With modern agentic workloads and long context windows, a common bottleneck in serving LLMs at scale is where to store a…
SemiAnalysis Twitter · SemiAnalysis (@SemiAnalysis_) · 2026-05-21
SemiAnalysis identifies KV cache storage as a key bottleneck in large-scale LLM serving and explains how Nvidia's tiered memory naming convention enables cache to extend beyond high-bandwidth memory.
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Extraction
Topics: llm-infrastructurekv-cachegpu-memorynvidiainference-at-scale
Claims
- KV cache storage is a common bottleneck when serving LLMs at scale under agentic workloads and long context windows.
- KV cache can be extended beyond GPU high-bandwidth memory into additional memory tiers.
- Nvidia provides a specific naming convention to describe the hierarchy of memory tiers used for KV cache.
Key quotes
With modern agentic workloads and long context windows, a common bottleneck in serving LLMs at scale is where to store all the KV cache.
KV cache can be extended beyond HBM into other tiers of memory.