How do you compress the KV cache at inference, and what does each method trade off?
At long context it is the KV cache, not the weights, that saturates the GPU. What matters is naming the levers (quantization, token eviction, head sharing, low-rank) and the quality cost of each.
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 it is the KV cache, not the weights, that saturates the GPU. What matters is naming the levers (quantization, token eviction, head sharing, low-rank) and the quality cost of each.
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.
