Originally published on AI Tech Connect.
What you will actually measure Most teams load-test their large language model service the way they load-test a REST API: point a tool at the endpoint, ramp up virtual users, watch requests-per-second and average response time, and call it capacity. Then production traffic hits, the first token takes four seconds to appear during the evening peak, and nobody can say why — because the dashboard was measuring the wrong things all along. LLM serving does not behave like a stateless CRUD endpoint. Responses stream token by token rather than arriving in one block. Output length varies enormously from one request to the next. The meaningful signals live at the token level, not the request level. And the hardware saturates in a pattern that has more to do with GPU memory and a structure called…
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