Cache Management in Large Language Models

Master the Memory Architecture That Powers Modern LLM Serving — From Transformer Foundations and PagedAttention to Disaggregated Clusters and Production Engines.

Preview: https://drive.google.com/file/d/12cUngq6P0yBTVra7W60Df28kxttRa4no/view

Github: https://github.com/lamhotsiagian/llm-cache-management-code

Overview

In modern artificial intelligence, the most expensive operation in production is not model training—it is autoregressive text generation at scale. Serving models like GPT-4, Llama 3, DeepSeek-V3, and Qwen 2.5 is rarely limited by compute FLOPs. Instead, performance is severely constrained by the capacity and bandwidth of High-Bandwidth Memory (HBM).