id=aws-credentials,target=/root/.aws/credentials,required=false \
-    if [ "$USE_SCCACHE" = "1" ]; then \
-        echo "Installing sccache..." \
-        && case "${TARGETPLATFORM}" in \
-          linux/arm64) SCCACHE_ARCH="aarch64" ;; \
-          linux/amd64) SCCACHE_ARCH="x86_64" ;; \
-          *) echo "Unsupported TARGETPLATFORM for sccache: ${TARGETPLATFORM}" >&2; exit 1 ;; \
-        esac \
-        && export SCCACHE_DOWNLOAD_URL="${SCCACHE_DOWNLOAD_URL:-https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-${SCCACHE_ARCH}-unknown-linux-musl.tar.gz}" \
-        && curl -L -o sccache.tar.gz ${SCCACHE_DOWNLOAD_URL} \
-        && tar -xzf sccache.tar.gz \
-        && sudo mv sccache-v0.8.1-${SCCACHE_ARCH}-unknown-linux-musl/sccache /usr/bin/sccache \
-        && rm -rf sccache.tar.gz sccache-v0.8.1-${SCCACHE_ARCH}-unknown-linux-musl \
-        && if [ ! -z ${SCCACHE_ENDPOINT} ] ; then export SCCACHE_ENDPOINT=${SCCACHE_ENDPOINT} ; fi \
-        && export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
-        && export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
-        && export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
-        && export SCCACHE_IDLE_TIMEOUT=0 \
-        && export CMAKE_BUILD_TYPE=Release \
-        && export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" \
-        && export VLLM_PRECOMPILED_WHEEL_COMMIT="${VLLM_MERGE_BASE_COMMIT}" \
-        && export VLLM_MAIN_CUDA_VERSION="${VLLM_MAIN_CUDA_VERSION}" \
-        && export VLLM_DOCKER_BUILD_CONTEXT=1 \
-        && sccache --show-stats \
-        && python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
-        && sccache --show-stats; \
-    fi
-
-ARG vllm_target_device="cuda"
-ENV VLLM_TARGET_DEVICE=${vllm_target_device}
-ENV CCACHE_DIR=/root/.cache/ccache
-RUN --mount=type=cache,target=/root/.cache/ccache \
-    --mount=type=cache,target=/root/.cache/uv \
-    if [ "$USE_SCCACHE" != "1" ]; then \
-        # Clean any existing CMake artifacts
-        rm -rf .deps && \
-        mkdir -p .deps && \
-        export VLLM_USE_PRECOMPILED="${VLLM_USE_PRECOMPILED}" && \
-        export VLLM_PRECOMPILED_WHEEL_COMMIT="${VLLM_MERGE_BASE_COMMIT}" && \
-        export VLLM_DOCKER_BUILD_CONTEXT=1 && \
-        python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
-    fi
-
-#################### CSRC BUILD IMAGE ####################
-
-#################### EXTENSIONS BUILD IMAGE ####################
-# Build DeepEP - runs in PARALLEL with csrc-build
-# This stage is independent and doesn't affect csrc cache
-FROM base AS extensions-build
-ARG CUDA_VERSION
-
-# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
-ENV UV_HTTP_TIMEOUT=500
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-ENV UV_LINK_MODE=copy
-
-WORKDIR /workspace
-
-# Build DeepEP wheels
-COPY tools/ep_kernels/install_python_libraries.sh /tmp/install_python_libraries.sh
-# Defaults moved here from tools/ep_kernels/install_python_libraries.sh for centralized version management
-ARG DEEPEP_COMMIT_HASH=73b6ea4
-ARG NVSHMEM_VER
-RUN --mount=type=cache,target=/root/.cache/uv \
-    mkdir -p /tmp/ep_kernels_workspace/dist && \
-    export TORCH_CUDA_ARCH_LIST='9.0a 10.0a' && \
-    /tmp/install_python_libraries.sh \
-        --workspace /tmp/ep_kernels_workspace \
-        --mode wheel \
-        ${DEEPEP_COMMIT_HASH:+--deepep-ref "$DEEPEP_COMMIT_HASH"} \
-        ${NVSHMEM_VER:+--nvshmem-ver "$NVSHMEM_VER"} && \
-    find /tmp/ep_kernels_workspace/nvshmem -name '*.a' -delete
-#################### EXTENSIONS BUILD IMAGE ####################
-
-#################### WHEEL BUILD IMAGE ####################
-FROM base AS build
-ARG TARGETPLATFORM
-
-ARG PIP_INDEX_URL UV_INDEX_URL
-ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
-ARG PYTORCH_CUDA_INDEX_BASE_URL
-
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install build dependencies
-COPY requirements/build/cuda.txt requirements/build/cuda.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
-
-# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
-# Reference: https://github.com/astral-sh/uv/pull/1694
-ENV UV_HTTP_TIMEOUT=500
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-# Use copy mode to avoid hardlink failures with Docker cache mounts
-ENV UV_LINK_MODE=copy
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing build requirements without torch..." \
-        && python3 use_existing_torch.py --prefix \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
-        && echo "Installing torch nightly..." \
-        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | grep -i "^torch=" | xargs) --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing build requirements..." \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/build/cuda.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
-
-WORKDIR /workspace
-
-# Copy pre-built csrc wheel directly
-COPY --from=csrc-build /workspace/dist /precompiled-wheels
-COPY . .
