# Chunked Prefill, clearly explained

DevFeed: [Chunked Prefill, clearly explained](<https://devfeed.tech/articles/chunked-prefill-clearly-explained-65863.md>)

Original publisher: [Read original article](<https://blog.dailydoseofds.com/p/chunked-prefill-clearly-explained>)

Author: Akshay Pachaar

Published: 2026-10-07T00:08:47Z

Content type: tutorial

Language: en

Sources: [Daily Dose of Data Science](<https://devfeed.tech/sources/daily-dose-of-data-science.md>)

Topics: [vllm](<https://devfeed.tech/topics/vllm.md>), [Model Routing](<https://devfeed.tech/topics/model-routing.md>)

Tags: [kv-cache](<https://devfeed.tech/tags/kv-cache.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [scheduler](<https://devfeed.tech/tags/scheduler.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

## AI overview

The article explains how vLLM chunked prefill divides long prompts into smaller token ranges so prompt processing can share GPU iterations with active decoding requests. Smaller chunks can reduce inter-token latency for ongoing responses, while larger chunks can improve time to first token; chunks that are too small add overhead and can reduce GPU utilization. It identifies --max-num-batched-tokens as a key setting and says there is no universally best chunk size.

## Source excerpt

How vLLM stops one long prompt from stalling others...