How Tiered KV Cache Storage Lifts 480B Inference Throughput by 29–40%
This story is from 2026-09-04. It is preserved in the archive; the latest stories are on the live feed.
Tiered KV Cache storage can lift inference throughput for 480B-class models by 29–40%. The core idea is to free attention computation from the constraint of GPU memory capacity—by placing KV Cache entries across tiers based on access frequency, hot data stays in GPU memory while cold data is offloa…
Read the full story at DEV Community — AI ↗
Timeline · 1 report
- 2026-09-04 12:09 · DEV Community — AI
How Tiered KV Cache Storage Lifts 480B Inference Throughput by 29–40%