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* [Optim] Optimize costtime in checking tasks in engine-worker-queue * Update fastdeploy/engine/common_engine.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update fastdeploy/inter_communicator/engine_worker_queue.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * [Docs] Add docstring to set_exist_tasks method (#5382) * Initial plan * Add docstring to set_exist_tasks method Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> * [Docs] Add docstring documentation to exist_tasks() method (#5381) * Initial plan * Add comprehensive docstring to exist_tasks() method Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> * [Optimization] Conditionally initialize shared memory for single-node deployments only (#5383) * Initial plan * Conditionally initialize exist_tasks_intra_signal for single-node deployments Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> * Use is_single_node flag for consistent deployment type checking Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> * Remove redundant None checks in exist_tasks methods Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com> * format code --------- Co-authored-by: Jiang-Jia-Jun <jiangjiajun@baidu.com> Co-authored-by: YuBaoku <49938469+EmmonsCurse@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.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 is newly released! It adds deployment support for two major models, ERNIE-4.5-VL-28B-A3B-Thinking and PaddleOCR-VL-0.9B, across multiple hardware platforms. It further optimizes comprehensive inference performance and brings more deployment features and usability enhancements. For all the upgrade details, refer to the v2.3 Release Note.
[2025-09] FastDeploy v2.2: 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, 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:
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.