Reasoning-Model Serving

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A reasoning model can spend 5 tokens or 5,000 on the same prompt — serving it well is mostly about taming that uncertainty.

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Key Insight

This project serves a long-chain-of-thought reasoning model, measures how wildly its output length swings from one request to the next, and then adds a thinking-budget knob that caps how long the model is allowed to think before it must answer. Watching the output-length distribution makes the core problem visible: unlike a chat model, you cannot guess how much work a single request will be.

Why This Matters

Output-length variance is what breaks naive capacity planning for reasoning models: a handful of hard prompts can each generate 10× the tokens of a normal reply, blowing up latency and cost for everyone sharing the GPU. A thinking budget gives you a direct dial to trade accuracy for predictable cost and tail latency — the single most useful control when serving this class of model.