Installation#

TensorRT Edge-LLM has two separate components that need to be installed on different systems:

  1. Export and quantization (runs on an x86 host; only quantization requires a GPU)

  2. C++ Runtime (Jetson Thor, NVIDIA DRIVE / DriveOS, NVIDIA DGX Spark, or optional x86 developer build)

Part 1: Export and Quantization (x86 Host)#

The Python frontend exports Hugging Face checkpoints and optionally quantizes FP16/BF16 checkpoints before export. Export runs on CPU. Quantization requires an NVIDIA GPU.

System Requirements#

  • Platform: x86-64 Linux system

  • Recommended OS: Ubuntu 22.04, 24.04

  • GPU for quantization: NVIDIA GPU with Compute Capability 8.0+ (Ampere or newer)

  • CUDA for quantization: 12.x or 13.x

  • Python: 3.10+

Memory Requirements#

  • Export: at least 1.5 times the checkpoint size in CPU memory. No GPU is required.

  • Quantization: GPU memory at least equal to the FP16 checkpoint size.

Verify Your Prerequisites:

# Check CUDA installation when quantizing
nvcc --version
# Should show CUDA 12.x or 13.x

# Check the GPU and available memory when quantizing
nvidia-smi
# Look for GPU memory (e.g., "24576MiB" for 24GB)

# Check Python version
python3 --version
# Should show Python 3.10 or higher

If CUDA is not installed:

Download and install CUDA Toolkit from NVIDIA CUDA Downloads. Choose version 12.x or 13.x for your system.

After installation, verify with nvcc --version and nvidia-smi.

Installing#

For a containerized environment for clean installation, it is recommended to use the NVIDIA PyTorch Docker image:

# Pull the recommended Docker image
docker pull nvcr.io/nvidia/pytorch:25.12-py3

# Run the container with GPU support
docker run --gpus all -it --rm \
    -v $(pwd):/workspace \
    -w /workspace \
    nvcr.io/nvidia/pytorch:25.12-py3 \
    bash

1. Clone Repository

git clone https://github.com/NVIDIA/TensorRT-Edge-LLM.git
cd TensorRT-Edge-LLM
git submodule update --init --recursive

2. Install Python Dependencies

If you are not using container, it is recommended to use a virtual environment:

# Create virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate

Install the package and its dependencies. The base install registers the CLI entry points (tensorrt-edgellm-export, tensorrt-edgellm-quantize, etc.) and pulls the core checkpoint export dependencies. Optional tool dependencies stay out of the base environment so export-only and server images do not pull quantization, audio, and LoRA-merge packages unnecessarily.

# Install the package (registers CLI entry points and core export dependencies)
pip3 install -e .

# Required for quantization, LoRA merge, vocabulary reduction, audio preprocessing,
# and tokenizer helpers (re-installs with the tools extra)
pip3 install -e ".[tools]"

# Required only for the experimental high-level Python API and server
pip3 install -e ".[server]"

The base install includes:

  • PyTorch

  • Transformers

  • ONNX

  • ONNX Script and ONNX GraphSurgeon

The optional tools extra adds NVIDIA Model Optimizer, calibration datasets, audio preprocessing dependencies, LoRA merge dependencies, and tokenizer helpers. The optional server extra adds FastAPI, Uvicorn, and pybind11 for the experimental high-level Python API and OpenAI-compatible server.

Note: Accuracy evaluation dependencies live under examples/accuracy/requirements.txt.

3. Verify the Checkpoint Export Workflow

Use the virtual environment created in Step 2 for this checkout. Do not mix packages from older release branches into the same environment.

Export an unquantized or supported pre-quantized Hugging Face checkpoint with tensorrt-edgellm-export. Run tensorrt-edgellm-quantize first only when you need to create a quantized checkpoint from an FP16/BF16 source checkpoint.

# Included in the base package
tensorrt-edgellm-export --help

# Available after installing the tools extra
tensorrt-edgellm-quantize --help
tensorrt-edgellm-merge-lora --help
tensorrt-edgellm-reduce-vocab --help

4. Configure HuggingFace Access (Optional)

Some models on HuggingFace require you to accept terms before downloading.

Models that require HuggingFace login:

  • Llama family (Llama 3.x)

  • Phi-4-Multimodal

  • Alpamayo-R1-10B

  • Other models marked as “gated” on HuggingFace

To configure access:

# Install HuggingFace CLI and login
hf auth login
# Enter your HuggingFace access token when prompted

How to get a token: Visit HuggingFace Settings - Tokens, create a new token (read access is sufficient), and copy it.

You’re done with export pipeline setup! You can now quantize and export models with the checkpoint-based workflow. The ONNX files will be transferred to the Edge device for runtime deployment.

