NVIDIA
NVIDIA
TensorRT LLM Release
Container
NVIDIA
NVIDIA
TensorRT LLM Release

TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.

  • LayerLabelCreated
    sha256:125af95f65885cd965d2f8a5a3d2cd6b07fe8d4024fc3926314b4204b1e31f5cRUN
    GIT_COMMIT=a8c595521e306b8fa60ddeaa533152f8052e1ac1 TRT_LLM_VER=1.3.0rc19 TARGETARCH=amd64 /bin/bash -c bash /mnt/gen_attribution.sh "release" "${TRT_LLM_VER}" "${TARGETARCH}"
    06/22/2026 4:08 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4ENV
    TRT_LLM_GIT_COMMIT=a8c595521e306b8fa60ddeaa533152f8052e1ac1 TRT_LLM_VERSION=1.3.0rc19
    06/22/2026 4:08 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4ARG
    TARGETARCH=amd64
    06/22/2026 4:08 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4ARG
    TRT_LLM_VER=1.3.0rc19
    06/22/2026 4:08 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4ARG
    GIT_COMMIT=a8c595521e306b8fa60ddeaa533152f8052e1ac1
    06/22/2026 4:08 AM UTC
    sha256:92ca06838217d0997f976cb4007329ff951aaa632bee37bb6ee4d5acacb9dcb0RUN
    /bin/bash -c cp /mnt/ctx/README.md ./ &&
      cp -r /mnt/ctx/docs ./docs &&
      cp -r /mnt/ctx/include ./include &&
      cp -r /mnt/ctx/examples ./examples &&
      chmod -R a+w examples &&
      cp /mnt/wheel/tensorrt_llm*.whl ./ &&
      cp -r /mnt/benchmarks ./benchmarks &&
      mkdir -p benchmarks/cpp &&
      cp /mnt/cpp_benchmarks/bertBenchmark /mnt/cpp_benchmarks/gptManagerBenchmark /mnt/cpp_benchmarks/disaggServerBenchmark benchmarks/cpp/ &&
      rm -v benchmarks/cpp/bertBenchmark.cpp benchmarks/cpp/gptManagerBenchmark.cpp benchmarks/cpp/disaggServerBenchmark.cpp benchmarks/cpp/CMakeLists.txt &&
      ln -sv $(python3 -c 'import site; print(f"{site.getsitepackages()[0]}/tensorrt_llm/bin")') bin &&
      test -f bin/executorWorker &&
      ln -sv $(python3 -c 'import site; print(f"{site.getsitepackages()[0]}/tensorrt_llm/libs")') lib &&
      test -f lib/libnvinfer_plugin_tensorrt_llm.so &&
      echo "/app/tensorrt_llm/lib" > /etc/ld.so.conf.d/tensorrt_llm.conf &&
      ldconfig &&
      ! ( ldd -v bin/executorWorker | grep tensorrt_llm | grep -q "not found" ) &&
      rm -rf /root/.cache/uv/archive-v0 &&
      rm -rf /usr/local/lib/python3.12/dist-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info &&
      rm -rf /usr/local/lib/python3.12/dist-packages/setuptools/_vendor/wheel-0.45.1.dist-info
    06/22/2026 4:08 AM UTC
    sha256:919a3dcbe33b87d356e80365e7c478287e68e00729ace1f5d2475de540c25599RUN
    /bin/bash -c pip install /tmp/wheel/tensorrt_llm*.whl
    06/22/2026 4:08 AM UTC
    sha256:e22b1b64c1b35c2f7b6588f017243b8e60ed80ca6529546be906d39c17e71f51WORKDIR
    /app/tensorrt_llm
    06/22/2026 4:01 AM UTC
    sha256:981d91b6eb23927b2f225149d81bd06ecdb83c39e0ea79b274a3c37f9f2fd3e5RUN
    SH_ENV=/etc/shinit_v2 BASH_ENV=/etc/bash.bashrc GITHUB_MIRROR=https://urm.nvidia.com/artifactory/github-go-remote PYTHON_VERSION=3.12.3 TRT_VER= CUDA_VER= CUDNN_VER= NCCL_VER= CUBLAS_VER= TORCH_INSTALL_TYPE=skip TRT_LLM_VER=1.3.0rc19 TARGETARCH=amd64 /bin/bash -c bash /tmp/gen_attribution.sh "devel" "${TRT_LLM_VER}" "${TARGETARCH}"
    06/22/2026 3:57 AM UTC
    sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4ARG
    TARGETARCH=amd64
    06/22/2026 3:57 AM UTC
    ...

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