-
-ARG GIT_REPO_CHECK=0
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
-
-ARG vllm_target_device="cuda"
-ENV VLLM_TARGET_DEVICE=${vllm_target_device}
-
-# Skip adding +precompiled suffix to version (preserves git-derived version)
-ENV VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX=1
-
-# Use existing torch for nightly builds
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        python3 use_existing_torch.py --prefix; \
-    fi
-
-# Build the vLLM wheel
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=.git,target=.git \
-    if [ "${vllm_target_device}" = "cuda" ]; then \
-        export VLLM_PRECOMPILED_WHEEL_LOCATION=$(ls /precompiled-wheels/*.whl); \
-    fi && \
-    python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38
-
-# Copy extension wheels from extensions-build stage for later use
-COPY --from=extensions-build /tmp/ep_kernels_workspace/dist /tmp/ep_kernels_workspace/dist
-
-# Check the size of the wheel if RUN_WHEEL_CHECK is true
-COPY .buildkite/check-wheel-size.py check-wheel-size.py
-# sync the default value with .buildkite/check-wheel-size.py
-ARG VLLM_MAX_SIZE_MB=500
-ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
-ARG RUN_WHEEL_CHECK=true
-RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
-        python3 check-wheel-size.py dist; \
-    else \
-        echo "Skipping wheel size check."; \
-    fi
-
-#################### WHEEL BUILD IMAGE ####################
-
-#################### DEV IMAGE ####################
-FROM base AS dev
-
-ARG PIP_INDEX_URL UV_INDEX_URL
-ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
-ARG PYTORCH_CUDA_INDEX_BASE_URL
-ARG BUILD_OS
-
-# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
-# Reference: https://github.com/astral-sh/uv/pull/1694
-ENV UV_HTTP_TIMEOUT=500
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-# Use copy mode to avoid hardlink failures with Docker cache mounts
-ENV UV_LINK_MODE=copy
-
-# Install libnuma-dev, required by fastsafetensors (fixes #20384)
-RUN if [ "${BUILD_OS}" = "manylinux" ]; then \
-        dnf install -y numactl-devel && dnf clean all && rm -rf /var/cache/dnf; \
-    else \
-        apt-get update && apt-get install -y --no-install-recommends libnuma-dev && rm -rf /var/lib/apt/lists/*; \
-    fi
-
-
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install development dependencies
-COPY requirements/lint.txt requirements/lint.txt
-COPY requirements/test/cuda.in requirements/test/cuda.in
-COPY requirements/test/cuda.txt requirements/test/cuda.txt
-COPY requirements/dev.txt requirements/dev.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing dev requirements plus torch nightly..." \
-        && python3 use_existing_torch.py --prefix \
-        && cat torch_lib_versions.txt >> requirements/test/cuda.in \
-        && uv pip compile requirements/test/cuda.in -o requirements/test/cuda.txt --index-strategy unsafe-best-match \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-        && uv pip install --python /opt/venv/bin/python3 $(cat torch_lib_versions.txt | xargs) --pre \
-        -r requirements/dev.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing dev requirements..." \
-        && uv pip install --python /opt/venv/bin/python3 -r requirements/dev.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
-
-#################### DEV IMAGE ####################
-#################### vLLM installation IMAGE ####################
-# image with vLLM installed
-FROM ${FINAL_BASE_IMAGE} AS vllm-base
-
-ARG CUDA_VERSION
-ARG PYTHON_VERSION
-ARG DEADSNAKES_MIRROR_URL
-ARG DEADSNAKES_GPGKEY_URL
-ARG GET_PIP_URL
-
-ENV DEBIAN_FRONTEND=noninteractive
-WORKDIR /vllm-workspace
-
-
-# Python version string for paths (e.g., "312" for 3.12)
-RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
-    echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
-
-# Install Python and system dependencies
-RUN apt-get update -y \
-    && (add-apt-repository -y universe 2>/dev/null || true) \
-    && apt-get update -y \
-    && apt-get install -y --no-install-recommends \
-        software-properties-common \
-        curl \
-        sudo \
-        ffmpeg \
-        libsm6 \
-        libxext6 \
-        libgl1 \
-    && if apt-cache show python${PYTHON_VERSION} >/dev/null 2>&1 ; then \
-        echo "python${PYTHON_VERSION} available in base repos, skipping deadsnakes" ; \
-    elif [ ! -z ${DEADSNAKES_MIRROR_URL} ] ; then \
-        if [ ! -z "${DEADSNAKES_GPGKEY_URL}" ] ; then \
-            mkdir -p -m 0755 /etc/apt/keyrings ; \
-            curl -L ${DEADSNAKES_GPGKEY_URL} | gpg --dearmor > /etc/apt/keyrings/deadsnakes.gpg ; \
-            sudo chmod 644 /etc/apt/keyrings/deadsnakes.gpg ; \
-            echo "deb [signed-by=/etc/apt/keyrings/deadsnakes.gpg] ${DEADSNAKES_MIRROR_URL} $(lsb_release -cs) main" > /etc/apt/sources.list.d/deadsnakes.list ; \
-        fi ; \
-    else \
-        for i in 1 2 3; do \
-            add-apt-repository -y ppa:deadsnakes/ppa && break || \
-            { echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
-        done ; \
-    fi \
-    && apt-get update -y \
-    && apt-get install -y --no-install-recommends \
-        python${PYTHON_VERSION} \
-        python${PYTHON_VERSION}-dev \
-        python${PYTHON_VERSION}-venv \
-        libibverbs-dev \
-    && rm -rf /var/lib/apt/lists/* \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
-    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
-    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
-    && rm -f /usr/lib/python${PYTHON_VERSION}/EXTERNALLY-MANAGED \
-    && curl -sS ${GET_PIP_URL} | python${PYTHON_VERSION} \
-    && python3 --version && python3 -m pip --version
-
-# Install CUDA development tools for runtime JIT compilation
-# (FlashInfer, DeepGEMM, EP kernels all require compilation at runtime)
-RUN CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-') && \
-    CUDA_VERSION_SHORT=$(echo $CUDA_VERSION | cut -d. -f1,2) && \
-    apt-get update -y && \
-    apt-get install -y --no-install-recommends --allow-change-held-packages \
-        cuda-nvcc-${CUDA_VERSION_DASH} \
-        cuda-cudart-${CUDA_VERSION_DASH} \
-        cuda-nvrtc-${CUDA_VERSION_DASH} \
-        cuda-cuobjdump-${CUDA_VERSION_DASH} \
-        libcurand-dev-${CUDA_VERSION_DASH} \
-        libcublas-dev-${CUDA_VERSION_DASH} \
-        # Required by fastsafetensors (fixes #20384)
-        libnuma-dev \
-        # numactl CLI for NUMA binding at runtime
-        numactl && \
-    # Fixes nccl_allocator requiring nccl.h at runtime
-    # https://github.com/vllm-project/vllm/blob/1336a1ea244fa8bfd7e72751cabbdb5b68a0c11a/vllm/distributed/device_communicators/pynccl_allocator.py#L22
-    # NCCL packages don't use the cuda-MAJOR-MINOR naming convention,
-    # so we pin the version to match our CUDA version
-    NCCL_VER=$(apt-cache madison libnccl-dev | grep "+cuda${CUDA_VERSION_SHORT}" | head -1 | awk -F'|' '{gsub(/^ +| +$/, "", $2); print $2}') && \
-    apt-get install -y --no-install-recommends --allow-change-held-packages libnccl-dev=${NCCL_VER} libnccl2=${NCCL_VER} && \
-    rm -rf /var/lib/apt/lists/*
-
-# Install uv for faster pip installs
-RUN python3 -m pip install uv
-
-# Environment for uv
-ENV UV_HTTP_TIMEOUT=500
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-ENV UV_LINK_MODE=copy
-
-# Enable CUDA forward compatibility by setting '-e VLLM_ENABLE_CUDA_COMPATIBILITY=1'
-# Only needed for datacenter/professional GPUs with older drivers.
