mtbf
AI infra interview questions tagged mtbf, across every topic.
4 questions · 2 unlocked for you
Concepts behind "mtbf"
The curriculum that explains the ideas these questions test.
Foundational
Training Uptime and Interruption StatisticsA frontier training run is a months-long job on tens of thousands of parts, each of which fails rarely, so the run as a whole fails constantly. The numbers that describe this are simple and worth deriving from scratch: a per-GPU failure rate, multiplied by the fleet, gives the interval between stops; the checkpoint interval and the restart time give the work lost per stop; together they give effective training time, which is the fraction of wall-clock the run is actually learning. This page derives all of it from the one public dataset that reports it and shows how to size checkpointing so the run stays above 90%.🩺 Fleet Reliability & Observability
Advanced
Checkpointing and Resumption at ScaleA training checkpoint at frontier scale is terabytes of sharded optimizer state that must be written often enough to bound lost work and fast enough not to stall the job. The interval is a formula in the failure rate and the write cost, and asynchronous sharded writes are what turn it from a 15% tax into a 3% one.🕸️ Distributed Training🔒 Premium
