The enterprise is entering a new computing era — one defined by the AI factory, where data, models and accelerated infrastructure come together to create intelligence at scale.
First introduced by Nvidia Corp.’s CEO, Jensen Huang, the AI factory concept represents a shift in how enterprises think about data centers. No longer just a collection of servers, storage and networking gear, it’s a center of excellence for enterprise AI — a place where organizations generate value from their proprietary data, according to Anne Hecht (pictured), senior director of product marketing for enterprise software at Nvidia.
Nvidia’s Anne Hecht discusses next-gen data centers with theCUBE.
“I like to describe the AI factory as … infrastructure; it’s the accelerated computing, networking, storage … you need models, there’s a layer of software in there,” she said. “But it doesn’t become a factory until you add that enterprise’s data, their IP that is embodied in that data, and you start to actually do inference and processing of that data and gain insights and create those tokens. That’s what we talk about: results and generating results based on that data.”
Hecht spoke with theCUBE’s John Furrier for theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event series, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed what the AI factory means for business transformation, how enterprises can build one, and why the future of computing is hybrid, intelligent and agentic.
The AI factory represents a new model for enterprise value
No longer just a collection of servers, storage and networking gear, the AI factory is a center of excellence for enterprise AI — a place where organizations generate value from their proprietary data. In Nvidia’s view, it is where data becomes tokens, which are usable outputs that represent knowledge and decision-making power.
“Enterprises that we’ve worked with, that maybe were early adopters and used accelerated computing for training, are realizing that with the onset of generative AI, agentic AI, inference and even the production workload needs to be on an accelerated system,” Hecht said.
Enterprises are now rethinking their long-standing reliance on x86-based CPU systems. The shift toward GPU-accelerated infrastructure allows organizations to handle both training and inference workloads for AI and agentic systems. Nvidia’s partners, such as Dell Technologies Inc., are helping enterprises evolve strategically — upgrading portions of their infrastructure to accommodate accelerated computing without necessarily increasing total spend, according to Hecht.
“It’s just spending your infrastructure budget in a slightly more strategic way and gradually building in room for accelerated computing so you can take advantage of these AI workloads,” she said. “We have a lot of broad offerings of accelerated computing. We have PCIe GPU systems that understand the modern data center and run x86 applications, so you can still do data processing, render basic workloads on these systems and start doing AI on these systems as well.”
As enterprises adopt AI, many discover fragmented use across departments — DIY chatbots, third-party tools and disconnected AI pilots that create security risks and inefficiencies. Nvidia’s solution is to help organizations standardize their AI development environments, Hecht noted.
“We have a whole ecosystem of partners that we’ve enabled with our libraries so they can expose through these developer environments that our third-party partners have created,” she said. “So, an AI practitioner can quickly create their own application or chatbot, even a deep research agent, but they can do it safely and then in a standard way as well.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event series:
Photo: SiliconANGLE
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