775edcc09a
* add start intercept * Adjustment GraphOptConfig * pre-commit * default use cudagraph * set default value * default use cuda graph * pre-commit * fix test case bug * disable rl * fix moba attention * only support gpu * Temporarily disable PD Disaggregation * set max_num_seqs of test case as 1 * set max_num_seqs and temperature * fix max_num_batched_tokens bug * close cuda graph * success run wint2 * profile run with max_num_batched_tokens * 1.add c++ memchecker 2.success run wint2 * updatee a800 yaml * update docs * 1. delete check 2. fix plas attn test case * default use use_unique_memory_pool * add try-except for warmup * ban mtp, mm, rl * fix test case mock * fix ci bug * fix form_model_get_output_topp0 bug * fix ci bug * refine deepseek ci * refine code * Disable PD * fix sot yaml
English | 简体中文
Installation
|
Quick Start
|
Supported Models
FastDeploy : Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle
News
[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!
[2025-08] 🔥 Released FastDeploy v2.1: A brand-new KV Cache scheduling strategy has been introduced, and expanded support for PD separation and CUDA Graph across more models. Enhanced hardware support has been added for platforms like Kunlun and Hygon, along with comprehensive optimizations to improve the performance of both the service and inference engine.
[2025-07] The FastDeploy 2.0 Inference Deployment Challenge is now live! Complete the inference deployment task for the ERNIE 4.5 series open-source models to win official FastDeploy 2.0 merch and generous prizes! 🎁 You're welcome to try it out and share your feedback! 📌Sign up here 📌Event details
[2025-06] 🔥 Released FastDeploy v2.0: Supports inference and deployment for ERNIE 4.5. Furthermore, we open-source an industrial-grade PD disaggregation with context caching, dynamic role switching for effective resource utilization to further enhance inference performance for MoE models.
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.