# Nvidia GPU - FastDeploy: Large Language Model Deployment

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# NVIDIA CUDA GPU Installation

The following installation methods are available when your environment meets these requirements:

- GPU Driver >= 535
- CUDA >= 12.3
- CUDNN >= 9.5
- Python >= 3.10
- Linux X86_64

## 1. Pre-built Docker Installation (Recommended)

Notice: The pre-built image only supports SM80/90 GPU(e.g. H800/A800)，if you are deploying on SM86/89GPU(L40/4090/L20), please reinstall `fastdeploy-gpu` after you create the container.

    docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.2.0

## 2. Pre-built Pip Installation

First install paddlepaddle-gpu. For detailed instructions, refer to PaddlePaddle Installation

    python -m pip install paddlepaddle-gpu==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

Then install fastdeploy. Do not install from PyPI. Use the following methods instead:

For SM80/90 architecture GPUs(e.g A30/A100/H100/):

    # Install stable release
    python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-gpu-80_90/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

    # Install latest Nightly build
    python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/fastdeploy-gpu-80_90/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

For SM86/89 architecture GPUs(e.g A10/4090/L20/L40):

    # Install stable release
    python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-gpu-86_89/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

    # Install latest Nightly build
    python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/fastdeploy-gpu-86_89/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

## 3. Build from Source Using Docker

- Note: `dockerfiles/Dockerfile.gpu` by default supports SM 80/90 architectures. To support other architectures, modify `bash build.sh 1 python false [80,90]` in the Dockerfile. It's recommended to specify no more than 2 architectures.

    git clone https://github.com/PaddlePaddle/FastDeploy
    cd FastDeploy

    docker build -f dockerfiles/Dockerfile.gpu -t fastdeploy:gpu .

## 4. Build Wheel from Source

First install paddlepaddle-gpu. For detailed instructions, refer to PaddlePaddle Installation

    python -m pip install paddlepaddle-gpu==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

Then clone the source code and build:

    git clone https://github.com/PaddlePaddle/FastDeploy
    cd FastDeploy

    # Argument 1: Whether to build wheel package (1 for yes, 0 for compile only)
    # Argument 2: Python interpreter path
    # Argument 3: Whether to compile CPU inference operators
    # Argument 4: Target GPU architectures
    bash build.sh 1 python false [80,90]

The built packages will be in the `FastDeploy/dist` directory.

## Environment Verification

After installation, verify the environment with this Python code:

    import paddle
    from paddle.jit.marker import unified
    # Verify GPU availability
    paddle.utils.run_check()
    # Verify FastDeploy custom operators compilation
    from fastdeploy.model_executor.ops.gpu import beam_search_softmax

If the above code executes successfully, the environment is ready.