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Method 3: RPM Package Installation#

Recommended for: System-wide installation, C++ and Python development, RHEL/CentOS/Fedora users

Advantages:

✓ Automatic dependency installation ✓ Integrates with system package manager ✓ Includes C++ headers ✓ Easy updates with dnf

Limitations:

✗ Requires sudo or root privileges ✗ Fixed installation location (/usr) ✗ Only one minor version of TensorRT can be installed at a time ✗ Linux only (RHEL/CentOS/Fedora)

Platform Support#

Supported Operating Systems:

  • RHEL 8, 9 (x86-64)

  • Rocky Linux 8, 9 (x86-64)

Prerequisites:

  • CUDA Toolkit installed using RPM packages

  • sudo or root access

Tip

Choose your installation method:

  • Local Repo (below): For new users or complete developer installation

  • Network Repo (refer to Network Repo Method): For advanced users, containers, or automation

Installation Steps (Local Repo Method)#

Note

  • Before issuing commands, replace rhelx, 10.x.x, and cuda-x.x with your specific OS, TensorRT, and CUDA versions.

  • When installing Python packages using this method, you must manually install dependencies with pip.

Step 1: Download the TensorRT RPM repository package

From the TensorRT download page, download the RPM repository package for your CUDA version and OS.

Example filename: nv-tensorrt-local-repo-rhel8-10.16.1-cuda-13.2-1.0-1.x86_64.rpm

Step 2: Install the repository package

os="rhelx"
tag="10.x.x-cuda-x.x"
sudo rpm -Uvh nv-tensorrt-local-repo-${os}-${tag}-1.0-1.x86_64.rpm
sudo dnf clean expire-cache

Step 3: Install TensorRT packages

Choose the package type that matches your needs:

Table 6 Package Installation Options#

Package Type

Installation Command

Full C++ and Python Runtime

 
sudo dnf install tensorrt

Installs all TensorRT components including runtime, development headers, and Python bindings.

Lean Runtime Only

 
sudo dnf install libnvinfer-lean10
sudo dnf install libnvinfer-vc-plugin10

Lean Runtime Python Package

 
sudo dnf install python3-libnvinfer-lean

Dispatch Runtime Only

 
sudo dnf install libnvinfer-dispatch10
sudo dnf install libnvinfer-vc-plugin10

Dispatch Runtime Python Package

 
sudo dnf install python3-libnvinfer-dispatch

Windows Builder Resource Library (for cross-platform support)

 
sudo dnf install libnvinfer-win-builder-resource10

All TensorRT Python Packages

 
python3 -m pip install numpy
sudo dnf install python3-libnvinfer-devel

This installs: python3-libnvinfer, python3-libnvinfer-lean, python3-libnvinfer-dispatch

ONNX Graph Surgeon (for samples or projects)

 
python3 -m pip install numpy onnx onnx-graphsurgeon

TensorRT Python bindings are only installed for Python 3.12 due to package dependencies. If your default python3 is not version 3.12:

  • Use update-alternatives to switch to Python 3.12 by default

  • Invoke Python using python3.12

  • For non-default Python versions, install .whl files directly from the tar package

Note

When installing Python packages using the local repo method, you must manually install TensorRT’s Python dependencies with pip.

Installation Steps (Network Repo Method)#

This method is for advanced users already familiar with TensorRT who want quick setup or automation (such as when using containers). New users should use the local repo method above.

Note

If you are using a CUDA container, the NVIDIA CUDA network repository is already set up. Skip Step 1.

Step 1: Set up CUDA network repository

Follow the CUDA Toolkit download page instructions:

  1. Select Linux operating system

  2. Select desired architecture

  3. Select RHEL or Rocky distribution

  4. Select desired RHEL or Rocky version

  5. Select rpm (network) installer type

  6. Enter the provided commands into your terminal

Tip

You can omit the final dnf install command if you do not require the entire CUDA Toolkit. When installing TensorRT, dnf downloads required CUDA dependencies automatically.

Step 2: Install TensorRT packages

Choose the package type for your needs:

Table 7 Network Repo Package Options#

Package Type

Installation Command

Lean Runtime Only

 
sudo dnf install libnvinfer-lean10

Lean Runtime Python Package

 
sudo dnf install python3-libnvinfer-lean

Dispatch Runtime Only

 
sudo dnf install libnvinfer-dispatch10

Dispatch Runtime Python Package

 
sudo dnf install python3-libnvinfer-dispatch

Windows Builder Resource Library

 
sudo dnf install libnvinfer-win-builder-resource10

C++ Applications Runtime Only

 
sudo dnf install tensorrt-libs

C++ Applications with Development

 
sudo dnf install tensorrt-devel

C++ with Lean Runtime Development

 
sudo dnf install libnvinfer-lean-devel

C++ with Dispatch Runtime Development

 
sudo dnf install libnvinfer-dispatch-devel

Standard Runtime Python Package

 
python3 -m pip install numpy
sudo dnf install python3-libnvinfer

Additional Python Modules (onnx-graphsurgeon)

Use pip to install additional modules. Refer to onnx-graphsurgeon on PyPI.

