gpu memory

Interviewers here want concrete figures, not 'grab a bigger GPU.' Weight memory stays constant, the KV cache scales with traffic, and the order you pull levers in settles the outcome. This walks through the napkin math and the serving stack.

The interviewer is checking that you understand how a model too large for any one GPU still gets trained, and can run the optimizer-state memory math that justifies sharding. This covers the parallelism taxonomy and the 16-bytes-per-parameter figure.

A staple at hardware-aware teams. What they look for is grasping that attention's cost is memory traffic rather than FLOPs, and that FlashAttention is an exact, IO-aware reordering rather than an approximation. This is the answer that shows you reason about the memory hierarchy.

OOM is the wall you hit most often in deep learning, and 'buy a bigger GPU' is the weakest reply. What counts is naming the memory consumers, knowing which one dominates, and pairing the right lever with it.

Data, tensor, and pipeline parallelism each leave a single sequence's activations on one device, so training past 200k tokens hits a wall none of them can clear. The fourth axis shards the sequence itself, and the interview turns on the communication math.