Challenges with Ultra-low Latency LLM Inference at Scale | Haytham Abuelfutuh

Haytham Abuelfutuh, co-founder and CTO at Union, discusses the complexities of deploying Large Language Models (LLMs) at scale, drawing a parallel to the unexpected challenges of DIY projects. He argues that while running LLMs locally may seem straightforward, production deployment requires specialized infrastructure to achieve optimal performance. Abuelfutuh details several key optimizations, including streamlining container image loading, direct GPU memory access, and splitting prefill and decode operations across GPUs. He emphasizes the importance of smart routing and caching strategies for horizontally scaling LLM services across regions, advocating for systems innovation to democratize LLM deployment and enable companies to compete with optimized API providers.