How do you serve many fine-tuned model variants efficiently (multi-LoRA serving)?
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.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
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.
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.
