AB
AB
AiBoss

All Categories

project

FastDeploy - Baidu's large-scale model inference deployment tool

FastDeploy is a high-performance inference and deployment tool developed by Baidu based on the PaddlePaddle framework, specifically designed for Large Language Models (LLMs) and Visual Language Models (VLMs). FastDeploy supports various hardware...

What is FastDeploy?

FastDeploy is a high-performance inference and deployment tool developed by Baidu based on the PaddlePaddle framework, specifically designed for large language models (LLMs) and visual language models (VLMs). FastDeploy supports multiple hardware platforms (such as NVIDIA GPUs and Kunlun XPUs), and features load balancing, quantization optimization, and distributed inference, significantly improving model inference performance and reducing hardware costs. FastDeploy is compatible with OpenAI APIs and vLLM interfaces, supporting local and service-oriented inference, simplifying the deployment process for large models. The latest version, FastDeploy 2.0, further optimizes performance, supports efficient deployment of large models such as Wenxin 4.5, and introduces 2-bit quantization technology, significantly reducing memory usage and hardware resource requirements during inference.

FastDeploy's main functions

  • Efficient inference deploymentIt supports multiple hardware platforms (such as NVIDIA GPU, Kunlun Chip XPU, etc.), provides one-click deployment capability, and simplifies the inference deployment process of large models.
  • Performance optimizationThe model inference performance is significantly improved through techniques such as quantization (including 2-bit quantization), CUDA Graph optimization, and speculative decoding.
  • Distributed reasoningIt supports large-scale distributed inference, optimizes communication efficiency, and improves the inference efficiency of large-scale models.
  • Load balancing and schedulingReal-time load awareness and distributed load balancing scheduling based on Redis are implemented to optimize cluster performance.
  • Ease of useIt provides a concise Python interface and detailed documentation, making it easy for users to get started and use it quickly.
  • 2-bit quantization technologyIntroducing 2-bit quantization significantly reduces memory usage and hardware resource requirements during inference, supporting the deployment of models with hundreds of billions of parameters on a single GPU.
  • compatibilityIt is compatible with the OpenAI API and vLLM interface, supports local and service-oriented inference, completes local inference in 4 lines of code, and starts the service in 1 line of command.

FastDeploy's technical principles

  • PD separation for load balancingBy using PD (Programmable Detection) separation technology, model parameters are distributed across multiple devices, enabling efficient distributed inference. FastDeploy 2.0 introduces context caching and dynamic instance role switching mechanisms to further optimize resource utilization. This technology balances Service Level Objective (SLO) compliance and throughput, significantly improving inference efficiency in large-scale industrial deployments and ensuring stable system operation under high load.
  • Unified KV buffered transmissionFastDeploy provides a lightweight, high-performance key-value (KV) cached transfer mechanism that can intelligently select NVLink or RDMA for data transfer. In FastDeploy 2.0, the self-developed transfer library further optimizes communication efficiency and supports multiple hardware platforms, including NVIDIA GPUs and Kunlun Core XPUs.
  • Quantitative technologyBy quantizing and compressing the model, memory usage and inference latency are significantly reduced. FastDeploy 2.0 introduces 2-bit quantization technology to further reduce memory usage, enabling a single GPU to deploy models with hundreds of billions of parameters. This quantization technique maintains near-lossless inference accuracy while reducing hardware resource requirements.
  • Speculative Decoding and OptimizationFastDeploy 2.0 optimizes inference performance by integrating kernel acceleration for pre- and post-processing, dynamic batch processing, and parallel verification. In FastDeploy 2.0, speculative decoding technology is further optimized, supporting multi-token prediction (MTP) and chunked prefill.
  • CUDA Graph OptimizationFastDeploy 2.0 utilizes PaddlePaddle's dynamic-to-static graph capture technology, supporting CUDA Graph optimization. Through full-graph capture and dynamic graph optimization, decoding speed is significantly improved.

FastDeploy's project address

  • Project official websitehttps://paddlepaddle.github.io/FastDeploy/
  • GitHub repositoryhttps://github.com/PaddlePaddle/FastDeploy

Application scenarios of FastDeploy

  • Natural Language Processing (NLP)It can be used in text generation, machine translation, sentiment analysis, and question answering systems to improve text processing efficiency.
  • Multimodal applicationsIt supports text and image generation, video subtitle generation, and image description generation, combining text and image processing capabilities.
  • Industrial-grade deploymentIt is suitable for large-scale distributed inference, optimizes resource utilization through real-time load balancing, and supports multiple hardware platforms.
  • academic researchIt provides researchers with high-performance inference tools to support model optimization and multimodal research.
  • Enterprise applicationsIt enables intelligent customer service, content recommendation, and data analysis, thereby improving enterprise service and decision-making efficiency.
Back