-# See: https://docs.nvidia.com/deploy/cuda-compatibility/
-ENV VLLM_ENABLE_CUDA_COMPATIBILITY=0
-
-# ============================================================
-# SLOW-CHANGING DEPENDENCIES BELOW
-# These are the expensive layers that we want to cache
-# ============================================================
-
-# Install PyTorch and core CUDA dependencies
-# This is ~2GB and rarely changes
-ARG PYTORCH_CUDA_INDEX_BASE_URL
-COPY requirements/common.txt /tmp/common.txt
-COPY requirements/cuda.txt /tmp/requirements-cuda.txt
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r /tmp/requirements-cuda.txt \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') && \
-    rm /tmp/requirements-cuda.txt /tmp/common.txt
-
-# Install FlashInfer JIT cache (requires CUDA-version-specific index URL)
-# https://docs.flashinfer.ai/installation.html
-# From versions.json: .flashinfer.version
-ARG FLASHINFER_VERSION=0.6.8.post1
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
-        --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
-
-# ============================================================
-# OPENAI API SERVER DEPENDENCIES
-# Pre-install these to avoid reinstalling on every vLLM wheel rebuild
-# ============================================================
-
-# Install gdrcopy (saves ~6s per build)
-# TODO (huydhn): There is no prebuilt gdrcopy package on 12.9 at the moment
-ARG GDRCOPY_CUDA_VERSION=12.8
-ARG GDRCOPY_OS_VERSION=Ubuntu22_04
-ARG TARGETPLATFORM
-COPY tools/install_gdrcopy.sh /tmp/install_gdrcopy.sh
-RUN set -eux; \
-    case "${TARGETPLATFORM}" in \
-      linux/arm64) UUARCH="aarch64" ;; \
-      linux/amd64) UUARCH="x64" ;; \
-      *) echo "Unsupported TARGETPLATFORM: ${TARGETPLATFORM}" >&2; exit 1 ;; \
-    esac; \
-    /tmp/install_gdrcopy.sh "${GDRCOPY_OS_VERSION}" "${GDRCOPY_CUDA_VERSION}" "${UUARCH}" && \
-    rm /tmp/install_gdrcopy.sh
-
-# Install vllm-openai dependencies (saves ~2.6s per build)
-# These are stable packages that don't depend on vLLM itself
-# From versions.json: .bitsandbytes.x86_64, .bitsandbytes.arm64
-# From versions.json: .openai_server_extras.timm, .openai_server_extras.runai_model_streamer
-ARG BITSANDBYTES_VERSION_X86=0.46.1
-ARG BITSANDBYTES_VERSION_ARM64=0.42.0
-ARG TIMM_VERSION=">=1.0.17"
-ARG RUNAI_MODEL_STREAMER_VERSION=">=0.15.7"
-RUN --mount=type=cache,target=/root/.cache/uv \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_ARM64}"; \
-    else \
-        BITSANDBYTES_VERSION="${BITSANDBYTES_VERSION_X86}"; \
-    fi; \
-    uv pip install --system accelerate modelscope \
-        "bitsandbytes>=${BITSANDBYTES_VERSION}" "timm${TIMM_VERSION}" "runai-model-streamer[s3,gcs,azure]${RUNAI_MODEL_STREAMER_VERSION}"
-
-# ============================================================
-# VLLM INSTALLATION (depends on build stage)
-# ============================================================
-
-ARG PIP_INDEX_URL UV_INDEX_URL
-ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
-ARG PYTORCH_CUDA_INDEX_BASE_URL
-ARG PIP_KEYRING_PROVIDER UV_KEYRING_PROVIDER
-
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install vLLM wheel first, so that torch etc will be installed.
-# Check whether to install torch nightly instead of release for this build.
