Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient
中文摘要
连续批处理通过动态调度和不规则批处理，优化LLM推理效率，超越静态批处理，能同时服务多用户请求。
English Summary
Continuous batching dynamically groups LLM inference requests, improving efficiency over static batching. It uses dynamic scheduling and ragged batching to serve multiple users concurrently.
Original Excerpt
This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch together.