In this interview from the Nvidia GTC AI Conference and Expo, Andy Pernsteiner, field chief technology officer of VAST Data, joins theCUBE's John Furrier to discuss why storage has emerged as the critical bottleneck — and enabler — for AI factories operating at scale. Pernsteiner explains how VAST Data's multi-year partnership with NVIDIA, from the early GPUDirect Storage collaboration to the newly released Dynamo inference engine, is reshaping how GPUs consume and offload data. By moving previously computed KV cache attention data off expensive GPU memory and onto storage, VAST is delivering a 10X improvement in inference capability from a single GPU server — freeing cycles for active sessions and the rising tide of agentic workloads. The conversation also explores how VAST Data is lowering the barrier to enterprise AI adoption through open-source foundation stacks built on NVIDIA blueprints, spanning document ingestion pipelines to video search and summarization. Pernsteiner highlights a recurring pattern among large enterprises: AI initiatives stalling in pilot phase because roll-your-own RAG systems cannot meet organizational security and multi-tenancy requirements at scale. VAST's integrated policy model addresses that gap, enabling regulated organizations to move from experimentation to production. The discussion also touches on the economics of intelligent tiering across GPU classes and the shift toward disaggregated infrastructure, where VAST's global namespace and data engine orchestrate compute jobs closest to the data. From tripling exabyte deployments year over year to preparing for a world where agents outnumber users by orders of magnitude, Pernsteiner provides a practical roadmap for building AI infrastructure that is fast, secure and ready to scale.