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Dynamic Batching for Inference

Dynamic Batching for Inference

1
posts
2026

This post introduces and explains dynamic batching as a server-side mechanism to improve GPU utilization in inference servers. It details 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 the role of semantic caching in reducing the number of requests that need to be batched.

2026

Dynamic batching: a how-to guide

6/21/2026

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.