Part 2: C++ Runtime (Edge Device)#

The C++ runtime builds TensorRT engines and runs inference on the target. For the authoritative JetPack, DriveOS, CUDA, TensorRT, and TensorRT Edge-LLM compatibility table, see the Official Support Matrix. Then use the matching platform command below for your device or SDK image.

Jetson Orin does not support FP8, MXFP8, FP4, or NVFP4 runtime precision in this release. Use FP16, INT8, or INT4 checkpoints for Orin.

System Requirements#

  • CUDA and TensorRT from the target JetPack, DriveOS SDK, or DGX Spark software release

  • Disk space: ~20-50GB for ONNX files and TensorRT engines

Build Instructions#

1. Install System Dependencies (on Edge device)

sudo apt update
sudo apt install -y \
    cmake \
    build-essential \
    git

2. Verify CUDA and TensorRT Installation

After JetPack is installed, inside the DriveOS SDK Docker image, or on DGX Spark, TensorRT should be installed in /usr.

# Check CUDA version
nvcc --version  # Should match the CUDA_CTK_VERSION for your platform below

# Check TensorRT version
dpkg -l | grep tensorrt  # Should show TensorRT 10.x+

3. Clone Repository (on Edge device)

# Clone to your chosen source directory
cd /path/to/parent-directory
git clone https://github.com/NVIDIA/TensorRT-Edge-LLM.git
cd TensorRT-Edge-LLM
git submodule update --init --recursive

4. Configure Build

Use the CMake command for your platform. All commands enable CuTe DSL kernels because Qwen3.5 and several other model paths require them.

JetPack 7.0/7.1 Thor

mkdir -p build
cd build

cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DTRT_PACKAGE_DIR=/usr \
    -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64_linux_toolchain.cmake \
    -DEMBEDDED_TARGET=jetson-thor \
    -DCUDA_CTK_VERSION=13.0 \
    -DENABLE_CUTE_DSL=ALL

JetPack 7.2 Thor

mkdir -p build
cd build

cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DTRT_PACKAGE_DIR=/usr \
    -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64_linux_toolchain.cmake \
    -DEMBEDDED_TARGET=jetson-thor \
    -DCUDA_CTK_VERSION=13.2 \
    -DENABLE_CUTE_DSL=ALL

DriveOS 7.2 Thor

Run this inside the DriveOS SDK Docker image, then copy build/ to the DRIVE system.

mkdir -p build
cd build

cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DTRT_PACKAGE_DIR=/usr \
    -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64_linux_toolchain.cmake \
    -DEMBEDDED_TARGET=auto-thor \
    -DCUDA_CTK_VERSION=13.3 \
    -DENABLE_CUTE_DSL=ALL

DGX Spark (GB10)

Run this directly on the DGX Spark system. Use gb10 as the embedded target and CUDA Toolkit 13.0.

mkdir -p build
cd build

cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DTRT_PACKAGE_DIR=/usr \
    -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64_linux_toolchain.cmake \
    -DEMBEDDED_TARGET=gb10 \
    -DCUDA_CTK_VERSION=13.0 \
    -DENABLE_CUTE_DSL=ALL

JetPack 7.2 Orin

mkdir -p build
cd build

cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DTRT_PACKAGE_DIR=/usr \
    -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64_linux_toolchain.cmake \
    -DEMBEDDED_TARGET=jetson-orin \
    -DCUDA_CTK_VERSION=13.2 \
    -DENABLE_CUTE_DSL=ALL

JetPack 6.2+ Orin

mkdir -p build
cd build

cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DTRT_PACKAGE_DIR=/usr \
    -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64_linux_toolchain.cmake \
    -DEMBEDDED_TARGET=jetson-orin \
    -DCUDA_CTK_VERSION=12.6 \
    -DENABLE_CUTE_DSL=ALL

Alternative: Building on x86 GPU Systems (Optional for Developers)

If you want to build and test on an x86 workstation with NVIDIA GPU (for development purposes before deploying to Edge devices), you can use this configuration instead:

mkdir -p build
cd build

cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DTRT_PACKAGE_DIR=/usr/local/TensorRT-10.x.x \
    -DCUDA_CTK_VERSION=<YOUR_CUDA_VERSION> \
    -DCUTE_DSL_ARTIFACT_TAG=<YOUR_SM> \
    -DENABLE_CUTE_DSL=ALL

Note: Replace /usr/local/TensorRT-10.x.x with your actual TensorRT installation path. Use dpkg -l | grep tensorrt to find it, or download from NVIDIA TensorRT downloads. Replace <YOUR_CUDA_VERSION> with your actual CUDA version (e.g., 13.0). Use nvcc --version to check your CUDA version. Replace <YOUR_SM> with the generated CuTe DSL artifact tag, for example sm_80, sm_100, or sm_120.

CMake Options:

Option

Description

Default

TRT_PACKAGE_DIR

Path to TensorRT installation. Auto-detected; manual hint to disambiguate multiple versions.

