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)→
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