Design fault-tolerant checkpointing for a 1000-GPU training run. How do you minimize lost work on a failure?
On a large training run a node will drop, and the interesting question is not whether but how many GPU-hours vanish when it does. Checkpoint cadence, sharded writes, and quick restart settle that.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
On a large training run a node will drop, and the interesting question is not whether but how many GPU-hours vanish when it does. Checkpoint cadence, sharded writes, and quick restart settle that.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
