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Installing TensorRT#

This guide provides step-by-step instructions for installing TensorRT using various methods. Choose the installation method that best fits your development environment and deployment needs.

Before You Begin: Ensure you have reviewed the Prerequisites to confirm your system meets all requirements.

What Is TensorRT, and What Does Installing It Give You?#

NVIDIA TensorRT is an SDK for high-performance deep learning inference (running a trained model on new inputs to produce predictions) on NVIDIA GPUs. It compiles a trained model into a hardware-specific binary called an engine (also referred to as a plan file), then runs that engine inside your application.

Installing TensorRT puts the following on your system:

  • The TensorRT libraries (libnvinfer, libnvonnxparser, and friends), which your C++ or Python application links against to load and execute engines.

  • Python bindings (the tensorrt Python package), so you can build, save, and run engines from Python without writing C++.

  • The trtexec command-line tool (Debian/RPM, tar/zip, and container installs only), which builds an engine from an ONNX file, benchmarks it, and is the fastest way to confirm a fresh non-pip install works. Pip wheels do not include trtexec.

  • C++ headers (NvInfer.h and friends), included with every method except pip, for applications that build against the C++ API.

After installation:

  • Non-pip methods: run trtexec --help and import tensorrt from Python, then follow Build Your First Engine.

  • Pip only: verify with import tensorrt (see Method 1: pip). For the ~10-minute CLI first-engine tutorial, install Debian/RPM, tar/zip, or container so trtexec is on your PATH.

If you only need to run engines built by someone else (for example, a deployment server), the Lean or Dispatch runtimes described below are smaller alternatives to the Full Runtime.

Installation Method Comparison#

Quick Comparison Table:

Method

Best For

Requires Root

C++ Headers

Multi-Version

Installation Time

pip (Python)

Python development

No

No

Yes (venv)

⚡ Fastest (~2 min)

Debian/RPM

System-wide install

Yes

Yes

No

🔵 Fast (~5 min)

Tar/Zip

Multiple versions

No

Yes

Yes

🟡 Moderate (~10 min)

Container (NGC)

Isolated environments

No (Docker)

Yes

Yes

⚡ Fastest (~5 min)

Choosing Your Installation Method#

Use the Installation Method Comparison table above to pick pip, Debian/RPM, Tar/Zip, or Container. Follow the matching page under Installation Methods for step-by-step instructions.

Understanding TensorRT Runtime Options#

TensorRT ships Full, Lean, and Dispatch runtime packages with different capabilities and footprint sizes. For package names, size ranges, and a development-to-production workflow, refer to TensorRT Installation Modes in the prerequisites.

Downloading TensorRT#

Before installing with Debian (local repo), RPM (local repo), Tar, or Zip methods, you must download TensorRT packages.

Tip

For pip installation: Skip this section. The pip method downloads packages automatically from PyPI.

  1. Go to https://developer.nvidia.com/tensorrt/download/11x.

  2. Accept the license agreement.

  3. Select TensorRT version 11.2.1 (or your target version).

  4. Download the package for your platform:

    • Linux x86-64: Debian local repo (.deb), RPM local repo (.rpm), or Tar (.tar.gz)

    • Linux ARM SBSA: Debian local repo (.deb) or Tar (.tar.gz)

      NVIDIA JetPack is not supported in TensorRT 11.2.1. Jetson deployments must remain on a TensorRT 10.x release supported by their JetPack version. Refer to Migrating TensorRT from 10.x to 11.x on Jetson/JetPack.

    • Windows x64: Zip (.zip)

Installation Methods#

Verifying Your Installation#

Verification depends on how you installed TensorRT.

Debian / RPM / tar / zip / container (includes trtexec):

trtexec --help

import tensorrt as trt
print(trt.__version__)

If both succeed, follow Build Your First Engine to build and run a test engine in about 10 minutes.

Pip (Python bindings and libraries only — no trtexec):

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

Do not expect trtexec --help to work after a pip-only install. For the CLI first-engine tutorial, install a non-pip method above, or stay on the Python API path documented in the Quick Start Guide and samples.

Each method page adds method-specific verification (package queries, LD_LIBRARY_PATH / PATH, or Lean/Dispatch imports). If verification fails, refer to Troubleshooting.