N/A

CMAKE_TOOLCHAIN_FILE

Required for Edge devices: Use cmake/aarch64_linux_toolchain.cmake for Edge device builds. Not needed for GPU builds

N/A

EMBEDDED_TARGET

Required for Edge devices: jetson-thor (Jetson Thor), auto-thor (DRIVE Thor / DriveOS), gb10 (DGX Spark), or jetson-orin (Jetson Orin). Not needed for GPU builds

N/A

CUDA_CTK_VERSION

CUDA Toolkit version. Use the platform command above to select 13.3, 13.2, 13.0, or 12.6. Do not pass -DCUDA_VERSION; CMake reserves that name for CUDA headers and rejects it.

target default

BUILD_UNIT_TESTS

Build unit tests

OFF

ENABLE_COVERAGE

Enable gcov code coverage instrumentation (see Code Coverage)

OFF

ENABLE_CUTE_DSL

Select generated CuTe DSL kernels: fmha, ALL, or a group list such as gdn, gemm, or ssd. Any selection also links fmha, which the attention plugins require. Use ALL for customer builds.

fmha

CUTE_DSL_ARTIFACT_TAG

Artifact tag under cpp/kernels/cuteDSLArtifact/<arch>/, for example sm_87, sm_110, or sm_121. Edge targets infer it from EMBEDDED_TARGET; pass it explicitly for x86 prebuilt artifacts or when multiple local tags exist for one CPU architecture.

auto

CuTe DSL Kernel Artifacts

CuTe DSL binaries are generated with kernelSrcs/build_cutedsl.py before configuring CMake. A normal build defaults to the canonical fmha family and therefore requires a matching artifact. This family provides Context/ViT attention on supported GPUs and adds the optimized Blackwell implementation on SM100/SM101/SM110 when available.

The platform commands above pass -DENABLE_CUTE_DSL=ALL because Qwen3.5 and several other model paths require optional groups. Selecting a narrower group still includes the fmha baseline; for example, -DENABLE_CUTE_DSL=gdn enables both GDN and FMHA.

If you have multiple local artifact tags for the same CPU architecture, also pass -DCUTE_DSL_ARTIFACT_TAG=<tag>.

For B200 or other SM100 build hosts without a matching prebuilt artifact, install the CuTe DSL package expected by kernelSrcs/build_cutedsl.py, then generate the artifact before running CMake:

pip install 'nvidia-cutlass-dsl==4.6.1'
python kernelSrcs/build_cutedsl.py --gpu_arch sm_100

For cross-compilation, pass --arch aarch64 when the artifact must be consumed by an AArch64 target build.

For supported model families, precisions, and hardware notes, see Supported Models.

5. Build Project

make -j$(nproc)

Build time: ~1-2 minutes depending on hardware.

6. Verify Build

# Test C++ examples
./examples/llm/llm_build --help
./examples/llm/llm_inference --help

You’re done with C++ runtime setup! You can now build engines and run inference on the Edge device.

Next Steps#

After installation, proceed to the Quick Start Guide for a complete end-to-end workflow, or see the Examples for detailed pipeline stages and advanced use cases.

Troubleshooting#

Common Installation Issues#

Issue: Python module import errors

Solution: Activate the virtual environment and reinstall the package from the current checkout:

source venv/bin/activate
python -m pip install -e .
tensorrt-edgellm-export --help

Issue: nvcc: command not found

Solution: Ensure the target JetPack release, DriveOS SDK Docker image, or DGX Spark software stack is installed with CUDA support:

# Verify CUDA installation
nvcc --version
# Should match the CUDA_CTK_VERSION used for CMake

Issue: TensorRT not found during CMake

Solution: Specify TensorRT package directory. This directory should contain lib and include directories, and we are looking for the nvinfer library and header:

cmake .. \
    -DTRT_PACKAGE_DIR=/usr/local/TensorRT-10.x.x \
    -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64_linux_toolchain.cmake \
    -DEMBEDDED_TARGET=<jetson-thor|auto-thor|gb10|jetson-orin> \
    -DCUDA_CTK_VERSION=<target CUDA version> \
    -DENABLE_CUTE_DSL=ALL

Issue: Thread issue during C++ build

Solution: Reduce parallel jobs or even use sequential build:

make -j  # Instead of make -j$(nproc)

Getting Help#

  • Documentation: Check the docs/source/developer_guide directory

  • Issues: Report bugs on GitHub Issues

  • Discussions: Ask questions on GitHub Discussions

  • Community: Join the NVIDIA Developer Forums

Uninstalling#

Quantization and tensorrt_edgellm (x86 Host):

  • Deactivate and remove virtual environment: deactivate && rm -rf venv

  • Remove repository (optional): rm -rf TensorRT-Edge-LLM

C++ Runtime (Edge Device):

  • Remove build directory: rm -rf build

  • Remove repository (optional): rm -rf TensorRT-Edge-LLM