 We're back, you're watching theCUBE's coverage of Red Hat Summit 2022 here in the Seaport in Boston. I'm Dave Vellante with my co-host, Paul Gillan. Justin Boitano is here. He's the Vice President of Enterprise and Edge Computing at NVIDIA. Maybe you've heard of them. And Tushar Khattarki, who's the Director of Product Management at Red Hat. Gentlemen, welcome to theCUBE. Good to see you. Thank you. Great to be here. Justin, you had keynote this morning. You got interviewed and shared your thoughts on AI. You encouraged people to think bigger on AI. I know it's kind of self-serving, but why? Why should we think bigger? When you think of AI, it's a monumental change that's going to affect every industry. And so when we think of AI, you step back, you're challenging companies to build intelligence and AI factories and factories that can produce intelligence. And so it forces you to rethink how you build data centers, how you build applications. It's a very data-centric process where you're bringing in an exponential amount of data. You have to label that data. You got to train a model. You got to test the model to make sure that it's accurate and delivers business value. Then you push it into production. It's going to generate more data. And you kind of work through that cycle over and over and over. So just as Red Hat talks about CICD of applications, we're talking about CICD of the AI model itself, right? So it becomes a continuous improvement of AI models in production, which is a big business transformation. Yeah, Chris Wright was talking about basically taking your typical application development, pipeline and lifecycle and apply that type of thinking to AI. I was saying those two worlds have to come together actually, the application stack and the data stack, including AI, need to come together. What's the role of Red Hat? What's your sort of posture on AI? Where do you fit with OpenShift? Yeah, so we're very excited about AI. I mean, a lot of our customers obviously are looking to take that data and make meaning out of it. Using AI is definitely a big important tool. And OpenShift and our approach to open a hybrid cloud really forms a successful platform to base all your AI journey on with partners such as NVIDIA, whom we are working very closely with. And so the idea really is, as Justin was saying, the end-to-end when you think about life of a model, you get data, you mine that data, you create models, you deploy it into production, that whole thing, what we call CI CD, as you were saying, DevOps, DevSecOps, and the hybrid cloud that Red has been talking about with OpenShift as the center forms a good basis for that. So, somebody said the other day, I'm going to ask you, is NVIDIA a hardware company or a software company? We're a company that people know for our hardware, but predominantly now we're a software company. And that's what we were on stage talking about. Ultimately, a lot of these customers know that they've got to embark on this journey to apply AI, to transform their business with it. It's such a big competitive advantage going into the next decade. And so the faster they get ahead of it, the more they're going to win, right? But some of them, they're just not really sure how to get going. And so a lot of this is, we want to lower the barrier to entry. We built this program, we call Launchpad, to basically make it so they get instant access to the servers, the AI servers, with OpenShift, with the MLOps tooling, with example applications. And then we walk them through examples like, how do you build a chatbot? How do you build a vision system for quality control? How do you build a price recommendation model? And they can do hands-on labs and walk out of Launchpad with all the software they need, I'll say the blueprint for building their application. They've got a way to have the software and containers supported in production. And they know the blueprint for the infrastructure and operating that at scale with OpenShift. So more and more, to come back to your question, we're focused on the software layers and making that easy to help either enterprises build their apps or work with our ecosystem and developers to buy solutions off the shelf. Now on the hardware side though, I mean, clearly NVIDIA has prospered on the backs of GPUs as the engines of AI development. Is that how it's going to be for the foreseeable future? Will GPUs continue to be core to building and training AI models? Or do you see something more specific to AI workloads? Yeah, I mean, that's a good question. So I think for the next decade, well, plus forever, we're going to always monetize hardware. It's a big market opportunity. Jensen talks about a $100 billion market opportunity for NVIDIA just on hardware. It's probably another $100 billion opportunity on the software. So the reality is we're getting going on the software side, so it's still kind of our early days, but that's a big area of growth for us in the future and we're making big investments in that area. On the hardware side and in the data center, the reality is since Moore's law has ended, acceleration is really the thing that's going to advance all data centers. So I think in the future, every server will have