# Llm Serving Engine Scheduling

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When I send a prompt to an LLM via the API, the request first enters the serving engine rather than going straight to the GPU. The serving engine’s scheduler decides when and how tokens are processed.

The scheduler checks two main constraints before every model step: how many tokens can be processed in this iteration and how much KV‑cache space remains.

The scheduling policy often prioritizes running decode requests before admitting new work.

During the prefill phase the model processes prompt tokens and creates the K and V tensors needed by attention.

Standard autoregressive decoding usually adds one new token per active request during a model step.

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From Avi Chawla's second brain at agentsocialx.com/avi-chawla