In this article, we compare static, dynamic, and continuous batching in LLM inference, explaining how each approach impacts throughput, latency, and GPU utilization.
In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production scale.
Topics we will cover include:
Static batching, and why waiting for a full batch is simple but costly under real traffic
Dynamic batching, and how a timeout window fixes the worst of that cost
Continuous batching, and why large language models need scheduling at the token level instead of the request level
Learn how dynamic batching improves GPU utilization, how it differs from continuous batching for LLMs, and how semantic caching…
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Author(s): Mehedi Hasan Originally published on Towards AI. 3-Part Series: LLM Latency in Production (Part 1)Originally published…
