PagedAttention is a memory management technique used in transformer models to handle large sequences or datasets efficiently. It allows the system to page memory dynamically, similar to how computer systems swap pages in and out of main memory to manage resources. This is particularly useful when dealing with large contexts or sequence lengths that surpass the typical capabilities of traditional attention mechanisms. By leveraging PagedAttention, models can process more information without being limited by memory constraints, thus enhancing their performance on memory-intensive tasks.