GPUs, every server will have GPUs, and we can talk a bit about what GPUs are. And so there's really kind of three primary processors that have to be there to form the foundation of the enterprise data center in the future. Did you bring up an interesting point about GPUs and MPUs and sort of the variations of GPUs that are coming about? Do you see that those different PU types continuing to proliferate? Oh, absolutely. I mean, we've done a bunch of work with Red Hat and we've got a, I'll say a beta of OpenShift 410 that now supports GPUs as the, I'll call it the control plane, like software-defined networking offload in the data center. So it takes all the software-defined networking off of CPUs. When everybody talks about, I'll call it software-defined networking in core data centers, you can think of that as just a CPU tax up to this point. So what's nice is that's all moving over to GPUs to offload and isolate it from the x86 cores. It increases the security of your data center. It improves the throughput of your data center. And so, yeah, GPUs, we see everybody copying that model. And to give credit where credit is due, I think companies like AWS, they bought Annapurna. They turned it into Nitro, which is the foundation of their data centers. And everybody wants the, I'll call it the democratized version of that to run their data centers. And so every financial institution and bank around the world sees the value of this technology but running in their data center. Everybody needs a Nitro, I've written about it. It's an Annapurna acquisition, $350 million. I mean, peanuts in the grand scheme of things. It's interesting, you said Moore's Law is dead. You know, we have that conversation all the time. Pat Kelsinger promised that Moore's Law is alive and well. But the interesting thing is when you look at the numbers, that's, you know, Moore's Law, we all know it doubling of the transistor densities every 18 to 24 months. Let's say that that promise that he made is true. What I think the industry maybe doesn't appreciate, I'm sure you do, we need an Nvidia. When you combine what you were just saying, the CPU, the GPU, Paul, the NPU, accelerators, all the XPUs, you're talking about, I mean, look at Apple with the M1. I mean, 6X in 15 months versus doubling every 18 to 24. The A15 is probably averaging over the last five years, 110% performance improvement each year versus the historical Moore's Law, which is 40%, it's probably down to the low 30s now. So it's a completely different world that we're entering now, and the new applications are going to be developed on these capabilities. It's just not your general purpose market anymore. From an application development standpoint, what does that mean to the world? Yeah, I mean, yeah, it's a great point. I mean, from an application, I mean, first of all, I mean, just talk about AI. I mean, they are all very compute intensive, they're data intensive, and to move data for those so much and to compute and crunch those numbers, I mean, I'd say you need all the PUs that you mentioned in the world. And then, and also there are other concerns that will augment that, right? Like, we want to, you know, security is so important, so we want to secure everything. Cryptography is going to take off to new levels, you know, that we are talking about, for example, in the case of deep use, we are talking about, you know, can that be used to offload your encryption and firewalling and so on and so forth. So I think there are a lot of opportunities from even from an application point of view to take advantage of this capacity. So I'd say we've never run out of the need for PUs. So is OpenShift the layer that's going to simplify all that for the developer? That's right, you know, so one of the things that we worked with NVIDIA, and in fact was, we developed this concept of an operator for GPUs, but you can use that pattern for any of the PUs. And so the idea really is that how do you, yeah, it's a new term. Yeah, it's a new term. XPUs. XPUs, yeah. And so that pattern becomes very easy for open GPUs or any other such accelerators to be easily added as a capacity. And for the Kubernetes scheduler to understand that there is that capacity, so that an application which says that I want to run on a GPU, then it becomes very easy for it to run on that GPU. And so that's the abstraction to your point about how we are making that happen. And to add to this, so the operator model, it's this, you know, open source model that does the orchestration. So Kubernetes will say, oh, there's a GPU in that node. Let me run the operator and it installs our entire runtime. And our runtime now, you know, it's got a big configuration utility, it's got the driver, it's got, you know, telemetry and metering of the actual GPU in the workload. You know, along with a bunch of other components, right? They get installed in that Kubernetes cluster. So instead of somebody trying to chase down all the little pieces and parts, it just happens automatically in seconds. We've extended the operator model to GPUs and networking cards as well. And we have all of those in the operator hub. So for somebody that running OpenShift in their data centers, it's really simple to, you