LLM Inference Optimization Part 1 — Attention Mechanism Deep Dive
Build Self-Attention from scratch. Compare MHA → GQA → MQA evolution in code. KV Cache mechanics and Prefill vs Decode analysis.
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
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LLM Inference Optimization Part 4 — Production Serving
Production deployment with vLLM and TGI. Continuous Batching, Speculative Decoding, memory budget design, and throughput benchmarks.
LLM Inference Optimization Part 3 — Sparse Attention in Practice
Sliding Window, Sink Attention, DeepSeek DSA, IndexCache, and Nvidia DMS. From dynamic token selection to Needle-in-a-Haystack evaluation.
LLM Inference Optimization Part 2 — KV Cache Optimization
KV Cache quantization (int8/int4), PCA compression (KVTC), and PagedAttention (vLLM). Hands-on memory reduction code and scenario-based configuration guide.