Continuous Batching and PagedAttention for Production LLM Serving in September 2026: Scheduling Tradeoffs That Protect TTFT
Continuous batching and PagedAttention are how most production LLM servers keep GPUs busy when traffic is bursty and sequence lengths differ. They are also easy to mis-tune: raise concurrency too far and time-to-first-token collapses; leave pages fragmented and you “run out of memory” with half the HBM free. This guide explains the scheduling tradeoffs that matter in September 2026—what to measure, which knobs move latency versus throughput, and when a simpler fixed-batch setup is still the right call. Image: mikemacmarketing / photo on flickr via Wikimedia Commons (CC BY 2.0) Image: Midjourney; prompt suggested by Grok via Wikimedia Commons (Public domain) What continuous batching actually does In a naive batch server, you wait until a batch fills, run prefill and decode for that group, then return results. Idle slots appear as soon as short sequences finish, and long sequences block everything behind them. Continuous batching (iteration-level scheduling) admits ...