You quantized your serving model to FP8 and throughput doubled. How do you prove accuracy held?
FP8 serving is fast and usually fine, until it quietly degrades on the one workload your benchmark did not cover. What counts is knowing what FP8 breaks and how to validate it. Here is the recipe.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
FP8 serving is fast and usually fine, until it quietly degrades on the one workload your benchmark did not cover. What counts is knowing what FP8 breaks and how to validate it. Here is the recipe.
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.
