:mega::mega: Announcement :mega::mega:

TensorRT 11.0 is coming soon in 2026 Q2 with powerful new capabilities designed to accelerate your AI inference workflows. With this major version bump, TensorRT's API will be streamlined and a few legacy features will be removed.

We recommend migrating early for the following features:

• Weakly-typed networks and related APIs will be removed, replaced by Strongly Typed Networks.
• Implicit quantization and related APIs will be removed, replaced by Explicit Quantization
• IPluginV2 and related APIs will be removed, replaced by IPluginV3
• TREX tool will be removed, replaced by Nsight Deep Learning Designer
• Python bindings for Python 3.9 and older versions will be removed starting TensorRT 10.16. RPM packages for RHEL/Rocky Linux 8 and RHEL/Rocky Linux 9 now depend on Python 3.12.

TensorRT Open Source Software

This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. It includes the sources for TensorRT plugins and ONNX parser, as well as sample applications demonstrating usage and capabilities of the TensorRT platform. These open source software components are a subset of the TensorRT General Availability (GA) release with some extensions and bug-fixes.

• For code contributions to TensorRT-OSS, please see our Contribution Guide and Coding Guidelines.
• For a summary of new additions and updates shipped with TensorRT-OSS releases, please refer to the Changelog.
• For business inquiries, please contact researchinquiries@nvidia.com
• For press and other inquiries, please contact Hector Marinez at hmarinez@nvidia.com

Need enterprise support? NVIDIA global support is available for TensorRT with the NVIDIA AI Enterprise software suite. Check out NVIDIA LaunchPad for free access to a set of hands-on labs with TensorRT hosted on NVIDIA infrastructure.

Join the TensorRT and Triton community and stay current on the latest product updates, bug fixes, content, best practices, and more.

Prebuilt TensorRT Python Package

We provide the TensorRT Python package for an easy installation.
To install:

pip install tensorrt

You can skip the Build section to enjoy TensorRT with Python.

Build

Prerequisites

To build the TensorRT-OSS components, you will first need the following software packages.

TensorRT GA build

• TensorRT v10.16.0.72
  • Available from direct download links listed below

System Packages

• CUDA
  • Recommended versions:
  • cuda-13.2.0
  • cuda-12.9.0
• CUDNN (optional)
  • cuDNN 8.9
• GNU make >= v4.1
• cmake >= v3.31
• python >= v3.10, <= v3.13.x
• pip >= v19.0
• Essential utilities
  • git, pkg-config, wget

Optional Packages

• NCCL >= v2.19, < v3.0 — only when building with multi-device support (-DTRT_BUILD_ENABLE_MULTIDEVICE=ON) for the sampleDistCollective sample.

• Containerized build

  • Docker >= 19.03
  • NVIDIA Container Toolkit

• PyPI packages (for demo applications/tests)

  • onnx
  • onnxruntime
  • tensorflow-gpu >= 2.5.1
  • Pillow >= 9.0.1
  • pycuda < 2021.1
  • numpy
  • pytest

• Code formatting tools (for contributors)

  • Clang-format
  • Git-clang-format

  NOTE: onnx-tensorrt, cub, and protobuf packages are downloaded along with TensorRT OSS, and not required to be installed.

Downloading TensorRT Build

1. Download TensorRT OSS

   git clone -b main https://github.com/nvidia/TensorRT TensorRT
   cd TensorRT
   git submodule update --init --recursive
   

2. (Optional - if not using TensorRT container) Specify the TensorRT GA release build path

   If using the TensorRT OSS build container, TensorRT libraries are preinstalled under /usr/lib/x86_64-linux-gnu and you may skip this step.

   Else download and extract the TensorRT GA build from NVIDIA Developer Zone with the direct links below:

   • TensorRT 10.16.0.72 for CUDA 13.2, Linux x86_64
   • TensorRT 10.16.0.72 for CUDA 12.9, Linux x86_64
   • TensorRT 10.16.0.72 for CUDA 13.2, Windows x86_64
   • TensorRT 10.16.0.72 for CUDA 12.9, Windows x86_64

   Example: Ubuntu 22.04 on x86-64 with cuda-13.2

   cd ~/Downloads
   tar -xvzf TensorRT-10.16.0.72.Linux.x86_64-gnu.cuda-13.2.tar.gz
   export TRT_LIBPATH=`pwd`/TensorRT-10.16.0.72/lib
   

   Example: Windows on x86-64 with cuda-12.9

   Expand-Archive -Path TensorRT-10.16.0.72.Windows.win10.cuda-12.9.zip
   $env:TRT_LIBPATH="$pwd\TensorRT-10.16.0.72\lib"
   

Setting Up The Build Environment

For Linux platforms, we recommend that you generate a docker container for building TensorRT OSS as described below. For native builds, please install the prerequisite System Packages.

1. Generate the TensorRT-OSS build container.

   Example: Ubuntu 24.04 on x86-64 with cuda-13.2 (default)

   ./docker/build.sh --file docker/ubuntu-24.04.Dockerfile --tag tensorrt-ubuntu24.04-cuda13.2
   

   Example: Rockylinux8 on x86-64 with cuda-13.2

   ./docker/build.sh --file docker/rockylinux8.Dockerfile --tag tensorrt-rockylinux8-cuda13.2
   

   Example: Ubuntu 24.04 cross-compile for Jetson (aarch64) with cuda-13.2 (JetPack SDK)

   ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-jetpack-cuda13.2
   

   Example: Ubuntu 24.04 on aarch64 with cuda-13.2

   ./docker/build.sh --file docker/ubuntu-24.04-aarch64.Dockerfile --tag tensorrt-aarch64-ubuntu24.04-cuda13.2
   

2. Launch the TensorRT-OSS build container.

   Example: Ubuntu 24.04 build container

   ./docker/launch.sh --tag tensorrt-ubuntu24.04-cuda13.2 --gpus all
   

   NOTE: <br> 1. Use the --tag corresponding to build container generated in Step 1. <br> 2. NVIDIA Container Toolkit is required for GPU access (running TensorRT applications) inside the build container. <br> 3. sudo password for Ubuntu build containers is 'nvidia'. <br> 4. Specify port number using --jupyter <port> for launching Jupyter notebooks. <br> 5. Write permission to this folder is required as this folder will be mounted inside the docker container for uid:gid of 1000:1000.

Building TensorRT-OSS

• Generate Makefiles and build

  Example: Linux (x86-64) build with default cuda-13.2

  cd $TRT_OSSPATH
  mkdir -p build && cd build
  cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out
  make -j$(nproc)
  

  Example: Linux (aarch64) build with default cuda-13.2

  cd $TRT_OSSPATH
  mkdir -p build && cd build
  cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out -DCMAKE_TOOLCHAIN_FILE=$TRT_OSSPATH/cmake/toolchains/cmake_aarch64-native.toolchain
  make -j$(nproc)
  

  Example: Native build on Jetson Thor (aarch64) with cuda-13.2

  cd $TRT_OSSPATH
  mkdir -p build && cd build
  cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out -DTRT_PLATFORM_ID=aarch64
  CC=/usr/bin/gcc make -j$(nproc)
  

  NOTE: C compiler must be explicitly specified via CC= for native aarch64 builds of protobuf.

  **Example: Ubuntu 24.04 Cross-Compile for Jet