KV cache

The shared LLM server has not crashed. GPU utilization remains high, requests are still finishing, and the monitoring dashboard shows no conventional out-of-memory failure.

Four people are talking to the same local LLM. One asks a short question and gets an answer in seconds. Another requests a long explanation.

A local AI server can feel almost instantaneous when one person is using it. Add a second conversation and it may still feel fine. Add several people with

A local model may advertise a 32K, 64K, 128K, or even larger context window. That does not mean you should configure your local runtime to use the maximum.

A local LLM can fit comfortably into your GPU at an 8,000-token context and run out of memory when you increase that context several times over.