Dynamic Batching for Inference

This post details dynamic batching as a server-side mechanism to improve GPU utilization in inference servers. It explains how dynamic batching combines individual requests into batches at runtime, trading increased latency for higher throughput. The post contrasts dynamic batching with static batching, explains the role of timeout windows, and discusses the limitations of dynamic batching for autoregressive LLMs, leading into the concept of continuous batching. It also highlights Redis's role in providing low-latency caching and memory solutions (Redis Iris, Redis LangCache, Redis Agent Memory) for these challenges, and discusses semantic caching as a way to reduce requests before they reach the batch queue.