profiling

LLM serving has its own metrics, and one latency number hides the real bottleneck. The signal is splitting prefill from decode and reading GPU utilization as a clue, not a verdict. Here is the diagnostic toolkit.

nvidia-smi showing 100% can hide that you are using a fraction of the hardware's real compute. The signal is MFU (useful FLOPs vs peak) and the gap between 'the GPU is busy' and 'the GPU is efficient'.

Paying for accelerators that idle half the time is the most common waste in ML training, and the instinct to add more GPUs makes it strictly worse. The interviewer wants the profiling discipline that finds what is starving them.

The collective communication in distributed training is pure overhead unless it runs while the GPU computes. Hiding it is the difference between 30% and 55% MFU. Here is how the overlap actually works and how you confirm it on a trace.

When all-reduce is the bottleneck, the cause is almost always a misconfigured path: traffic on the wrong link, a downed NIC, or a topology NCCL never discovered. Here is the systematic way to find the lost bandwidth.