Installing on Linux via pip
#
Install TensorRT-LLM (tested on Ubuntu 24.04).
Install prerequisites
Before the pre-built Python wheel can be installed via
pip
, a few prerequisites must be put into place:Install CUDA Toolkit following the CUDA Installation Guide for Linux and make sure
CUDA_HOME
environment variable is properly set.# Optional step: Only required for NVIDIA Blackwell GPUs and SBSA platform pip3 install torch==2.7.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 sudo apt-get -y install libopenmpi-dev
PyTorch CUDA 12.8 package is required for supporting NVIDIA Blackwell GPUs and SBSA platform. On prior GPUs or Linux x86_64 platform, this extra installation is not required.
Tip
Instead of manually installing the preqrequisites as described above, it is also possible to use the pre-built TensorRT-LLM Develop container image hosted on NGC (see here for information on container tags).
Install pre-built TensorRT-LLM wheel
Once all prerequisites are in place, TensorRT-LLM can be installed as follows:
pip3 install --upgrade pip setuptools && pip3 install tensorrt_llm
This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.
Sanity check the installation by running the following in Python (tested on Python 3.12):
1from tensorrt_llm import LLM, SamplingParams 2 3 4def main(): 5 6 # Model could accept HF model name, a path to local HF model, 7 # or TensorRT Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF. 8 llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0") 9 10 # Sample prompts. 11 prompts = [ 12 "Hello, my name is", 13 "The capital of France is", 14 "The future of AI is", 15 ] 16 17 # Create a sampling params. 18 sampling_params = SamplingParams(temperature=0.8, top_p=0.95) 19 20 for output in llm.generate(prompts, sampling_params): 21 print( 22 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}" 23 ) 24 25 # Got output like 26 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming' 27 # Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the' 28 # Prompt: 'The capital of France is', Generated text: 'Paris.' 29 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are' 30 31 32if __name__ == '__main__': 33 main()
Known limitations
There are some known limitations when you pip install pre-built TensorRT-LLM wheel package.
MPI in the Slurm environment
If you encounter an error while running TensorRT-LLM in a Slurm-managed cluster, you need to reconfigure the MPI installation to work with Slurm. The setup methods depends on your slurm configuration, pls check with your admin. This is not a TensorRT-LLM specific, rather a general mpi+slurm issue.
The application appears to have been direct launched using "srun", but OMPI was not built with SLURM support. This usually happens when OMPI was not configured --with-slurm and we weren't able to discover a SLURM installation in the usual places.