An AI factory, at any scale, is a system where you put in data and energy and, as if by magic, intelligence, tokens, and business outcomes come out — structured across five layers: energy, chips, infrastructure, models, and applications.

0:00"AI factory" is one of those phrases that seems to have appeared overnight. Vendors are saying it, enterprises are asking about it, governments are funding it. But depending on who you talk to, it can be anything from a GPU cluster with better branding to a completely new operating model for building and running AI at scale. So which is it? I can't think of two better people to help

0:20me get to the bottom of this question than Thierry Pienaar, CTO of AI and HPC at HPE, and Kaushik Shirhatti, VP of AI Factory at NVIDIA, who are here with me for a special edition of the Twimel Briefing Room live from HPE Discover twenty twenty-six. When you talk about an AI factory at scale, like what are we actually talking about? at a basic fundamental level, what we think about an AI factory, doesn't

0:46matter if it's big or small, is when you put data in, you put energy in, and boom, like magic, intelligence, tokens, business outcomes come out. The way HPE and NVIDIA thinks about this AI factory is essentially across five layers. So you think about the energy You think about the chips, so all the innovation that NVIDIA brings in, all the innovation that HPE brings in. You think about the infrastructure,

1:16w- you know, all the hardware, all the-- where all the different options we give customers to go hosted. And then you start thinking about different models, and ultimately, the applications. Thierry, how does that jive with the way you think about it? HPE's historically had a private cloud AI offering. You've got a AI factory offering. Are those the same? Are they different? If you think about the scale, the

1:41maturity of different customers, we have customers that need sixteen GPUs, and they need to do generative AI, and they're just getting onto their journey. We have other customers that have been doing HPC for twenty, thirty years and understand traditional HPC environments. Now they're moving into AI and using AI to transform HPC, but they're highly sophisticated. So the concept of AI factory, can move along this medium, in

2:03terms of need and sophistication. if we look back a couple of years, training huge models was the big driver, right? the frontier labs, would largely work with the hyperscalers and contract, many s- servers and GPUs to do training workloads. More recently, though, inferences kinda come to the forefront as the driver for, enterprise and really, creating these new opportunities for neo clouds and others. talk a little bit about what you're seeing