How PagedAttention resolves memory waste of LLM systems

The blog post discusses PagedAttention, a technique designed to tackle memory waste in large language models (LLMs) caused by inefficient management of the key-value (KV) cache. Traditional systems allocate memory based on maximum potential size, leading to fragmentation and wasted resources. PagedAttention offers a solution by dividing the KV cache into smaller, on-demand blocks to optimize memory usage, minimize internal and external fragmentation, and enhance throughput. This innovative approach allows for better GPU memory utilization and improved performance in concurrent workloads.