GPU clusters sit idle and budgets balloon when storage can't keep pace with how fast AI workloads are scaling. In this episode, Cameron Bahar, GM of Scale AI at Everpure, explores how storage architecture must shift from rigid, single-purpose systems to flexible, cloud-like models built for constant change. The conversation covers the move from training to inference and agentic workloads, the case for separating metadata from data scaling, and what always-on, multi-tenant infrastructure looks like in practice. This episode is sponsored by <a href= "https://www.everpuredata.com/">Everpure</a>. Emerj works with a select group of AI vendors to reach Fortune 500 decision makers through research, media, and direct access. If you want to be considered, download our media kit at <a href= "http://emerj.com/AD1">http://emerj.com/AD1</a>

<div>GPU clusters sit idle and budgets balloon when storage can't keep pace with how fast AI workloads are scaling. In this episode, Cameron Bahar, GM of Scale AI at Everpure, explores how storage architecture must shift from rigid, single-purpose systems to flexible, cloud-like models built for constant change. The conversation covers the move from training to inference and agentic workloads, the case for separating metadata from data scaling, and what always-on, multi-tenant infrastructure looks like in practice.</div> <div> </div> <div>This episode is sponsored by <a href= "https://www.everpuredata.com/">Everpure</a>.</div> <div> </div> <div>Emerj works with a select group of AI vendors to reach Fortune 500 decision makers through research, media, and direct access. If you want to be considered, download our media kit at <a href= "http://emerj.com/AD1">http://emerj.com/AD1</a></div>

Inside the Performance Constraints of Modern AI Workflows - with Cameron Bahar of Everpure

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