@JunchenJiang: I was delighted to give three keynotes recently, one at the 3rd HotInfra workshop at ISCA'26 (hosted by Prof. Jian Huan…
Summary
Junchen Jiang delivered three keynotes arguing that KV cache is an underappreciated asset for LLM inference, enabling cost savings, latency reduction, and quality improvements, and should be treated as a core data layer in future inference infrastructure.
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I was delighted to give three keynotes recently, one at the 3rd HotInfra workshop at ISCA’26 (hosted by Prof. Jian Huang), one at the UIUC Systems Groups retreat (hosted by @tianyin_xu ), and a CMU guest lecture (hosted by Prof. Lei Li).
The message was simple: KV cache is BEYOND cacheable intermediate states.
It is what makes inference systems SMART.
- If cached, it saves cost and reduces latency.
- If analyzed, it reveals how the model attends to and understands context.
- If optimized, it can improve not only performance, but also output quality.
It’s not a liability, but an ASSET — (almost) always the more you have, the better.
That is the part most people in industry still underestimate, even though the techniques are already here today: KV cache compression, blending, semantic reuse, cross-model sharing, attention steering, KV-cache-based retrieval, and more.
The gap is not imagination. The gap is productization.
KV cache will become a core AI-native data layer in future inference infrastructure — not just something we store, but something we manage, analyze, and optimize.
#KVCache, #LMCache, #LLMInference, #Tensormesh, #vLLM, #SGLang, #MLSys, #HotInfra
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