efficiency

Both are conditional compute, but one scales parameters and the other scales depth. The signal is knowing which axis each routes along and why that shifts the FLOP story. Here is the sharp version.

Distillation gives you most of a frontier model's quality for a fraction of the serving cost. The signal is naming the three LLM-specific variants and knowing which one holds up against a closed API. Here is the answer.

A single trained embedding you can cut to any length and still use. The signal is the nested-prefix training objective and the coarse-to-fine retrieval win it enables at scale. Here is the answer.

nvidia-smi reading 100% can mask the fact that you are tapping only a fraction of the hardware's real compute. What matters is MFU (useful FLOPs vs peak) and the gap between 'the GPU is busy' and 'the GPU is efficient'.

Running one full fine-tuned model per customer grows GPU count linearly and drains the budget quickly. A method exists to pack hundreds of variants onto a single GPU without sacrificing batching. The interviewer wants to hear how.