rollout

A fleet-wide version change is the most likely cause of the next unexplained performance regression, so the rollout is designed to make that attributable. Cohorts, a canary that measures rather than boots, and the rollback that has to be real before the first node is touched.

Two steps consume most of the week and neither is the one people plan for. The gate that can end the project on day one, the multi-hour download, the correctness check almost nobody runs, and the canary that has to see real traffic before Friday.

A model update is a deploy whose failures are invisible to every deployment metric. What has to be re-validated even for a point release, the canary that watches output shape rather than error rate, and the fingerprint that makes a customer report investigable.

Speculative decoding buys latency with spare compute, and a busy fleet has no spare compute. The speedup formula with acceptance rate and draft cost inside it, the batch size where the gain turns negative, where the draft runs and what it costs, the monitoring that catches a silent regression, and rollout per class.