LLM Continuous Batching Explained: The Secret Behind Fast LLMs
Last Updated on August 24, 2026 by Editorial Team
Author(s): Divy Yadav
Originally published on Towards AI.
The scheduling trick behind every fast LLM response, and the real reason your Claude replies don’t crawl.
Right now, thousands of people are asking the same AI questions you are.
The article explains why “fast” LLM responses depend less on model speed and more on how inference is scheduled: running one request at a time wastes GPU capacity, while static batching wastes even more time because the slowest request holds the rest. Continuous batching solves this by swapping requests in and out at each generation step so finished “seats” immediately take new work, significantly improving throughput and reducing latency. It also clarifies that each request has two distinct phases—prefill (processing the whole prompt in one heavy burst) and decode (generating tokens step-by-step)—and that the scheduler must juggle both across many concurrent requests. Memory is another hidden bottleneck due to KV caches, but techniques like PagedAttention manage KV cache memory in small chunks to allow more concurrent requests. Finally, the article notes practical trade-offs such as chunked prefill to prevent new long prompts from stalling others, and it provides guidance on API latency patterns, plus a reminder not to confuse continuous batching with dynamic batching or speculative decoding.
Read the full blog for free on Medium.
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