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@ -89,20 +89,16 @@ Currently, PaddleOCR-VL offers four inference methods, with varying levels of su
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</tbody>
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</table>
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TIP:
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1. When using NVIDIA GPU for inference, ensure that the Compute Capability (CC) and CUDA version meet the requirements:
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- PaddlePaddle: CC ≥ 7.0, CUDA ≥ 11.8
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- vLLM: CC ≥ 8.0, CUDA ≥ 12.6
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- SGLang: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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- FastDeploy: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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- Common GPUs with CC ≥ 8 include RTX 30/40/50 series and A10/A100, etc. For more models, refer to [CUDA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus)
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2. vLLM compatibility note: Although vLLM can be launched on NVIDIA GPUs with CC 7.x such as T4/V100, timeout or OOM issues may occur, and its use is not recommended.
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3. Currently, PaddleOCR-VL does not support ARM architecture CPUs. More hardware support will be expanded based on actual needs in the future, so stay tuned!
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4. vLLM, SGLang, and FastDeploy cannot run natively on Windows or macOS. Please use the Docker images we provide.
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> TIP:
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> - When using NVIDIA GPU for inference, ensure that the Compute Capability (CC) and CUDA version meet the requirements:
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> > - PaddlePaddle: CC ≥ 7.0, CUDA ≥ 11.8
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> > - vLLM: CC ≥ 8.0, CUDA ≥ 12.6
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> > - SGLang: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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> > - FastDeploy: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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> > - Common GPUs with CC ≥ 8 include RTX 30/40/50 series and A10/A100, etc. For more models, refer to [CUDA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus)
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> - vLLM compatibility note: Although vLLM can be launched on NVIDIA GPUs with CC 7.x such as T4/V100, timeout or OOM issues may occur, and its use is not recommended.
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> - Currently, PaddleOCR-VL does not support ARM architecture CPUs. More hardware support will be expanded based on actual needs in the future, so stay tuned!
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> - vLLM, SGLang, and FastDeploy cannot run natively on Windows or macOS. Please use the Docker images we provide.
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Since different hardware requires different dependencies, if your hardware meets the requirements in the table above, please refer to the following table for the corresponding tutorial to configure your environment:
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@ -136,7 +132,7 @@ Since different hardware requires different dependencies, if your hardware meets
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</tbody>
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</table>
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> [!TIP]
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> TIP:
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> For example, if you are using an RTX 50 series GPU that meets the device requirements for both PaddlePaddle and vLLM inference methods, please refer to the [PaddleOCR-VL NVIDIA Blackwell Architecture GPU Environment Configuration Tutorial](./PaddleOCR-VL-NVIDIA-Blackwell.en.md) to complete the environment configuration before using PaddleOCR-VL.
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## 1. Environment Preparation
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@ -202,14 +198,14 @@ python -m pip install https://paddle-whl.bj.bcebos.com/nightly/cu126/safetensors
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python -m pip install https://xly-devops.cdn.bcebos.com/safetensors-nightly/safetensors-0.6.2.dev0-cp38-abi3-win_amd64.whl
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```
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> [!IMPORTANT]
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> IMPORTANT:
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> **Please ensure that you install PaddlePaddle framework version 3.2.1 or above, along with the special version of safetensors.** For macOS users, please use Docker to set up the environment.
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## 2. Quick Start
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PaddleOCR-VL supports two usage methods: CLI command line and Python API. The CLI command line method is simpler and suitable for quickly verifying functionality, while the Python API method is more flexible and suitable for integration into existing projects.
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> [!TIP]
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> TIP:
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> The methods introduced in this section are primarily for rapid validation. Their inference speed, memory usage, and stability may not meet the requirements of a production environment. **If deployment to a production environment is needed, we strongly recommend using a dedicated inference acceleration framework**. For specific methods, please refer to the next section.
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### 2.1 Command Line Usage
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@ -2260,7 +2256,7 @@ foreach ($result as $i => $item) {
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### 4.4 Pipeline Configuration Adjustment Instructions
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> [!NOTE]
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> NOTE:
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> If you do not need to adjust pipeline configurations, you can ignore this section.
