search
KV cache
Trends
- 1AI agents burn five times more tokens than humans▼AI agents use 5x more tokens than humans as cached prompts explode, headed for 10x — agents are mostly rereading what they've already seen, skyrocketing KV cache demand threatens already-worsening RAM shortages
AI agents are consuming roughly five times more tokens than human users, a figure projected to reach 10x as agentic workflows proliferate. Much of that usage comes from agents repeatedly rereading content they have already processed, driving explosive growth in cached prompts and KV cache memory requirements. The surge is raising alarms about RAM demand, which was already under strain, and could intensify memory shortages across the AI infrastructure industry.
- 2Engineer implements KV cache in custom GPT to learn prompt caching●いくら艦長とはいえ、charについてはただ見守るしかないかもしれません 自作GPTにKVキャッシュを実装し、プロンプトキャッシュの仕組みを学んだ - $shibayu36->blog; https:// blog.shibayu36.org
Japanese software engineer shibayu36 has published a blog post describing how he implemented a KV cache in his self-built GPT model, using the exercise to learn how prompt caching works in large language model inference. The writeup walks through the mechanics of caching attention key-value pairs to speed up generation. It is being shared among developers interested in LLM internals and practical implementations of transformer optimization techniques.
Repos
- incoai/splash A local inference engine for Apple silicon, built around the model.
- kvmem/kvmem-llama.cpp
- amitshekhariitbhu/ai-system-design AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step.
- TypeLLM/TypeLLM TypeLLM: LLMs with type-safe generation