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Method 2: Debian Package Installation#

Recommended for: System-wide installation, C++ and Python development, Ubuntu/Debian users

Advantages:

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

Limitations:

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

Platform Support#

Supported Operating Systems:

  • Ubuntu 22.04 (x86-64)

  • Ubuntu 24.04 (x86-64, ARM SBSA)

  • Debian 12 (x86-64, ARM SBSA)

Prerequisites:

  • CUDA Toolkit installed using Debian 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)#

Step 1: Download the TensorRT Debian repository package

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

Example filename: nv-tensorrt-local-repo-ubuntu2404-10.16.1-cuda-13.2_1.0-1_amd64.deb

Replace 10.x.x with your TensorRT version and cuda-x.x with your CUDA version. Replace amd64 with arm64 for ARM SBSA.

Step 2: Install the repository package

sudo dpkg -i nv-tensorrt-local-repo-ubuntu2404-10.x.x-cuda-x.x_1.0-1_amd64.deb

Step 3: Copy the keyring

sudo cp /var/nv-tensorrt-local-repo-ubuntu2404-10.x.x-cuda-x.x/*-keyring.gpg /usr/share/keyrings/

Step 4: Update the package index

sudo apt-get update

Step 5: Install TensorRT packages

Choose the package type that matches your needs:

Table 3 Package Installation Options#

Package Type

Installation Command

Full C++ and Python Runtime

 
sudo apt-get install tensorrt

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

Lean Runtime Only

 
sudo apt-get install libnvinfer-lean10
sudo apt-get install libnvinfer-vc-plugin10

Lean Runtime Python Package

 
sudo apt-get install python3-libnvinfer-lean

Dispatch Runtime Only

 
sudo apt-get install libnvinfer-dispatch10
sudo apt-get install libnvinfer-vc-plugin10

Dispatch Runtime Python Package

 
sudo apt-get install python3-libnvinfer-dispatch

Windows Builder Resource Library (for cross-platform support)

 
sudo apt-get install libnvinfer-win-builder-resource10

All TensorRT Python Packages

 
python3 -m pip install numpy
sudo apt-get install python3-libnvinfer-dev

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

Note

If you need Python for a non-default Python version, install the .whl files from the tar package instead.

ONNX Graph Surgeon (for samples or projects)

 
python3 -m pip install numpy onnx onnx-graphsurgeon

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 Ubuntu or Debian distribution

  4. Select desired Ubuntu or Debian version

  5. Select deb (network) installer type

  6. Enter the provided commands into your terminal

Tip

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

Step 2: Install TensorRT packages

Choose the package type for your needs:

Table 4 Network Repo Package Options#

Package Type

Installation Command

Lean Runtime Only

 
sudo apt-get install libnvinfer-lean10

Lean Runtime Python Package

 
sudo apt-get install python3-libnvinfer-lean

Dispatch Runtime Only

 
sudo apt-get install libnvinfer-dispatch10

Dispatch Runtime Python Package

 
sudo apt-get install python3-libnvinfer-dispatch

Windows Builder Resource Library

 
sudo apt-get install libnvinfer-win-builder-resource10

C++ Applications Runtime Only

 
sudo apt-get install tensorrt-libs

C++ Applications with Development

 
sudo apt-get install tensorrt-dev

C++ with Lean Runtime Development

 
sudo apt-get install libnvinfer-lean-dev

C++ with Dispatch Runtime Development

 
sudo apt-get install libnvinfer-dispatch-dev

Standard Runtime Python Package

 
python3 -m pip install numpy
sudo apt-get 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, Ubuntu installs TensorRT for the latest CUDA version when using the CUDA network repository. To install for a specific CUDA version and prevent automatic updates:

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

sudo apt-mark hold \
libnvinfer-bin \
libnvinfer-dev \
libnvinfer-dispatch-dev \
libnvinfer-dispatch10 \
libnvinfer-headers-dev \
libnvinfer-headers-plugin-dev \
libnvinfer-headers-python-plugin-dev \
libnvinfer-lean-dev \
libnvinfer-lean10 \
libnvinfer-plugin-dev \
libnvinfer-plugin10 \
libnvinfer-safe-headers-dev \
libnvinfer-vc-plugin-dev \
libnvinfer-vc-plugin10 \
libnvinfer-win-builder-resource10 \
libnvinfer10 \
libnvonnxparsers-dev \
libnvonnxparsers10 \
python3-libnvinfer-dev \
python3-libnvinfer-dispatch \
python3-libnvinfer-lean \
python3-libnvinfer \
tensorrt-dev \
tensorrt-libs \
tensorrt

To upgrade to latest version, unhold the packages:

sudo apt-mark unhold \
libnvinfer-bin \
libnvinfer-dev \
libnvinfer-dispatch-dev \
libnvinfer-dispatch10 \
libnvinfer-headers-dev \
libnvinfer-headers-plugin-dev \
libnvinfer-headers-python-plugin-dev \
libnvinfer-lean-dev \
libnvinfer-lean10 \
libnvinfer-plugin-dev \
libnvinfer-plugin10 \
libnvinfer-safe-headers-dev \
libnvinfer-vc-plugin-dev \
libnvinfer-vc-plugin10 \
libnvinfer-win-builder-resource10 \
libnvinfer10 \
libnvonnxparsers-dev \
libnvonnxparsers10 \
python3-libnvinfer-dev \
python3-libnvinfer-dispatch \
python3-libnvinfer-lean \
python3-libnvinfer \
tensorrt-dev \
tensorrt-libs \
tensorrt

Verification#

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

Package Verification:

Verify that TensorRT packages are installed:

Table 5 Package Verification Commands#

Package Type

Command

Expected Output

Full TensorRT Release

 
dpkg-query -W tensorrt

tensorrt  10.16.1.x-1+cuda13.2

Lean/Dispatch Runtime

 
dpkg-query -W "*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 detailed 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: E: Unable to locate package tensorrt

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

    apt-cache policy tensorrt

Issue: Dependency conflicts with existing CUDA installation

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

Issue: Samples fail to compile

  • Solution: Install build essentials:

    sudo apt-get install build-essential cmake