December 16, 2025
Inside the AI Factory: NVIDIA's Playbook for Building AI at Scale
NVIDIA's John Gentry and WWT's Derek Elbert outline how AI factories are evolving from isolated GPU clusters into flexible, multi-tiered, distributed systems — and why data strategy, security and ecosystem partnerships now determine whether enterprises can keep pace.
AI factories — once a niche academic concept — are fast becoming the organizing architecture for enterprise AI.
As NVIDIA's John Gentry explains, the factory model resonates because it treats AI development like industrial production: power and data flow in, intelligence flows out. But the metaphor belies a growing complexity. Enterprises must now combine compute, networking, storage, workflow orchestration and multi-tenant management into a coherent, secure system that can serve many business units simultaneously.
Data, long viewed as an asset, has become the chokepoint. Companies need industrial-grade data pipelines capable of classifying, curating and staging petabytes for training and fine-tuning. At the same time, rapid innovation cycles — new GPUs every 12 months — push IT teams to build for flexibility and avoid lock-in.
The next frontier, Gentry said, is distributed inference and agentic AI, where models run at the edge while core factories continue to manufacture new intelligence. For enterprises, the mandate is clear: architect for portability, scalability and continuous evolution.
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AI deployment and adoption are complex. The AI Proving Ground Podcast makes them actionable. Join top experts, IT leaders and innovators as we explore AI's toughest challenges, uncover real-world case studies and reveal practical insights that drive AI ROI. From strategy to execution, we break down what works (and what doesn't) in enterprise AI. New episodes are available every week.