TensorRT-LLMs/docs/source/installation/linux.md

(linux)=

Installing on Linux via pip

1. 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.

   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](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/devel)
   (see [here](containers) 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.

2. Sanity check the installation by running the following in Python (tested on Python 3.12):

       :language: python
       :linenos:
   

Known limitations

There are some known limitations when you pip install pre-built TensorRT-LLM wheel package.

1. 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.