LLM Inference Optimization Part 1 — Attention Mechanism Deep Dive

LLM Inference Optimization Part 1 — Attention Mechanism Deep Dive

When you deploy an LLM to a production service, the first wall you hit is inference speed and memory. No matter how good the model is, it's useless if it's slow and expensive. In this series, we dissect the core bottlenecks of LLM inference one by one and cover practical optimization techniques with code.

In Part 1, we implement the Attention mechanism from scratch — the starting point of all optimizations — and compare the evolution from MHA to GQA to MQA directly in code.

Self-Attention — Implementing from Scratch

Basic Structure