Why it matters: buyers often mistake a smaller training-memory requirement for a complete cost advantage.
A strong answer should cover: training versus inference, peak memory, context length, batch size, precision, runtime support, adapter management and repeated experiment costs. Ask for the exact experimental setting behind any numerical claim.
Follow-up: “Which parts of the QLoRA result can we reasonably transfer to our workload, and which need a new test?” The draft framing should distinguish feasibility from quality, serving economics and maintenance.
Interview status: Not yet interviewed.