An AI stack: from scaling AI workloads to evaluating LLMs

Large language models (LLMs) have taken the world by storm—enabling new applications, intensifying GPU shortages, and raising concerns about the accuracy of their outputs. In this talk, I will present several projects I have worked on to address these challenges. Specifically, I will focus on: (i) Ray, a distributed framework for scaling AI workloads; (ii) vLLM and SGLang, two high-throughput inference engines for LLMs; and (iii) Chatbot Arena, a platform for accurate LLM benchmarking. I will conclude with key lessons learned and outline directions for future research.