Prerequisites

Before installing TensorRT, ensure your system meets the following requirements. This page is organized by category to help you quickly find the information you need.

Quick Checklist:

✓ NVIDIA GPU (Turing architecture or later) ✓ CUDA Toolkit installed (see Which CUDA should I install? below) ✓ Appropriate GPU drivers (r535+ on Linux, r537+ on Windows) ✓ Python 3.10-3.14 recommended (3.8-3.9 bindings available but samples not supported)

Important

Which CUDA should I install for TensorRT 11.2.1?

• Debian / RPM / tar / zip packages: Install CUDA Toolkit 13.3 update 1 and download the TensorRT package whose filename contains cuda-13.3. This matches the path used by Build Your First Engine.

• Pip wheels: Use a CUDA 12.x or 13.x driver/toolkit that matches the wheel suffix (tensorrt / tensorrt-cu13 for CUDA 13, or tensorrt-cu12 for CUDA 12). Pip does not include trtexec.

• Containers: The NGC image pins the CUDA toolkit for you.

The checklist line “CUDA 12.x or 13.x” below means TensorRT can run against those major lines depending on install method — it does not mean every 11.2.1 package variant is built for every CUDA minor.

Before You Begin

Review Release Information

Before installation, familiarize yourself with the NVIDIA TensorRT Release Notes to understand:

• New features in this release

• Known issues and limitations

• Platform-specific considerations

• Compatibility changes

Choose Your API

TensorRT provides both C++ and Python APIs:

• C++ API - Full functionality, no Python dependency

• Python API - Convenient for rapid prototyping and integration

• Both - Most users install both (default)

The installation instructions assume you want both APIs. For C++ only installation, skip Python-specific packages.

Required Software

NVIDIA CUDA Toolkit

TensorRT requires the NVIDIA CUDA Toolkit. If not already installed, refer to the NVIDIA CUDA Installation Guide.

Note

TensorRT 11.2.1 Debian, RPM, tar, and zip packages are published for CUDA 13.3 (unchanged from 11.1.0). When downloading from the TensorRT download page, select the cuda-13.3 variant to match the package filename. Prefer CUDA Toolkit 13.3 update 1 for those packages; use CUDA 12.x only when you intentionally install matching pip wheels (tensorrt-cu12) or an older toolkit path documented in the support matrix.

Supported CUDA Versions:

CUDA 13.x

• 13.3 update 1

• 13.2 update 1

• 13.1 update 1

• 13.0 update 2

CUDA 12.x

• 12.9 update 1

• 12.8 update 1

• 12.6 update 3

• 12.5 update 1

• 12.4 update 1

• 12.3 update 2

• 12.2 update 2

• 12.1 update 1

• 12.0 update 1

Driver Requirements:

• Linux: NVIDIA driver r535 or later

• Windows: NVIDIA driver r537 or later

• CUDA 13.x: NVIDIA driver r580 or later (both platforms)

For more information, refer to the TensorRT Support Matrix.

Optional Dependencies

The following libraries are optional and only needed for specific use cases:

cuBLAS (Optional)

cuBLAS is optional and only used for a few specific layers.

• When needed: If your model requires cuBLAS-accelerated layers

• Installation: Refer to the NVIDIA cuBLAS website

CUDA-Python (Optional)

CUDA-Python enables direct CUDA kernel calls from Python.

• When needed: If you use TensorRT Python API with custom CUDA operations

• Installation: Refer to the NVIDIA CUDA-Python documentation

NCCL (Optional)

Required only for the multi-device inference feature.

• When needed: When using IDistCollectiveLayer (SM 80+ / Ampere and later) or multi-device attention through IAttention::setNbRanks (SM 100+ / Blackwell and later)

• Installation: Refer to the NVIDIA NCCL Installation Guide. The Deep Learning Framework Containers include a compatible NCCL build.

• B300 platforms: When using multi-device inference on NVIDIA B300, use NCCL 2.30.x or later to avoid long cold-initialization latency on the first ncclCommInitRank call. Refer to the TensorRT 11.0.0 release notes (Known Issues) and TensorRT 11.1.0 release notes (Fixed Issues) for details.

For more information, refer to the TensorRT Support Matrix.

Framework and Model Support

PyTorch Integration

If you plan to use TensorRT with PyTorch:

• Tested with: PyTorch >= 2.0

• Compatibility: May work with older versions

• Use case: Examples and integration samples

ONNX Model Support

The ONNX-TensorRT parser supports:

• ONNX version: 1.20.0

• Opset support: Up to opset 25

• Backward compatibility: Official support is provided for opset 9 and above

For more information, refer to the TensorRT Support Matrix and the ONNX Opset Guide.

TensorRT Installation Modes

TensorRT offers Full, Lean, and Dispatch runtime packages with different capabilities and footprint sizes. The Installation Method Comparison table on the installing guide summarizes install paths; Understanding TensorRT Runtime Options there describes when to choose each runtime package.

For a development-to-production workflow, use Full Installation during development, serialize optimized engines to plan files, then deploy with Lean or Dispatch in production.

Next Steps

After verifying prerequisites:

1. Proceed to Installing TensorRT to choose your installation method

2. Follow the step-by-step installation instructions for your platform

3. After verification, follow Build Your First Engine to confirm your install with a 10-minute end-to-end build