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Adjusting the PaddleOCR-VL configuration for service deployment involves only three steps:
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@ -89,16 +89,16 @@ PaddleOCR-VL 是一款先进、高效的文档解析模型，专为文档中的
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</tbody>
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</table>
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> [!TIP]
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> 1. 使用英伟达 GPU 推理时需要注意 Compute Capability（简称 CC） 和 CUDA 版本（简称 CUDA）是否满足要求：
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> - PaddlePaddle: CC ≥ 7.0, CUDA ≥ 11.8
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> - vLLM: CC ≥ 8.0, CUDA ≥ 12.6
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> - SGLang: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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> - FastDeploy: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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> - CC ≥ 8 的常见显卡包括 RTX 30/40/50 系列及 A10/A100 等，更多型号可查看 [CUDA GPU 计算能力](https://developer.nvidia.cn/cuda-gpus)
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> 2. 虽然 vLLM 可在 T4/V100 等 CC 7.x 的 NVIDIA GPU 上启动，但容易出现超时或 OOM，不推荐使用。
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> 3. 当前，PaddleOCR-VL 暂不支持 ARM 架构 CPU。后续将根据实际需求扩展更多硬件支持，敬请期待！
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> 4. vLLM、SGLang 和 FastDeploy 无法在 Windows 或 macOS 上原生运行，请使用我们提供的 Docker 镜像。
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> TIP:
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> - 使用英伟达 GPU 推理时需要注意 Compute Capability（简称 CC） 和 CUDA 版本（简称 CUDA）是否满足要求：
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> > - PaddlePaddle: CC ≥ 7.0, CUDA ≥ 11.8
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> > - vLLM: CC ≥ 8.0, CUDA ≥ 12.6
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> > - SGLang: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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> > - FastDeploy: 8.0 ≤ CC < 12.0, CUDA ≥ 12.6
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> > - CC ≥ 8 的常见显卡包括 RTX 30/40/50 系列及 A10/A100 等，更多型号可查看 [CUDA GPU 计算能力](https://developer.nvidia.cn/cuda-gpus)
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> - 虽然 vLLM 可在 T4/V100 等 CC 7.x 的 NVIDIA GPU 上启动，但容易出现超时或 OOM，不推荐使用。
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> - 当前，PaddleOCR-VL 暂不支持 ARM 架构 CPU。后续将根据实际需求扩展更多硬件支持，敬请期待！
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> - vLLM、SGLang 和 FastDeploy 无法在 Windows 或 macOS 上原生运行，请使用我们提供的 Docker 镜像。
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由于不同硬件所需的依赖各不相同，如果您的硬件满足上述表格的要求，请参考下表查看对应的教程进行环境配置：
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@ -132,7 +132,7 @@ PaddleOCR-VL 是一款先进、高效的文档解析模型，专为文档中的
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</tbody>
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</table>
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> [!TIP]
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> TIP:
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> 例如您使用的是 RTX 50 系 GPU，满足 PaddlePaddle 和 vLLM 推理方式的设备要求，请参考 [PaddleOCR-VL NVIDIA Blackwell 架构 GPU 环境配置教程](./PaddleOCR-VL-NVIDIA-Blackwell.md) 完成环境配置后再进行 PaddleOCR-VL 的使用。
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## 1. 环境准备
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@ -198,7 +198,7 @@ python -m pip install https://paddle-whl.bj.bcebos.com/nightly/cu126/safetensors
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python -m pip install https://xly-devops.cdn.bcebos.com/safetensors-nightly/safetensors-0.6.2.dev0-cp38-abi3-win_amd64.whl
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```
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> [!IMPORTANT]
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> IMPORTANT:
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> **请注意安装 3.2.1 及以上版本的飞桨框架，同时安装特殊版本的 safetensors。** 对于 macOS 用户，请使用 Docker 进行环境搭建。
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## 2. 快速开始
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@ -207,7 +207,7 @@ python -m pip install https://xly-devops.cdn.bcebos.com/safetensors-nightly/safe
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PaddleOCR-VL 支持 CLI 命令行方式和 Python API 两种使用方式，其中 CLI 命令行方式更简单，适合快速验证功能，而 Python API 方式更灵活，适合集成到现有项目中。
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> [!TIP]
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> TIP:
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> 本节所介绍的方法主要用于快速验证，其推理速度、显存占用及稳定性表现未必能满足生产环境的要求。**若需部署至生产环境，我们强烈建议使用专门的推理加速框架** ，具体方法请参考下一节。
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### 2.1 命令行方式体验
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@ -2300,7 +2300,7 @@ foreach ($result as $i => $item) {
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### 4.4 产线配置调整说明
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> [!NOTE]
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> NOTE:
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> 若您无需调整产线配置，可忽略此小节。
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调整服务化部署的 PaddleOCR-VL 配置只需以下三步：
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