LLM Interview Series #5: What Is PagedAttention?

========================================================== Preparing for AI, ML, or LLM infrastructure interviews? Practice real interview-style questions here: https://interview.vizuara.ai/ ========================================================== "What is PagedAttention?" is a deep LLM inference interview question because it tests whether you understand not just attention, but how attention is served efficiently on real GPUs. Most candidates know that KV cache is important. But they often do not understand the memory management problem behind it: how KV cache grows during generation, why traditional allocation wastes GPU memory, and why paging becomes so powerful for high-throughput inference. In this video, we derive the idea on the blackboard step by step: GPU memory management for LLM inference Traditional KV cache management Why KV cache memory gets wasted or fragmented How PagedAttention works The step-by-step mechanism of pages and block tables Why this matters for serving many requests efficiently A brief preview of online softmax, which we will cover in the next video A strong interview answer should not just define PagedAttention. It should explain the problem, motivate the design, walk through the mechanism, and connect it back to real inference systems. The goal is to answer with depth, clarity, and passion, so the interviewer can see that you understand the system beyond the surface level. ========================================================== Preparing for AI, ML, or LLM infrastructure interviews? Practice real interview-style questions here: https://interview.vizuara.ai/ ========================================================== #LLMInterview #PagedAttention #KVCache #LLMInference #AIInfrastructure