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KV cache
Trends
- 1Researchers unveil self-pruning transformer with extreme KV-cache compression●A Self-Pruning Transformer: Extreme KV-Cache Compression w/Universal Attention
A new paper on arXiv introduces a self-pruning transformer architecture that achieves extreme compression of the KV-cache using a universal attention mechanism. The approach would let large language models run with far less memory, a major cost driver in inference. The work is drawing attention among AI engineers discussing efficiency improvements for serving models at scale.
- 2AI 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.
- 3Critical unpatched RCE flaw reported in LMCache used by vLLM●🤖 CVE-2026-105192 (CVSS 9.8): unpatched RCE in LMCache, the KV-cache server used by vLLM. Multiprocess mode exposes an u
Security researchers are flagging CVE-2026-105192, a CVSS 9.8 remote code execution vulnerability in LMCache, the KV-cache server used with the vLLM inference framework. In multiprocess mode, an unauthenticated ZeroMQ socket unpickles attacker-controlled data, allowing code execution as root on official images. Versions 0.3.9 through 0.5.5 are affected, and no fix is currently available, raising concern among teams running AI infrastructure.
- 4Unpatched critical flaw allows remote code execution in LMCache●🤖 Unpatched critical RCE in LMCache, the LLM KV-cache server used with vLLM: in multiprocess mode an unauthenticated att
Security researchers have disclosed an unpatched critical remote code execution vulnerability in LMCache, the KV-cache server used alongside the vLLM inference framework. In multiprocess mode, an unauthenticated attacker can execute arbitrary code over ZeroMQ. No fixed version is available yet, leaving deployments exposed until a patch or mitigation is released.
- 5Engineer 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
- TypeLLM/TypeLLM TypeLLM: LLMs with type-safe generation
- lightseekorg/tokenspeed TokenSpeed is a speed-of-light LLM inference engine.
- amitshekhariitbhu/ai-system-design AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step.