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The Serving Optimization Waterfall

Tune an LLM deployment one change at a time against one fixed workload, in an order that follows the binding constraint. A measured run went from 789 to 6,568 output tokens per second, and the two biggest wins were not the knobs people usually reach for first.

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RELATED CONCEPTS

Serving Benchmarks That Do Not Lie Prefix Caching and KV Reuse Continuous Batching Chunked Prefill Latency Metrics: TTFT, TPOT and Goodput Cost per Million Tokens

LESSONS THAT TEACH THIS

Inference EngineeringTune in the order the bottleneck moves, and log a prediction for every step26 min→Inference EngineeringCapstone lab: deploy one model, tune it, run a batch and hand it off360 min→

PRACTICE THIS IN REAL QUESTIONS

Open-Weights Models & Serving EnginesSet max-model-len and max-num-seqs for a chat product from first principles.→Open-Weights Models & Serving EnginesPrefix caching cut your benchmark's latency in half. Why might production see none of that?→Open-Weights Models & Serving EnginesYou are serving an agent product with long shared prompts. vLLM or SGLang?→Open-Weights Models & Serving EnginesThe engine logs say CUDA graphs are disabled for your deployment. Does it matter?→Napkin Math, Cost & CapacityWhat does it cost per million output tokens to serve a 70B model on eight H100s?→Open-Weights Models & Serving EnginesYour model now serves 6,568 tokens per second. What does a million tokens cost, and what do you hand the gateway and eval teams?→