Paged Attention Explained: The Secret Behind vLLM’s Speed

Paged Attention is one of the key innovations behind fast LLM inference systems like vLLM. Instead of storing the KV cache as one large contiguous block of memory, paged attention divides memory into smaller reusable blocks, dramatically improving GPU utilization and throughput. In this video, we break down how paged attention works and why it’s critical for modern large language model systems. If you're learning about LLM inference, transformer optimization, or AI systems engineering, this concept is essential for understanding how modern AI systems scale efficiently. #PagedAttention #vLLM…