The article explores the evolution of large language model (LLM) serving, highlighting significant advancements from pre-2020 frameworks to the introduction of vLLM in 2023. It discusses the challenges of efficient memory management in LLM serving and how vLLM's PagedAttention technique revolutionizes the field by reducing memory wastage and enabling better utilization of GPU resources.
This post explores optimization techniques for the Key-Value (KV) cache in Large Language Models (LLMs) to enhance scalability and reduce memory footprint, covering methods like Grouped-query Attention, Sliding Window Attention, PagedAttention, and distributed KV cache across multiple GPUs.
