How do you quantize or compress the KV cache, and why does it matter for long-context serving?
At long context the KV cache, not the weights, dominates GPU memory and decode bandwidth. What counts is spotting it as the binding constraint and naming the levers that shrink it without wrecking quality.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
At long context the KV cache, not the weights, dominates GPU memory and decode bandwidth. What counts is spotting it as the binding constraint and naming the levers that shrink it without wrecking quality.
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.