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
-RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
-    --mount=type=cache,target=/root/.cache/uv \
-    if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-        echo "Installing torch nightly..." \
-        && uv pip install --system $(cat torch_lib_versions.txt | xargs) --pre \
-        --index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-        && echo "Installing vLLM..." \
-        && uv pip install --system dist/*.whl --verbose \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    else \
-        echo "Installing vLLM..." \
-        && uv pip install --system dist/*.whl --verbose \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-    fi
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-. /etc/environment && \
-uv pip list
-
-# Pytorch now installs NVSHMEM, setting LD_LIBRARY_PATH
-ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
-
-# Install EP kernels wheels (DeepEP) that have been built in the `build` stage
-RUN --mount=type=bind,from=build,src=/tmp/ep_kernels_workspace/dist,target=/vllm-workspace/ep_kernels/dist \
-    --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system ep_kernels/dist/*.whl --verbose \
-        --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
-
-# Download FlashInfer precompiled cubins AFTER all pip installs are done.
-# This must run after the vLLM wheel and EP kernels installs above, because
-# those can reinstall/touch flashinfer packages. Downloading cubins earlier
-# (in the flashinfer-jit-cache layer) causes ~2.5 GB of layer duplication
-# when a later pip install overwrites flashinfer package files.
-RUN flashinfer show-config && flashinfer download-cubin
-
-# CUDA image changed from /usr/local/nvidia to /usr/local/cuda in 12.8 but will
-# return to /usr/local/nvidia in 13.0 to allow container providers to mount drivers
-# consistently from the host (see https://github.com/vllm-project/vllm/issues/18859).
-# Until then, add /usr/local/nvidia/lib64 before the image cuda path to allow override.
-ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:${LD_LIBRARY_PATH}
-
-# Copy examples and benchmarks at the end to minimize cache invalidation
-COPY examples examples
-COPY benchmarks benchmarks
-COPY ./vllm/collect_env.py .
-#################### vLLM installation IMAGE ####################
-#################### TEST IMAGE ####################
-# image to run unit testing suite
-# note that this uses vllm installed by `pip`
-FROM vllm-base AS test
-
-ADD . /vllm-workspace/
-
-ARG PYTHON_VERSION
-
-ARG PIP_INDEX_URL UV_INDEX_URL
-ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
-ARG PYTORCH_CUDA_INDEX_BASE_URL
-
-# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
-# Reference: https://github.com/astral-sh/uv/pull/1694
-ENV UV_HTTP_TIMEOUT=500
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-# Use copy mode to avoid hardlink failures with Docker cache mounts
-ENV UV_LINK_MODE=copy
-
-RUN apt-get update -y \
-    && apt-get install -y git
-
-# We can specify the standard or nightly build of PyTorch
-ARG PYTORCH_NIGHTLY
-
-# Install development dependencies (for testing)
-COPY requirements/lint.txt requirements/lint.txt
-COPY requirements/test/cuda.in requirements/test/cuda.in
-COPY requirements/test/cuda.txt requirements/test/cuda.txt
-COPY requirements/dev.txt requirements/dev.txt
-COPY use_existing_torch.py use_existing_torch.py
-COPY --from=base /workspace/torch_lib_versions.txt torch_lib_versions.txt
-RUN --mount=type=cache,target=/root/.cache/uv \
-    CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
-    if [ "$CUDA_MAJOR" -ge 12 ]; then \
-        if [ "${PYTORCH_NIGHTLY}" = "1" ]; then \
-            echo "Installing dev requirements plus torch nightly..." \
-            && python3 use_existing_torch.py --prefix \
-            && cat torch_lib_versions.txt >> requirements/test/cuda.in \
-            && uv pip compile requirements/test/cuda.in -o requirements/test/cuda.txt --index-strategy unsafe-best-match \
-            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') \
-            && uv pip install --system $(cat torch_lib_versions.txt | xargs) --pre \
-            -r requirements/dev.txt \
-            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-        else \
