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docs/get_started/installation/nvidia_gpu.md

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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)
+```shell
+docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy:${fastdeploy_latest_version}
+```
+Where ```${fastdeploy_latest_version}``` is the FastDeploy release version number. [Check latest release here](https://github.com/PaddlePaddle/FastDeploy/releases). For example:
+```shell
+docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy:2.0.0
+```
+
+## 2. Pre-built Pip Installation
+
+First install paddlepaddle-gpu. For detailed instructions, refer to [PaddlePaddle Installation](https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html)
+```shell
+python -m pip install paddlepaddle-gpu==3.1.0 -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 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 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.
+
+```shell
+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](https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html)
+```shell
+python -m pip install paddlepaddle-gpu==3.1.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
+```
+
+Then clone the source code and build:
+```shell
+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:
+```python
+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.