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LLM Inference Optimization & Serving

Where inference cost actually goes, and how to claw it back: continuous batching and paged attention, quantization and distillation for serving, and the throughput-latency-SLO trade-offs behind a production serving stack.

1The Inference Cost ModelInference cost is the price of turning a prompt into an answer, shaped by tokens, model choice, latency, context, and tooling.Advanced

2Continuous Batching & Paged AttentionHow modern model servers keep expensive GPUs busy while handling many uneven requests at once.Advanced

3Quantization & Distillation for ServingHow smaller numbers and smaller models can make inference cheaper and faster without losing the quality users notice.Advanced

4Throughput, Latency & SLO Trade-offsA first-principles guide to how much work a system can do, how long work takes, and what promises are worth making.Advanced

5Serving Stacks & AutoscalingA practical guide to how software is served to users, what makes up a serving stack, and how autoscaling keeps capacity aligned with demand.Advanced