LLM Inference

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.

A large language model can have a powerful GPU almost to itself and still produce a single conversation one token at a time. The accelerator finishes one

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.

Three people are using the same local AI server. Two answers are already streaming at a comfortable pace. Then a third user pastes a long document and presses Send.

There is an appealingly simple upgrade path for a busy local AI server: install another GPU. Twice the accelerators should mean something close to twice

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 LLM produces 100 tokens per second. On paper, that sounds fast. Then a real user sends a long prompt and waits several seconds before anything