[Doc] Update docs for v2.3.0rc0

README.md

@@ -0,0 +1 @@
+README_CN.md

README_CN.md

@@ -26,11 +26,12 @@
 # FastDeploy ：基于飞桨的大语言模型与视觉语言模型推理部署工具包
 
 ## 最新活动
-**[2025-09] 🔥 FastDeploy v2.2 全新发布**: HuggingFace生态模型兼容，性能进一步优化，更新增对[baidu/ERNIE-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking)支持!
 
-**[2025-08] FastDeploy v2.1 发布**:全新的KV Cache调度策略，更多模型支持PD分离和CUDA Graph，昆仑、海光等更多硬件支持增强，全方面优化服务和推理引擎的性能。
+**[2025-11]** 🔥FastDeploy v2.3-rc0 PaddleOCR-VL 0.9B推理性能专项优化发布，**相比vLLM吞吐提升35%**！[部署指南](docs/best_practices/PaddleOCR-VL-0.9B.md)
+
+**[2025-09] FastDeploy v2.2 全新发布**: HuggingFace生态模型兼容，性能进一步优化，更新增对[baidu/ERNIE-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking)支持!
 
-**[2025-07] 《FastDeploy2.0推理部署实测》专题活动已上线!** 完成文心4.5系列开源模型的推理部署等任务，即可获得骨瓷马克杯等FastDeploy2.0官方周边及丰富奖金！🎁 欢迎大家体验反馈～ 📌[报名地址](https://www.wjx.top/vm/meSsp3L.aspx#)   📌[活动详情](https://github.com/PaddlePaddle/FastDeploy/discussions/2728)
+**[2025-08] FastDeploy v2.1 发布**:全新的KV Cache调度策略，更多模型支持PD分离和CUDA Graph，昆仑、海光等更多硬件支持增强，全方面优化服务和推理引擎的性能。
 
 ## 关于
 

README_EN.md

@@ -0,0 +1,89 @@
+English | [简体中文](README_CN.md)
+<p align="center">
+  <a href="https://github.com/PaddlePaddle/FastDeploy/releases"><img src="https://github.com/user-attachments/assets/42b0039f-39e3-4279-afda-6d1865dfbffb" width="500"></a>
+</p>
+<p align="center">
+    <a href=""><img src="https://img.shields.io/badge/python-3.10-aff.svg"></a>
+    <a href=""><img src="https://img.shields.io/badge/os-linux-pink.svg"></a>
+    <a href="https://github.com/PaddlePaddle/FastDeploy/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/FastDeploy?color=9ea"></a>
+    <a href="https://github.com/PaddlePaddle/FastDeploy/commits"><img src="https://img.shields.io/github/commit-activity/m/PaddlePaddle/FastDeploy?color=3af"></a>
+    <a href="https://github.com/PaddlePaddle/FastDeploy/issues"><img src="https://img.shields.io/github/issues/PaddlePaddle/FastDeploy?color=9cc"></a>
+    <a href="https://github.com/PaddlePaddle/FastDeploy/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/FastDeploy?color=ccf"></a>
+
+</p>
+
+<p align="center">
+     <a href="https://trendshift.io/repositories/4046" target="_blank"><img src="https://trendshift.io/api/badge/repositories/4046" alt="PaddlePaddle%2FFastDeploy | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></br>
+    <a href="https://paddlepaddle.github.io/FastDeploy/get_started/installation/nvidia_gpu/"><b> Installation </b></a>
+    |
+    <a href="https://paddlepaddle.github.io/FastDeploy/get_started/quick_start"><b> Quick Start </b></a>
+    |
+    <a href="https://paddlepaddle.github.io/FastDeploy/supported_models/"><b> Supported Models </b></a>
+
+</p>
+
+--------------------------------------------------------------------------------
+# FastDeploy : Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle
+
+## News
+
+**[2025-11] 🔥 FastDeploy v2.3-rc0: The specialized optimization release for PaddleOCR-VL 0.9B inference performance has been launched, achieving a 35% increase in throughput compared to vLLM! [Deployment Guide](docs/best_practices/PaddleOCR-VL-0.9B.md)
+
+**[2025-09] FastDeploy v2.2 is newly released!** It now offers compatibility with models in the HuggingFace ecosystem, has further optimized performance, and newly adds support for [baidu/ERNIE-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking)!
+
+## About
+
+**FastDeploy** is an inference and deployment toolkit for large language models and visual language models based on PaddlePaddle. It delivers **production-ready, out-of-the-box deployment solutions** with core acceleration technologies:
+
+- 🚀 **Load-Balanced PD Disaggregation**: Industrial-grade solution featuring context caching and dynamic instance role switching. Optimizes resource utilization while balancing SLO compliance and throughput.
+- 🔄 **Unified KV Cache Transmission**: Lightweight high-performance transport library with intelligent NVLink/RDMA selection.
+- 🤝 **OpenAI API Server and vLLM Compatible**: One-command deployment with [vLLM](https://github.com/vllm-project/vllm/) interface compatibility.
+- 🧮 **Comprehensive Quantization Format Support**: W8A16, W8A8, W4A16, W4A8, W2A16, FP8, and more.
+- ⏩ **Advanced Acceleration Techniques**: Speculative decoding, Multi-Token Prediction (MTP) and Chunked Prefill.
+- 🖥️ **Multi-Hardware Support**: NVIDIA GPU, Kunlunxin XPU, Hygon DCU, Ascend NPU, Iluvatar GPU, Enflame GCU, MetaX GPU, Intel Gaudi etc.
+
+## Requirements
+
+- OS: Linux
+- Python: 3.10 ~ 3.12
+
+## Installation
+
+FastDeploy supports inference deployment on **NVIDIA GPUs**, **Kunlunxin XPUs**, **Iluvatar GPUs**, **Enflame GCUs**, **Hygon DCUs** and other hardware. For detailed installation instructions:
+
+- [NVIDIA GPU](./docs/get_started/installation/nvidia_gpu.md)
+- [Kunlunxin XPU](./docs/get_started/installation/kunlunxin_xpu.md)
+- [Iluvatar GPU](./docs/get_started/installation/iluvatar_gpu.md)
+- [Enflame GCU](./docs/get_started/installation/Enflame_gcu.md)
+- [Hygon DCU](./docs/get_started/installation/hygon_dcu.md)
+- [MetaX GPU](./docs/get_started/installation/metax_gpu.md)
+- [Intel Gaudi](./docs/get_started/installation/intel_gaudi.md)
+
+**Note:** We are actively working on expanding hardware support. Additional hardware platforms including Ascend NPU are currently under development and testing. Stay tuned for updates!
+
+## Get Started
+
+Learn how to use FastDeploy through our documentation:
+- [10-Minutes Quick Deployment](./docs/get_started/quick_start.md)
+- [ERNIE-4.5 Large Language Model Deployment](./docs/get_started/ernie-4.5.md)
+- [ERNIE-4.5-VL Multimodal Model Deployment](./docs/get_started/ernie-4.5-vl.md)
+- [Offline Inference Development](./docs/offline_inference.md)
+- [Online Service Deployment](./docs/online_serving/README.md)
+- [Best Practices](./docs/best_practices/README.md)
+
+## Supported Models
+
+Learn how to download models, enable using the torch format, and more:
+- [Full Supported Models List](./docs/supported_models.md)
+
+## Advanced Usage
+
+- [Quantization](./docs/quantization/README.md)
+- [PD Disaggregation Deployment](./docs/features/disaggregated.md)
+- [Speculative Decoding](./docs/features/speculative_decoding.md)
+- [Prefix Caching](./docs/features/prefix_caching.md)
+- [Chunked Prefill](./docs/features/chunked_prefill.md)
+
+## Acknowledgement
+
+FastDeploy is licensed under the [Apache-2.0 open-source license](./LICENSE). During development, portions of [vLLM](https://github.com/vllm-project/vllm) code were referenced and incorporated to maintain interface compatibility, for which we express our gratitude.

