Systems, agents, retrieval, infrastructure

High-Performance Inference, Caching, and Cost Control

High-scale AI systems fail when teams treat inference as an opaque API call. Real production work requires understanding prefill versus decode latency, token throughput, context-window growth, KV-cache reuse, batching, speculative decoding, streaming, and how prompt shape directly affects both cost and responsiveness.

Deep dive summary

What this module actually teaches.

Passage 1High-scale AI systems fail when teams treat inference as an opaque API call. Real production work requires understanding prefill versus decode latency, token throughput, context-window growth, KV-cache reuse, batching, speculative decoding, streaming, and how prompt shape directly affects both cost and responsiveness.

Passage 2This module explains performance strategies across hosted APIs and self-hosted inference. Learners compare prompt caching, semantic caching, response memoization, request coalescing, tiered context windows, quantization tradeoffs, and autoscaling patterns for GPU-backed workloads. The focus is practical: knowing which lever improves p95 latency, which only lowers average cost, and which creates correctness risk.

Passage 3The module culminates in an inference budget model that connects product experience to infrastructure math. Engineers learn to reason about token ceilings, concurrency, queueing, cold starts, regional placement, and observability signals that reveal when an AI feature is becoming economically unsustainable.

Learning operating system

Module prerequisites, concepts, outcomes, and artifacts

Prerequisites

  • API integration experience
  • basic performance profiling
  • familiarity with async request handling

Key concepts

  • prefill latency
  • decode throughput
  • KV cache
  • prompt caching
  • semantic cache invalidation
  • speculative decoding
  • GPU autoscaling
  • cost observability

Target audience

  • Engineers responsible for latency, reliability, platform cost, or high-traffic AI product surfaces.

Outcomes

  • Build a practical latency and cost budget for model-backed features
  • Select caching strategies without corrupting personalized or safety-critical answers
  • Interpret inference telemetry and identify bottlenecks in request lifecycle traces

Artifacts

  • latency budget worksheet
  • cache strategy comparison
  • read-only performance tracing example

Code example

Real patterns, ready to copy into your editor.

A code reference card that frames this module's concepts in real syntax. Copy any example to study it or adapt it directly in your own editor.

Copy-ready snippets
curriculum/se-high-performance-inference-scaling.tsts
1type ModuleBrief = {2  track: string;3  difficulty: string;4  keyConcepts: string[];5  reviewGate: 'human-review-required';6};7 8export const moduleBrief: ModuleBrief = {9  track: 'Software Engineers',10  difficulty: 'Masterclass',11  keyConcepts: [12    'prefill latency',13    'decode throughput',14    'KV cache',15    'prompt caching',16    'semantic cache invalidation',17  ],18  reviewGate: 'human-review-required',19};