-            echo "Installing dev requirements..." \
-            && uv pip install --system -r requirements/dev.txt \
-            --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
-        fi \
-    fi
-
-# install development dependencies (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -e tests/vllm_test_utils
-
-# enable fast downloads from hf (for testing)
-ENV HF_XET_HIGH_PERFORMANCE 1
-
-# increase timeout for hf downloads (for testing)
-ENV HF_HUB_DOWNLOAD_TIMEOUT 60
-
-# Copy in the v1 package for testing (it isn't distributed yet)
-COPY vllm/v1 /usr/local/lib/python${PYTHON_VERSION}/dist-packages/vllm/v1
-
-# Source code is used in the `python_only_compile.sh` test
-# We hide it inside `src/` so that this source code
-# will not be imported by other tests
-RUN mkdir src
-RUN mv vllm src/vllm
-#################### TEST IMAGE ####################
-
-#################### OPENAI API SERVER ####################
-# base openai image with additional requirements, for any subsequent openai-style images
-FROM vllm-base AS vllm-openai-base
-ARG TARGETPLATFORM
-ARG INSTALL_KV_CONNECTORS=false
-ARG CUDA_VERSION
-ARG VLLM_BUILD_COMMIT
-ARG VLLM_BUILD_PIPELINE
-ARG VLLM_BUILD_URL
-ARG VLLM_IMAGE_TAG
-
-ARG PIP_INDEX_URL UV_INDEX_URL
-ARG PIP_EXTRA_INDEX_URL UV_EXTRA_INDEX_URL
-
-# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
-# Reference: https://github.com/astral-sh/uv/pull/1694
-ENV UV_HTTP_TIMEOUT=500
-
-# install kv_connectors if requested
-ARG torch_cuda_arch_list='7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX'
-ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=requirements/kv_connectors.txt,target=/tmp/kv_connectors.txt,ro \
-    CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
-    CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-'); \
-    CUDA_HOME=/usr/local/cuda; \
-    # lmcache requires explicit specifying CUDA_HOME
-    BUILD_PKGS="libcusparse-dev-${CUDA_VERSION_DASH} \
-                libcublas-dev-${CUDA_VERSION_DASH} \
-                libcusolver-dev-${CUDA_VERSION_DASH}"; \
-    if [ "$INSTALL_KV_CONNECTORS" = "true" ]; then \
-        if [ "$CUDA_MAJOR" -ge 13 ]; then \
-            uv pip install --system nixl-cu13; \
-        fi; \
-        uv pip install --system -r /tmp/kv_connectors.txt --no-build || ( \
-            # if the above fails, install from source
-            apt-get update -y && \
-            apt-get install -y --no-install-recommends --allow-change-held-packages ${BUILD_PKGS} && \
-            uv pip install --system -r /tmp/kv_connectors.txt --no-build-isolation && \
-            apt-get purge -y ${BUILD_PKGS} && \
-            # clean up -dev packages, keep runtime libraries
-            rm -rf /var/lib/apt/lists/* \
-        ); \
-    fi
-
-ENV VLLM_USAGE_SOURCE production-docker-image
-ENV VLLM_BUILD_COMMIT=${VLLM_BUILD_COMMIT:-unknown} \
-    VLLM_BUILD_PIPELINE=${VLLM_BUILD_PIPELINE:-local} \
-    VLLM_BUILD_URL=${VLLM_BUILD_URL:-} \
-    VLLM_IMAGE_TAG=${VLLM_IMAGE_TAG:-local/vllm-openai:dev}
-LABEL org.opencontainers.image.source="https://github.com/vllm-project/vllm" \
-      org.opencontainers.image.revision="${VLLM_BUILD_COMMIT}" \
-      org.opencontainers.image.version="${VLLM_IMAGE_TAG}" \
-      org.opencontainers.image.url="${VLLM_BUILD_URL}" \
-      ai.vllm.build.commit="${VLLM_BUILD_COMMIT}" \
-      ai.vllm.build.pipeline="${VLLM_BUILD_PIPELINE}" \
-      ai.vllm.build.url="${VLLM_BUILD_URL}" \
-      ai.vllm.image.tag="${VLLM_IMAGE_TAG}"
-
-# define sagemaker first, so it is not default from `docker build`
-FROM vllm-openai-base AS vllm-sagemaker
-
-COPY examples/online_serving/sagemaker-entrypoint.sh .
-RUN chmod +x sagemaker-entrypoint.sh
-ENTRYPOINT ["./sagemaker-entrypoint.sh"]
-
-FROM vllm-openai-base AS vllm-openai
-
-ENTRYPOINT ["vllm", "serve"]
-#################### OPENAI API SERVER ####################
diff --git a/docker/versions.json b/docker/versions.json
index 75652823db0b..5abe35298ab8 100644
--- a/docker/versions.json
+++ b/docker/versions.json
@@ -35,7 +35,7 @@
       "default": "false"
     },
     "TORCH_CUDA_ARCH_LIST": {
-      "default": "7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0+PTX"
+      "default": "7.5 8.0 8.6 8.9 9.0 10.0 11.0 12.0"
     },
     "MAX_JOBS": {
       "default": "2"