How do LLMs handle GBs of KV cache in production?
This is a dev post classified by Jev as Hosting & infra (a tutorial), kept by the Dev Radar because it carries real work, not commentary.
How do LLMs handle GBs of KV cache in production? This is a popular technical LLM interview question. During inference, every attention layer creates KV tensors that store information from the tokens already processed. Instead of recomputing the entire sequence for every new token, the model reuses this KV cache. For one request, that's temporary GPU memory. But at production scale, things get interesting. → Thousands of requests → Shared prompt prefixes → Multiple inference workers → Limited GPU memory Now KV cache starts looking like a storage system. Imagine a new request shares 80
Posted by SCR (7.3k followers) 5 days ago · 9 likes · 606 views · view the original post on X. Kept by the Dev Radar as Hosting & infra. Tools mentioned: lmcache.
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