12 KV Cache Reduction Techniques Every AI Engineer Should Understand, Explained
blaizedsouza · x · 2026-09-11
- Avi Chawla (2.5 years running LLMs in production) published "KV Cache Engineering for LLM Serving," a systematic walkthrough of why the KV cache grows and 12 techniques to shrink it.
- KV cache memory scales with layers, KV heads, retained tokens, dimensions, bytes per value, and concurrent requests.
- Techniques include GQA/MQA (fewer shared KV heads), cross-layer KV sharing (needs purpose-trained models), sliding-window attention (local layers' cache stops growing), and others that operate at either the model architecture or the serving-engine level.
- The piece explains what each technique actually saves and the trade-offs determining which fits your setup.
Related event: Engineer's Deep Dive into KV Cache Compression for LLM Serving(2 posts)→
More from Infra
- The shadow GPU capacity market is formalizing, with Meta selling excess compute to outside buyers — DavidLinthicum · 2026-09-11
- Engram's random reads don't suit SSDs; CPU-memory over NVLink could serve all 72 GPUs — bookwormengr · 2026-09-11
- 80% of the DIY LLM inference hype posters have already quit — it's brutally hard systems work — abhijithneil · 2026-09-11
- Hugging Face's Ultra Scale Playbook: a free book on training LLMs on GPU clusters — mdancho84 · 2026-09-11
- Is inference latency becoming the biggest bottleneck for production AI agents? — Euphoric_Sea632 · 2026-09-11
- LLM Serving Metrics Thread: Why TPOT and Uptime Make or Break User Experience — abhijithneil · 2026-09-11