HPE News from NVIDIA GTC DC 2025

So just really quickly, we'll just touch on some of the overall, um, overall, uh, press releases from yesterday. Obviously we will be focusing in on Veil, but just overall, so that we didn't leave anything behind. Of course, everybody knows about AI Factory and have had those conversations, whether it's, um, you know, really, and from an HPE perspective, how do we help with that blueprint for AI success?

All of the things that we're talking about, all of the challenges around, and things that we know around high failure rates, you know, data being scattered across silos, of course, as you understand from having gone through the ka waza overview, part of why we chose working with them in particular for this like approach at Veil, how do you look at that operational? I think some of where we were just talking about, you know, we look at and we see like the really cool sexy end, end thing that AI can do and that it gets lost sometimes that somebody has to operate it and that they're gonna have to life cycle manage it, and they're gonna have to look at over time, how do you keep that AI stack up to date? Because as we all know with Nvidia moving at the speed of light, so do the AI stacks and so does all of the technology around it, and then how do you have kind of that unified environment?

And so from a, from an HPE perspective, whether you're talking about kind of that sovereign AI factory that goes out to thousands of GPUs, like we're talking about with several of the DE and installations, or whether you're talking about private cloud for ai, where you can have it all kind of pre-built and pre-integrated, uh, from HPE and Nvidia together, being able to have those outcomes and be very outcome focused on how we're talking, I then bring in the Unleash AI on top of that to not just talk about outcomes from the technology perspective, but outcomes from a business use case perspective. I was gonna say you have the clicker. Yeah, the, uh, so really quickly, we did some announcements around, uh, private cloud AI being able to go out to every scale, uh, being able to talk about having RTX, uh, 6,000 pro, uh, available in, in the small solution.

Excited for that because of the, um, capability and flexibility it gives us in supporting different workloads as well as now moving to air gapped management, uh, so that this lifecycle managed, pre-integrated AI stack can, can also go out and be, be now installed on dark sites or air gapped environments where it's necessary, particularly when we think about public sector, that is frequently the case. And then also our A NPS can help. We announced with SHI, uh, at, uh, discover a, uh, dig a digital, um, concierge now kind of our A NPS, taking that as a repeatable, uh, offering essentially associated with private cloud ai.

So excited to be able to build upon these type of platforms that make it simpler for our customers to be able to manage these things on a daily basis. But obviously today we're gonna lean in and Sure. How much of Juniper's, uh, AI technology is Involved in this private AI cloud.

Uh, that is something that you'll see us be, um, be integrating in as the acquisition is now closed. Okay. So at this point in time, you don't have anything, Uh, there are, um, let me get back to you and let me just take the note on the specifics of the networking piece to it.

Okay, thank you. Um, the, um, so on the, so obviously today we're focusing on the, um, on the town of Vail to be able to, uh, you know, kind of touch on all of the things that we've been doing. We're going to focus on the, on several different use cases.

We do have a demo associated with each, so you can see how it's actually working. And, uh, and so we are excited that we've been able to be public with the town of Vail and to be able to continue to grow out this partnership together in what we're able to achieve and with all of our other Unleash AI partners. You mentioned four specific use, um, uh, outcomes use cases, but four specific outcomes, I guess.

What are the outcomes that they're seeing? I mean, do you have details, I think details next slide on it actually. Yeah, I was like, I will pay you.

Thank you. Nice Set. Nice setup.

Well played. So this is, uh, you know, this is just really touching on the challenge that we all see whether, regardless of what horrific number that you see is that 85% is at 90%, whatever that percentage is, that's not going from pilot to production. Um, one of my passions in life is how do we help people avoid that chasm of despair and uh, and be able to, to cross that and actually get, to be able to get the value out of the ai.

You know, being able to show that you have a pilot is great. Being able to use it every day is actually meaningful and, and transformational for the business. So whether it's that, so how do we focus on that time to value?

How do we focus on getting around the complexity and how do we focus on the security and governance? We've talked a lot about what common Kami WASA can help bring to that table. Obviously building out on the infrastructure, being able to have Kami Waza being able to, um, run within the, um, town of Vail's own, um, data center connected directly to their data.

We start to bring down some of the complexity that you have when you start to bring this together and actually treating it like a true solution and platform. Not a whole bunch of things just running together, integrated approach, accelerated computing and secure infrastructure. So this is what the, what the basic stack looks like.

And then we'll start to go into the specific use cases. Kami was essentially, I lovingly refer to it as the agentic backend, but obviously it does a great deal more than that. Being able to have those agents being able to bring those workflows and the con and the contextual understanding of the alerts that it's getting, particularly when we get into fire detection, uh, it starts to bring some really interesting, uh, capabilities to that vision detection.

Um, because smoke looks a lot like fog, which, which looks like, which looks a lot like a backyard barbecue. So, you know, how do you, and when do you actually deploy people? And, and how do you manage that?

That's gonna be something that is impactful, not just from a fire safety perspective, but also from a personnel safety perspective. Because if you don't actually have good detection, you do have to deploy people at one o'clock in the morning into the depths of the woods to try to go see if there's a fire or not. Uh, so we have, so working with Kami Waza to provide all of those pieces integrating on top of that pro hoc, which gives us that video, um, restoration, um, capabilities, the Vidia, which brings up video intelligence and then black Shark looking at geospatial.

When you start to think about fire prevention, um, and just touching on fire, for those of us who are accustomed to living out west, that is a really big deal. Um, out West. I'm saying that Vail is using this ENT solution for fire.

We're Gonna show you a use case and expansion that we're doing with the Mon. Yeah, so the, uh, so, and one of our other passions is particularly when, um, Vail has a big, uh, commitment to sustainability. Uh, all of the, uh, their energy and for their data center is actually from renewables.

And so we're also able to reuse all of the cameras that they have installed in their town and now just apply intelligence to that without necessarily having to go in with a very costly and very complex camera implementation or leveraging infrastructure that they already have. And just bringing intelligence to that. And then we're working with SHI to, uh, for assistance with the deployment, being able to really having that trusted partnership around, uh, integration around being able to scale it out, and of course, ensuring that we can support it ongoing as we continue to grow this both at Veil and at other, um, areas.

This.