65 lines
1.7 KiB
ReStructuredText
65 lines
1.7 KiB
ReStructuredText
.. _installation:
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Installation
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============
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vLLM is a Python library that also contains pre-compiled C++ and CUDA (12.1) binaries.
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Requirements
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------------
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* OS: Linux
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* Python: 3.8 -- 3.11
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* GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
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Install with pip
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----------------
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You can install vLLM using pip:
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.. code-block:: console
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$ # (Optional) Create a new conda environment.
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$ conda create -n myenv python=3.8 -y
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$ conda activate myenv
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$ # Install vLLM with CUDA 12.1.
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$ pip install vllm
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.. note::
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As of now, vLLM's binaries are compiled on CUDA 12.1 by default.
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However, you can install vLLM with CUDA 11.8 by running:
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.. code-block:: console
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$ # Install vLLM with CUDA 11.8.
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$ # Replace `cp310` with your Python version (e.g., `cp38`, `cp39`, `cp311`).
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$ pip install https://github.com/vllm-project/vllm/releases/download/v0.2.2/vllm-0.2.2+cu118-cp310-cp310-manylinux1_x86_64.whl
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$ # Re-install PyTorch with CUDA 11.8.
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$ pip uninstall torch -y
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$ pip install torch --upgrade --index-url https://download.pytorch.org/whl/cu118
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.. _build_from_source:
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Build from source
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-----------------
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You can also build and install vLLM from source:
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.. code-block:: console
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$ git clone https://github.com/vllm-project/vllm.git
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$ cd vllm
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$ pip install -e . # This may take 5-10 minutes.
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.. tip::
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If you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.
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.. code-block:: console
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$ # Use `--ipc=host` to make sure the shared memory is large enough.
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$ docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:23.10-py3
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