docs/get_started/installation/kunlunxin_xpu.md

@@ -27,9 +27,9 @@ Verified platform:
 ```bash
 mkdir Work
 cd Work
-docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0
+docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0-rc0
 docker run --name fastdeploy-xpu --net=host -itd --privileged -v $PWD:/Work -w /Work \
-    ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0 \
+    ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0-rc0 \
     /bin/bash
 docker exec -it fastdeploy-xpu /bin/bash
 ```
@@ -51,7 +51,7 @@ python -m pip install --pre paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/
 ### Install FastDeploy (**Do NOT install via PyPI source**)
 
 ```bash
-python -m pip install fastdeploy-xpu==2.3.0 -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-xpu-p800/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+python -m pip install fastdeploy-xpu==2.3.0-rc0 -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-xpu-p800/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
 ```
 
 Alternatively, you can install the latest version of FastDeploy (Not recommended)

docs/get_started/installation/nvidia_gpu.md

@@ -15,15 +15,15 @@ The following installation methods are available when your environment meets the
 **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.
 
 ```shell
-docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.2.1
+docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.3.0-rc0
 ```
 
 ## 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
 # Install stable release
-python -m pip install paddlepaddle-gpu==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
+python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
 
 # Install latest Nightly build
 python -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu126/
@@ -34,7 +34,7 @@ Then install fastdeploy. **Do not install from PyPI**. Use the following methods
 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
+python -m pip install fastdeploy-gpu==2.3.0-rc0 -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
@@ -43,7 +43,7 @@ python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages
 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
+python -m pip install fastdeploy-gpu==2.3.0-rc0 -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
@@ -64,7 +64,7 @@ docker build -f dockerfiles/Dockerfile.gpu -t fastdeploy:gpu .
 
 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==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
+python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
 ```
 
 Then clone the source code and build:

docs/zh/get_started/installation/kunlunxin_xpu.md

@@ -27,9 +27,9 @@
 ```bash
 mkdir Work
 cd Work
-docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0
+docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0-rc0
 docker run --name fastdeploy-xpu --net=host -itd --privileged -v $PWD:/Work -w /Work \
-    ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0 \
+    ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.3.0-rc0 \
     /bin/bash
 docker exec -it fastdeploy-xpu /bin/bash
 ```
@@ -51,7 +51,7 @@ python -m pip install --pre paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/
 ### 安装 FastDeploy（**注意不要通过 pypi 源安装**）
 
 ```bash
-python -m pip install fastdeploy-xpu==2.3.0 -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-xpu-p800/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
+python -m pip install fastdeploy-xpu==2.3.0-rc0 -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-xpu-p800/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
 ```
 
 或者你也可以安装最新版 FastDeploy（不推荐）