A strong answer. “The timing is the biggest clue. A failure at a consistent elapsed time rather than a consistent step count points at something thermal or something that accumulates, not at a code path — code bugs tend to fail at the same iteration, not the same hour.

So I'd check, in this order. Thermal. Pull per-GPU temperature and clock telemetry across the run and look for throttling or a card drifting hot before the failure. A partially blocked cooling loop or a failing pump produces exactly this signature. A single degraded node. In a synchronous job the slowest rank sets the pace and a marginal card can hold the whole run until a collective times out; I'd look at per-rank step times, and the outlier is usually obvious once you plot it. The fabric. NCCL timeouts are frequently a link flapping rather than a compute problem, so I'd check error counters and retransmits on the interconnect over the same window. Memory. ECC error accumulation on a specific card, which is the classic slow-building failure. Then the customer's code — a memory leak or a checkpoint routine that only runs every N hours.

Practically I'd also do something cheap immediately: pin the job away from any node that has ever reported a correctable ECC error or a thermal event, and see whether the failure moves. That is a five-minute experiment that discriminates between hardware and software, which is the fork the whole investigation turns on.

And I'd want checkpointing frequency reviewed regardless. Failing at hour nine costs nine hours only if the last checkpoint was at hour zero.”