Dynamic batching: a how-to guide

What this post added

This post introduces the concept of dynamic batching for inference servers, explaining its mechanism, benefits for GPU utilization, and the latency-throughput trade-off. It details how timeout windows balance these factors and contrasts dynamic batching with static batching. The post also discusses the limitations of dynamic batching for LLMs and introduces continuous batching as a more advanced technique. Finally, it emphasizes the role of semantic caching in reducing the overall request load.