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Alternative Installation Methods#

Aside from installing TensorRT from the standard product packages described above, TensorRT is also integrated into other NVIDIA platforms and tools. These alternative methods provide convenient ways to access TensorRT without manual installation.

NVIDIA NIM#

For developing AI-powered enterprise applications and deploying AI models in production. NVIDIA NIM offers optimized inference microservices for deploying AI models at scale.

For more information, refer to the NVIDIA NIM technical blog post.

NVIDIA JetPack#

For NVIDIA Jetson platforms, JetPack bundles all Jetson platform software, including TensorRT. Use it to flash your Jetson Developer Kit with the latest OS image, install NVIDIA SDKs, and jumpstart your development environment.

Key Features:

  • Complete software stack for Jetson platforms

  • TensorRT pre-installed and optimized

  • Integrated development environment

  • Automatic dependency management

Resources:

NVIDIA DriveOS#

For automotive applications, TensorRT is included in NVIDIA DriveOS Linux Standard. The safety proxy runtime is not installed by default in the NVIDIA DriveOS Linux SDK.

Installation:

For step-by-step instructions on installing TensorRT with NVIDIA SDK Manager, refer to the NVIDIA DRIVE Platform Installation section in the DriveOS Installation Guide.

Nsight Deep Learning Designer#

For developers who primarily convert ONNX models into TensorRT engines, Nsight Deep Learning Designer provides a GUI-based tool that can be used without a separate installation of TensorRT.

Key Features:

  • No installation required: Automatically downloads necessary TensorRT components (including CUDA, cuDNN, and cuBLAS) on-demand

  • GUI interface: Visual model editing and optimization workflow

  • All-in-one solution: Complete environment for model conversion

  • Primary use case: ONNX model conversion and optimization

When to Use:

  • You prefer a GUI-based workflow over command-line tools

  • You primarily work with ONNX models

  • You want to avoid manual installation and dependency management

  • You need a quick way to convert and optimize models

Download: Nsight Deep Learning Designer

Cross-Compilation for AArch64#

If you intend to cross-compile TensorRT applications for AArch64 targets (for example, NVIDIA Jetson platforms) from an x86 host, follow these steps to prepare your machine for cross-compilation and build TensorRT samples.

Step 1: Set up the network repository and TensorRT for the host

Start with the Network Repo Installation Method section to set up the network repository and install TensorRT on your x86 host.

Step 2: Install cross-compilation tools

sudo apt-get install g++-aarch64-linux-gnu

Step 3: Download AArch64 TensorRT package

From the TensorRT download page, download the tar file for AArch64.

Example: TensorRT-10.16.1.x.Linux.aarch64-gnu.cuda-13.2.tar.gz

Step 4: Extract and configure

tar -xzvf TensorRT-10.x.x.x.Linux.aarch64-gnu.cuda-x.x.tar.gz -C $HOME/cross-compile

Step 5: Get the TensorRT Samples from GitHub

$ git clone git@github.com:NVIDIA/TensorRT.git $HOME/TensorRT

Step 6: Build samples with cross-compilation

$ cd $HOME/TensorRT
$ mkdir build && cd build
$ cmake .. \
   -DTRT_LIB_DIR=$HOME/cross-compile/TensorRT-10.x.x/lib \
   -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/cmake_aarch64_cross.toolchain \
   -DTRT_OUT_DIR=`pwd`/out \
   -DBUILD_SAMPLES=ON \
   -DBUILD_PARSERS=OFF \
   -DBUILD_PLUGINS=OFF
$ cmake --build . --parallel 4

For detailed cross-compilation instructions and additional information, refer to the TensorRT samples README and the Cross Compiling Samples documentation.

Next Steps#

After successfully installing TensorRT, you can: