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* w4afp8 支持per group * code style * 精度完成 * revert append attn utils * ffn1 动态量化 * ffn2 支持动态量化 * code style * code style * 修改单测 * 修改单测 * fix bug * Implement conditional parameter creation for layers Add parameter creation for up_gate_proj_in_scale when ep_size > 1. * code style * fix conflict * code style * code style * 修复w4aint8 精度 * fix ci --------- Co-authored-by: yuanxiaolan <yuanxiaolan01@baidu.com>
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Installation
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Quick Start
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Supported Models
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
[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!
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 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:
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
- ERNIE-4.5 Large Language Model Deployment
- ERNIE-4.5-VL Multimodal Model Deployment
- Offline Inference Development
- Online Service Deployment
- Best Practices
Supported Models
Learn how to download models, enable using the torch format, and more:
Advanced Usage
Acknowledgement
FastDeploy is licensed under the Apache-2.0 open-source license. During development, portions of vLLM code were referenced and incorporated to maintain interface compatibility, for which we express our gratitude.