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FastDeploy - Baidu's Large Model Inference Deployment Tool

Author: AI小集 · Original source: AI工具集

What is FastDeploy

FastDeploy is a high-performance inference and deployment tool developed by Baidu based on the PaddlePaddle framework, designed specifically for large language models (LLMs) and vision-language models (VLMs). FastDeploy supports multiple hardware platforms (such as NVIDIA GPU, Kunlun Core XPU, etc.), and features load balancing, quantization optimization, distributed inference, etc., significantly improving model inference performance and reducing hardware costs. FastDeploy is compatible with the OpenAI API and vLLM interfaces, supports local and service-based inference, and simplifies the deployment process of large models. The latest version, FastDeploy 2.0, further optimizes performance and supportsERNIE 4.5and other large models for efficient deployment, introducing 2-bit quantization technology, significantly reducing memory usage and hardware resource requirements during inference.

FastDeploy

Main Features of FastDeploy

  • Efficient Inference Deployment: Supports multiple hardware platforms (such as NVIDIA GPU, Kunlun Core XPU, etc.), providing one-click deployment capability and simplifying the inference deployment process for large models.
  • Performance Optimization: Through quantization (including 2-bit quantization), CUDA Graph optimization, speculative decoding, and other technologies, significantly improves model inference performance.
  • Distributed Inference: Supports large-scale distributed inference, optimizes communication efficiency, and improves the inference efficiency of large-scale models.
  • Load Balancing and Scheduling: Implements real-time load awareness and distributed load balancing scheduling based on Redis, optimizing cluster performance.
  • Ease of Use: Provides a concise Python interface and detailed documentation, making it convenient for users to quickly get started and use.
  • 2-bit Quantization Technology: Introduces 2-bit quantization, significantly reducing memory usage and hardware resource requirements during inference, and supports single-card deployment of models with hundreds of billions of parameters.
  • Compatibility: Compatible with the OpenAI API and vLLM interfaces, supports local and service-based inference, completes local inference in 4 lines of code, and starts the service with 1 command.

Technical Principles of FastDeploy

  • Load-Balanced PD Disaggregation: Through PD disaggregation technology, model parameters are distributed across multiple devices to achieve efficient distributed inference. In FastDeploy 2.0, context caching and dynamic instance role switching mechanisms are introduced to further optimize resource utilization. This technology can balance service-level objective (SLO) compliance and throughput, significantly improving inference efficiency in large-scale industrial deployments and ensuring the system can still operate stably under high-load conditions.
  • Unified KV Cache Transfer: FastDeploy provides a lightweight and high-performance KV cache transfer mechanism that can intelligently choose NVLink or RDMA for data transmission. In FastDeploy 2.0, the self-developed transmission library further optimizes communication efficiency and supports multiple hardware platforms, including NVIDIA GPU and Kunlun Core XPU.
  • Quantization Technology: Through quantization, models are compressed, significantly reducing memory usage and inference latency. In FastDeploy 2.0, 2-bit quantization technology is introduced to further reduce memory usage, allowing a single card to deploy models with hundreds of billions of parameters. This quantization technology can maintain near-lossless inference accuracy and reduce hardware resource requirements.
  • Speculative Decoding and Optimization: Through fused kernel acceleration for pre- and post-processing, dynamic batching, parallel verification, and other means, inference performance is optimized. In FastDeploy 2.0, speculative decoding technology is further optimized, supporting multi-token prediction (MTP) and chunked prefill.
  • CUDA Graph Optimization: Uses PaddlePaddle's dynamic-to-static technology for graph capture, supporting CUDA Graph optimization. In FastDeploy 2.0, through full-graph capture and dynamic graph optimization, decoding speed is significantly improved.

FastDeploy project address

  • Project official website：https://paddlepaddle.github.io/FastDeploy/
  • GitHub repository：https://github.com/PaddlePaddle/FastDeploy

FastDeploy application scenarios

  • Natural Language Processing (NLP): Used in text generation, machine translation, sentiment analysis, and question answering systems, etc., to improve text processing efficiency.
  • Multimodal applications: Supports image-text generation, video subtitle generation, and image caption generation, combining text and image processing capabilities.
  • Industrial-grade deployment: Suitable for large-scale distributed inference, optimizing resource utilization through real-time load balancing, and supporting multiple hardware platforms.
  • Academic research: Provides researchers with high-performance inference tools, supporting model optimization and multimodal research.
  • Enterprise-level applications: Helps intelligent customer service, content recommendation, and data analysis, improving enterprise service and decision-making efficiency.