Dynamic batching: a how-to guide

This post provides a practical guide to dynamic batching for inference servers, explaining its mechanism, the latency-throughput trade-off on GPUs, and the role of timeout windows. It contrasts dynamic batching with static batching and introduces continuous batching as a solution for autoregressive LLMs, highlighting its scheduling granularity and performance benefits. The post also emphasizes the role of semantic caching in reducing the number of requests that reach the batch queue.