#KV Cache

Articles about KV Cache — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with kv cache. ← All posts

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Pratik Dhanave · ·5 min read

Memory Management for LLM Inference — The KV Cache Problem

The single largest consumer of GPU memory during inference often isn't the model — it's the KV cache, the growing store of attention state that lets a model generate text without recomputing the past. Managing it well is the difference between serving a handful of users and serving thousands on the same hardware. This post explains what the KV cache is, why it's such a brutal memory problem, and how PagedAttention borrowed a 1960s operating-systems idea to solve it.

The KV cache often exceeds the model weights in HBM, and naive contiguous allocation wrecks concurrency with fragmentation. PagedAttention applies operating-system paging to fix it — a core inference bottleneck solved by borrowing a classic systems idea.

#GPU#KV Cache#PagedAttention#Inference#Performance#LLMs#Concurrency

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.