hot spares
AI infra interview questions tagged hot spares, across every topic.
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Concepts behind "hot spares"
The curriculum that explains the ideas these questions test.
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Elastic and Fault-Tolerant TrainingAt 16,384 GPUs something fails every few hours, so a training job is a loop of detect, isolate, replace and resume rather than one process that runs for months. The Llama 3 record of 466 interruptions in 54 days is the reference point, and the design goal is keeping effective training time above 90% by making each event cost minutes.🕸️ Distributed Training🔒 Premium
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Incident Response for GPU FleetsAn 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.🩺 Fleet Reliability & Observability🔒 Premium
