AI Engineering/Phase 10/Lesson 12

Inference Optimization

Two phases define LLM inference. Prefill processes your prompt in parallel -- compute-bound. Decode generates tokens one at a time -- memory-bound. Every optimization targets one or both.

Build 2 hr Python

Prerequisites: Phase 10, Lessons 01-08 (Transformer architecture, attention)

#Learning Objectives

Implement KV-cache to eliminate redundant computation during autoregressive token generation
Explain the prefill vs decode phases of LLM inference and why each has different bottlenecks (compute-bound vs memory-bound)
Implement continuous batching and PagedAttention concepts to maximize GPU utilization under concurrent requests
Compare inference optimization techniques (KV-cache, speculative decoding, flash attention) and their throughput/latency tradeoffs

Full Access lesson

Keep going. The rest of this lesson is one step away.

Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.

See plans Sign in

The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.