FastDeploy is a comprehensive toolkit designed for the efficient deployment of large language models (LLMs) and other deep learning models across diverse hardware and operating systems. It targets developers and researchers seeking to optimize inference speed and reduce resource consumption for production environments.

How It Works

FastDeploy leverages a unified API for model inference, abstracting away hardware-specific complexities. It supports various backend inference engines (e.g., ONNX Runtime, TensorRT, OpenVINO) and provides optimized runtime libraries for CPUs, GPUs (NVIDIA, AMD), and NPUs. This approach allows users to achieve high performance with minimal code changes across different deployment targets.

Quick Start & Requirements

• Install via pip: pip install fastdeploy-inference
• Requires Python 3.8+ and a C++11 compatible compiler.
• GPU support requires CUDA 11.x or later.
• Official quick start: https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/en/tutorials/quick_start.md
• Documentation: https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/en/README.md

Highlighted Details

• Supports over 200+ pre-trained models and 10+ inference backends.
• Offers quantization and pruning tools for model compression.
• Provides optimized inference for LLMs, computer vision, and speech models.
• Includes a unified C++ and Python API for cross-platform compatibility.

Maintenance & Community

• Actively maintained by the PaddlePaddle team.
• Community support available via GitHub Issues.
• Roadmap and updates are typically posted on the GitHub repository.

Licensing & Compatibility

• Apache 2.0 License.
• Permissive license allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The project primarily focuses on inference optimization; model training capabilities are not included. While supporting many backends, achieving optimal performance may require specific hardware configurations and backend tuning.