vLLM in 2026: Challenges and Optimizations

Abstract

As LLMs grow in size, context length, and architectural complexity, vLLM must evolve to meet new performance and scalability challenges. This talk presents key improvements in vLLM's core architecture and highlights major optimizations in KV cache management and GPU kernels. Furthermore, this talk covers the latest updates related to vLLM community, large scale serving, and across hardware effort.