Step 3 (Optional): Install specific CUDA version

By default, RHEL installs TensorRT for the latest CUDA version when using the CUDA network repository. To install TensorRT for a specific CUDA version and prevent automatic updates:

version="10.x.x.x-1.cudax.x"
sudo dnf install \
libnvinfer-bin-${version} \
libnvinfer-devel-${version} \
libnvinfer-dispatch-devel-${version} \
libnvinfer-dispatch10-${version} \
libnvinfer-headers-devel-${version} \
libnvinfer-headers-plugin-devel-${version} \
libnvinfer-headers-python-plugin-devel-${version} \
libnvinfer-lean-devel-${version} \
libnvinfer-lean10-${version} \
libnvinfer-plugin-devel-${version} \
libnvinfer-plugin10-${version} \
libnvinfer-safe-headers-devel-${version} \
libnvinfer-vc-plugin-devel-${version} \
libnvinfer-vc-plugin10-${version} \
libnvinfer-win-builder-resource10-${version} \
libnvinfer10-${version} \
libnvonnxparsers-devel-${version} \
libnvonnxparsers10-${version} \
python3-libnvinfer-${version} \
python3-libnvinfer-devel-${version} \
python3-libnvinfer-dispatch-${version} \
python3-libnvinfer-lean-${version} \
tensorrt-${version} \
tensorrt-devel-${version} \
tensorrt-libs-${version}

sudo dnf install dnf-plugin-versionlock
sudo dnf versionlock \
libnvinfer-bin \
libnvinfer-devel \
libnvinfer-dispatch-devel \
libnvinfer-dispatch10 \
libnvinfer-headers-devel \
libnvinfer-headers-plugin-devel \
libnvinfer-headers-python-plugin-devel \
libnvinfer-lean-devel \
libnvinfer-lean10 \
libnvinfer-plugin-devel \
libnvinfer-plugin10 \
libnvinfer-safe-headers-devel \
libnvinfer-vc-plugin-devel \
libnvinfer-vc-plugin10 \
libnvinfer-win-builder-resource10 \
libnvinfer10 \
libnvonnxparsers-devel \
libnvonnxparsers10 \
python3-libnvinfer \
python3-libnvinfer-devel \
python3-libnvinfer-dispatch \
python3-libnvinfer-lean \
tensorrt \
tensorrt-devel \
tensorrt-libs

To upgrade to latest version, unlock the packages:

sudo dnf versionlock delete \
libnvinfer-bin \
libnvinfer-devel \
libnvinfer-dispatch-devel \
libnvinfer-dispatch10 \
libnvinfer-headers-devel \
libnvinfer-headers-plugin-devel \
libnvinfer-headers-python-plugin-devel \
libnvinfer-lean-devel \
libnvinfer-lean10 \
libnvinfer-plugin-devel \
libnvinfer-plugin10 \
libnvinfer-safe-headers-devel \
libnvinfer-vc-plugin-devel \
libnvinfer-vc-plugin10 \
libnvinfer-win-builder-resource10 \
libnvinfer10 \
libnvonnxparsers-devel \
libnvonnxparsers10 \
python3-libnvinfer \
python3-libnvinfer-devel \
python3-libnvinfer-dispatch \
python3-libnvinfer-lean \
tensorrt \
tensorrt-devel \
tensorrt-libs

Verification#

These verification steps apply to both Local Repo and Network Repo installation methods.

Package Verification:

Verify that TensorRT packages are installed:

Table 8 Package Verification Commands#

Package Type

Command

Expected Output

Full TensorRT Release

 
rpm -q tensorrt

tensorrt-10.16.1.x-1.cuda-13.2.x86_64

Lean/Dispatch Runtime

 
rpm -qa | grep nvinfer

Lists all installed libnvinfer* files

C++ Verification:

Compile and run a sample, such as sampleOnnxMNIST. Samples and sample data are only available from GitHub. The instructions to prepare the sample data can be found within the samples README.md. To build all the samples, use the following commands:

$ cd <cloned_tensorrt_dir>
$ mkdir build && cd build
$ cmake .. \
   -DTRT_LIB_DIR=$TRT_LIBPATH \
   -DTRT_OUT_DIR=`pwd`/out \
   -DBUILD_SAMPLES=ON \
   -DBUILD_PARSERS=OFF \
   -DBUILD_PLUGINS=OFF
$ cmake --build . --parallel 4
$ ./out/sample_onnx_mnist

For information about the samples, refer to TensorRT Sample Support Guide.

Python Verification:

import tensorrt as trt
print(trt.__version__)
assert trt.Builder(trt.Logger())

Troubleshooting#

These troubleshooting steps apply to both Local Repo and Network Repo installation methods.

Issue: No package tensorrt available

  • Solution: Ensure you completed the repository setup steps correctly. Check that the repository is enabled:

    dnf repolist

Issue: Dependency conflicts with existing CUDA installation

  • Solution: Ensure CUDA Toolkit was installed using RPM packages (not tar file or runfile).

Issue: Samples fail to compile

  • Solution: Install development tools:

    sudo dnf groupinstall "Development Tools"