A deep dive into memory fragmentation, paged memory management, and why PagedAttention can deliver up to 24× higher throughput than conventional KV cache implementations. Every token you generate during LLM inference silently eats GPU memory. With traditional KV caching, a significant portion of that memory is wasted — never used, never reclaimed. vLLM’s PagedAttention changed that by borrowing a decades-old idea from operating systems. Here’s exactly how it works and why it matters. Table of Contents What Is a KV Cache and Why Does It Exist? The Problem: Traditional KV Cache and Memory Fragmentation Inspiration from OS Virtual Memory — The vLLM Insight PagedAttention: How It Works Memory Fragmentation: Before vs After Throughput Gains: Numbers and Benchmarks Trade-offs and Limitations Who Should Care About This? Key Takeaways Section 1 — What Is a KV Cache and Why Does It Exist?…