Why GPT Hits a Memory Wall

Large Language Models were never meant to read entire books, and yet today, they can. So how do modern LLMs reason over tens or even hundreds of thousands of tokens without running out of memory? In this video, we dive into Infini-Attention, the architectural shift that allows Transformers to scale beyond fixed context windows. You’ll see why traditional self-attention breaks down at long lengths, why KV Cache alone is not enough, and how modern models rethink attention as memory management rather than brute-force comparison. We cover: - Why self-attention scales quadratically and hits…