vllm/docs/source/getting_started/installation/gpu-cuda.md

(installation-cuda)=

Installation for CUDA

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

Requirements

• OS: Linux
• Python: 3.9 -- 3.12
• GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)

Install released versions

Create a new Python environment

You can create a new Python environment using conda:

$ # (Recommended) Create a new conda environment.
$ conda create -n myenv python=3.12 -y
$ conda activate myenv

[PyTorch has deprecated the conda release channel](https://github.com/pytorch/pytorch/issues/138506). If you use `conda`, please only use it to create Python environment rather than installing packages. In particular, the PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.

Or you can create a new Python environment using uv, a very fast Python environment manager. Please follow the documentation to install uv. After installing uv, you can create a new Python environment using the following command:

$ # (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools` in the environment.
$ uv venv myenv --python 3.12 --seed
$ source myenv/bin/activate

In order to be performant, vLLM has to compile many cuda kernels. The compilation unfortunately introduces binary incompatibility with other CUDA versions and PyTorch versions, even for the same PyTorch version with different building configurations.

Therefore, it is recommended to install vLLM with a fresh new environment. If either you have a different CUDA version or you want to use an existing PyTorch installation, you need to build vLLM from source. See below for more details.

Install vLLM

You can install vLLM using either pip or uv pip:

$ # Install vLLM with CUDA 12.1.
$ pip install vllm # If you are using pip.
$ uv pip install vllm # If you are using uv.

As of now, vLLM's binaries are compiled with CUDA 12.1 and public PyTorch release versions by default. We also provide vLLM binaries compiled with CUDA 11.8 and public PyTorch release versions:

$ # Install vLLM with CUDA 11.8.
$ export VLLM_VERSION=0.6.1.post1
$ export PYTHON_VERSION=310
$ 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 --extra-index-url https://download.pytorch.org/whl/cu118

(install-the-latest-code)=

Install the latest code

LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on a x86 platform with CUDA 12 for every commit since v0.5.3.

Install the latest code using pip

$ pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

--pre is required for pip to consider pre-released versions.

If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), due to the limitation of pip, you have to specify the full URL of the wheel file by embedding the commit hash in the URL:

$ export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
$ pip install https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl

Note that the wheels are built with Python 3.8 ABI (see PEP 425 for more details about ABI), so they are compatible with Python 3.8 and later. The version string in the wheel file name (1.0.0.dev) is just a placeholder to have a unified URL for the wheels, the actual versions of wheels are contained in the wheel metadata (the wheels listed in the extra index url have correct versions). Although we don't support Python 3.8 any more (because PyTorch 2.5 dropped support for Python 3.8), the wheels are still built with Python 3.8 ABI to keep the same wheel name as before.

Install the latest code using uv

Another way to install the latest code is to use uv:

$ uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly

If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), you can specify the commit hash in the URL:

$ export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
$ uv pip install vllm --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}

The uv approach works for vLLM v0.6.6 and later and offers an easy-to-remember command. A unique feature of uv is that packages in --extra-index-url have higher priority than the default index. If the latest public release is v0.6.6.post1, uv's behavior allows installing a commit before v0.6.6.post1 by specifying the --extra-index-url. In contrast, pip combines packages from --extra-index-url and the default index, choosing only the latest version, which makes it difficult to install a development version prior to the released version.

Install the latest code using docker

Another way to access the latest code is to use the docker images:

$ export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
$ docker pull public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:${VLLM_COMMIT}

These docker images are used for CI and testing only, and they are not intended for production use. They will be expired after several days.

The latest code can contain bugs and may not be stable. Please use it with caution.

(build-from-source)=

Build from source

(python-only-build)=

Python-only build (without compilation)

If you only need to change Python code, you can build and install vLLM without compilation. Using pip's --editable flag, changes you make to the code will be reflected when you run vLLM:

$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ VLLM_USE_PRECOMPILED=1 pip install --editable .

This will download the latest nightly wheel from https://wheels.vllm.ai/nightly/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl and use the compiled libraries from there in the installation.

