What is vLLM and why is PagedAttention needed?

vLLM is a high-performance engine for inference of large language models (LLM), optimized for efficient resource use and scalability. It allows running large models with less memory and time.

PagedAttention is an attention optimization technique used in vLLM. It divides long sequences into pages (chunks) to reduce memory requirements during attention calculation while maintaining model quality. This is especially useful for processing very long contexts, where traditional attention mechanisms become too resource-intensive.

Thus, PagedAttention helps vLLM efficiently scale inference of large models, reducing memory consumption and accelerating computations without significant quality loss.