LLM Inference Interview Questions #18 - The Log-Linear Inference Trap

Why using Best-of-N to boost agent performance quietly bankrupts your QPS budget, and the elite-level difference between buying benchmark points and shipping a viable AI product.

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Best-of-16 rollouts Critic reranker SWE-bench KV cache ORM (outcome reward model) unit tests Best-of-N sampling Critic reranker evaluation SWE-bench benchmark Inference cost scaling KV cache reuse Verifier loss in agent trajectories Latency and p99 performance Product vs. research tradeoffs

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