The New Rules of AI Inference Optimization and Future-Proofing

AI infrastructure is evolving faster than ever, reshaping how organizations approach performance, efficiency, and long-term architectural decisions. In this episode of Shared Everything, Nicole talks with Anat Heilper, Director of AI Architecture at VAST Data, about what optimization really means in 2026. Drawing on more than 14 years at Intel developing AI accelerators and large-scale training and inference systems, Anat explores the shifting balance between compute, memory, and data movement, the importance of KV cache reuse, and why inference optimization now extends far beyond the GPU. The conversation also looks ahead to the demands of agentic AI, multiple models, and emerging physical AI applications. Anat explains why scale, flexibility, and shared infrastructure are increasingly critical as enterprises move AI into production. With models and frameworks changing rapidly but infrastructure investments expected to last for years, the central challenge is clear: how to optimize for today's workloads while building systems ready for whatever comes next.