Long-context serving: KV memory, prefill cost, context parallelism and cheaper attention
What changes when requests carry 100,000 tokens or more: per-sequence KV memory, quadratic prefill cost, chunking, KV quantisation and offload, sliding and sparse attention, context parallelism across GPUs, and how to price and schedule it.
17 min·Verified 2026-111 animated diagramPro+50 XP
Share
A 4,000-token chat and a 200,000-token codebase question run the same model and need very different engineering. Long context moves the bottleneck from the weights to the KV cache and from decode to prefill. This lesson gives the numbers and the techniques.
Pro content
Keep reading with Inference Academy Pro
Unlock the rest of this lesson, including 1 animated diagram, plus every advanced lesson, hands-on lab, the larger playground models and completion certificates.