The VLLM_PRECOMPILED_WHEEL_LOCATION environment variable can be used instead of VLLM_USE_PRECOMPILED to specify a custom path or URL to the wheel file. For example, to use the 0.6.1.post1 PyPi wheel:

$ export VLLM_PRECOMPILED_WHEEL_LOCATION=https://files.pythonhosted.org/packages/4a/4c/ee65ba33467a4c0de350ce29fbae39b9d0e7fcd887cc756fa993654d1228/vllm-0.6.3.post1-cp38-abi3-manylinux1_x86_64.whl
$ pip install --editable .

You can find more information about vLLM's wheels above.

There is a possibility that your source code may have a different commit ID compared to the latest vLLM wheel, which could potentially lead to unknown errors.
It is recommended to use the same commit ID for the source code as the vLLM wheel you have installed. Please refer to [the section above](#install-the-latest-code) for instructions on how to install a specified wheel.

Full build (with compilation)

If you want to modify C++ or CUDA code, you'll need to build vLLM from source. This can take several minutes:

$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ pip install -e .

Building from source requires a lot of compilation. If you are building from source repeatedly, it's more efficient to cache the compilation results.

For example, you can install [ccache](https://github.com/ccache/ccache) using `conda install ccache` or `apt install ccache` .
As long as `which ccache` command can find the `ccache` binary, it will be used automatically by the build system. After the first build, subsequent builds will be much faster.

[sccache](https://github.com/mozilla/sccache) works similarly to `ccache`, but has the capability to utilize caching in remote storage environments.
The following environment variables can be set to configure the vLLM `sccache` remote: `SCCACHE_BUCKET=vllm-build-sccache SCCACHE_REGION=us-west-2 SCCACHE_S3_NO_CREDENTIALS=1`. We also recommend setting `SCCACHE_IDLE_TIMEOUT=0`.

Use an existing PyTorch installation

There are scenarios where the PyTorch dependency cannot be easily installed via pip, e.g.:

• Building vLLM with PyTorch nightly or a custom PyTorch build.
• Building vLLM with aarch64 and CUDA (GH200), where the PyTorch wheels are not available on PyPI. Currently, only the PyTorch nightly has wheels for aarch64 with CUDA. You can run pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124 to install PyTorch nightly, and then build vLLM on top of it.

To build vLLM using an existing PyTorch installation:

$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ python use_existing_torch.py
$ pip install -r requirements-build.txt
$ pip install -e . --no-build-isolation

Use the local cutlass for compilation

Currently, before starting the build process, vLLM fetches cutlass code from GitHub. However, there may be scenarios where you want to use a local version of cutlass instead. To achieve this, you can set the environment variable VLLM_CUTLASS_SRC_DIR to point to your local cutlass directory.

$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ VLLM_CUTLASS_SRC_DIR=/path/to/cutlass pip install -e .

Troubleshooting

To avoid your system being overloaded, you can limit the number of compilation jobs to be run simultaneously, via the environment variable MAX_JOBS. For example:

$ export MAX_JOBS=6
$ pip install -e .

This is especially useful when you are building on less powerful machines. For example, when you use WSL it only assigns 50% of the total memory by default, so using export MAX_JOBS=1 can avoid compiling multiple files simultaneously and running out of memory. A side effect is a much slower build process.

Additionally, 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

If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from the official website. After installation, set the environment variable CUDA_HOME to the installation path of CUDA Toolkit, and make sure that the nvcc compiler is in your PATH, e.g.:

$ export CUDA_HOME=/usr/local/cuda
$ export PATH="${CUDA_HOME}/bin:$PATH"

Here is a sanity check to verify that the CUDA Toolkit is correctly installed:

$ nvcc --version # verify that nvcc is in your PATH
$ ${CUDA_HOME}/bin/nvcc --version # verify that nvcc is in your CUDA_HOME

Unsupported OS build

vLLM can fully run only on Linux but for development purposes, you can still build it on other systems (for example, macOS), allowing for imports and a more convenient development environment. The binaries will not be compiled and won't work on non-Linux systems.

Simply disable the VLLM_TARGET_DEVICE environment variable before installing:

$ export VLLM_TARGET_DEVICE=empty
$ pip install -e .