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.

Tip

TensorRT LLM 1.1 supports both CUDA 12.9 and 13.0. The wheel package release only supports CUDA 12.9, while CUDA 13.0 is only supported through NGC container release.
```
# 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

# Optional step: Only required for disagg-serving
sudo apt-get -y install libzmq3-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):

from tensorrt_llm import LLM, SamplingParams


def main():

    # Model could accept HF model name, a path to local HF model,
    # or TensorRT Model Optimizer's quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.
    llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")

    # Sample prompts.
    prompts = [
        "Hello, my name is",
        "The capital of France is",
        "The future of AI is",
    ]

    # Create a sampling params.
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    for output in llm.generate(prompts, sampling_params):
        print(
            f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
        )

    # Got output like
    # 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'
    # 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'
    # Prompt: 'The capital of France is', Generated text: 'Paris.'
    # 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'


if __name__ == '__main__':
    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.
```

Installing on Linux via pip#

Installing on Linux via `pip`#