Installation

vLLM is a Python library that also contains pre-compiled C++ and CUDA (12.1) binaries.

Requirements

• OS: Linux

• Python: 3.8 – 3.11

• GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)

Install with pip

You can install vLLM using pip:

$ # (Optional) Create a new conda environment.
$ conda create -n myenv python=3.9 -y
$ conda activate myenv

$ # Install vLLM with CUDA 12.1.
$ pip install vllm

Note

As of now, vLLM’s binaries are compiled on CUDA 12.1 by default. However, you can install vLLM with CUDA 11.8 by running:

$ # Install vLLM with CUDA 11.8.
$ export VLLM_VERSION=0.2.4
$ export PYTHON_VERSION=39
$ pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux1_x86_64.whl

$ # Re-install PyTorch with CUDA 11.8.
$ pip uninstall torch -y
$ pip install torch --upgrade --index-url https://download.pytorch.org/whl/cu118

$ # Re-install xFormers with CUDA 11.8.
$ pip uninstall xformers -y
$ pip install --upgrade xformers --index-url https://download.pytorch.org/whl/cu118

Build from source

You can also build and install vLLM from source:

$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ pip install -e .  # This may take 5-10 minutes.

Tip

If you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.

$ # Use `--ipc=host` to make sure the shared memory is large enough.
$ docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:23.10-py3

Note

If you are developing the C++ backend of vLLM, consider building vLLM with

$ python setup.py develop

since it will give you incremental builds. The downside is that this method is deprecated by setuptools.