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Incident Response for GPU Fleets
An incident on a GPU fleet is a training run that stopped, a serving endpoint burning its error budget, or a fleet-wide symptom nobody has explained yet. The response has a shape: detect, stabilize, diagnose, repair, return through the gate, write it up. The stabilizing move (drain the node, restart from checkpoint, or shift traffic) comes before the diagnosis, because a frontier run loses more per minute than any investigation is worth. This page gives the triage order, the 3am decision tree, the spare-capacity arithmetic behind drain-and-replace, and what a fleet postmortem has to contain.
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Behavioral & OwnershipWalk me through the worst on-call incident you have handled.→Kubernetes, Slurm & GPU SchedulingA GPU in a running 512-GPU training job is throwing errors. How do you get it out of the job without losing the run?→Napkin Math, Cost & CapacityHow long does that 70B run take on 16,384 H100s at 40% MFU?→Behavioral & OwnershipThree incidents are open, your pager is going off, and a customer is escalating. What do you do first?→GPU Fleet Reliability & ObservabilityYou own the on-call rota for a GPU fleet. What is allowed to wake someone at 3am, and what must not?→GPU Fleet Reliability & ObservabilityA node reports a GPU has fallen off the bus. What happened, what can software do, and what should the platform do automatically?→