know, turn on node feature discovery. You point to the operators. And when you see new accelerated nodes, the entire runtime's automatically installed for you. So it really makes, you know, GPUs and our networking, our advanced networking capabilities, really first class citizens in the data center. So you can kind of connect the dots and see how NVIDIA and the Red Hat partnership are sort of aiming at the enterprise. I mean, NVIDIA, if you've obviously got the AI piece, I always thought maybe 25% of the compute cycles in the data center were wasted doing on storage off loads or networking off loads security. I think Jensen says it's 30%. Probably a better number than I have. But so now you're seeing a lot of new innovation in new hardware devices that are attacking that with alternative processors. And then my question is, what about the edge? Is that a blue field out at the edge? What does that look like to NVIDIA and where does OpenShift play? Yeah, so when we talk about the edge, we're always going to start talking about like which edge are we talking about? Because it's everything outside the core data center. I mean, some of the trends that we see with regard to the edge is, you know, when you get to the far edge, it's single nodes. You don't have the guards, gates and guns protection of the data center. So you start having to worry about physical security with the hardware so you can imagine there's really stringent requirements on protecting the intellectual property of the AI model itself. You spend millions of dollars to build it. If I push that out to an edge data center, how do I make sure that that's fully protected? And that's the area that we just announced a new processor that we call Hopper H100. It supports confidential computing so that you can basically ensure that model's always encrypted in system memory across the PCI bus to the GPU and it's run in a confidential way on the GPU. So you're protecting your data, which is your model, plus the data flowing through it, you know, in transit, while it's stored and then in use. So that really adds to that edge security model. What do they ask you about the cloud? Correct me if I'm wrong, but it seems to me that AI workloads have been slower than most to make their way to the cloud. There's a lot of concerns about data transfer capacity and even cost. Do you see that, first of all, do you agree with that? And secondly, is that going to change in the short term? Yeah, so I think there's different classes of problems. So we'll take, there's some companies where their data's generated in the cloud and we see a ton of, I'll say, adoption of AI by cloud service providers, right? Recommendation engines, translation engines, conversational AI services that all the clouds are building, that's all our processors. There's also problems that enterprises have now I'm trying to take some of these automation capabilities but I'm trying to create an intelligent factory where I want to merge kind of AI with the physical world. And that really has to run at the edge because there's too much data being generated by cameras to bring that all the way back into the cloud. So I think we're seeing mass adoption in the cloud today. I think at the edge, a lot of businesses are trying to understand, how do I deploy that reliably and securely and scale it? So I do think there's different problems that are going to run in different places and ultimately we want to help anybody apply AI where their business is generating the data. So obviously very memory intensive applications as well. We've seen you, Nvidia architecturally, kind of move away from the traditional x86 approach, take better advantage of memories where obviously you have relationships with ARM. So you've got a very diverse set of capabilities and then all these other components that come in, it used to just be a kind of x86 centric world and now it's all these other supporting components to support these new applications. How should we think about the future? Yeah, I mean, it's very exciting for sure, right? Like, you know, the future, the data is out there at the edge. The data can be in the data center and so we are trying to weave a hybrid cloud footprint that spans that. I mean, you heard Paul Commyer talk about it, but you know, we've talked about it for some time now. And so the paradigm really is that it beat an application and when I say application, it could be even an AI model as a service. You can think about that as an application. How does an application span that entire paradigm from the core to the edge and beyond where the future is and of course, there's a lot of technical challenges for us to get there and I think that partnerships like this are going to help us and our customers to get there. So the world is very exciting. You know, I'm very bullish on how this will play out. Justin, we'll give you the last word with closing thoughts. Well, you know, I think a lot of this is, like I said, it's how do we reduce the complexity for enterprises to get started, which is why Launchpad is so fundamental. It gives, you know, access to the entire stack instantly with like hands-on curated labs for both IT and data scientists. So they can, again, walk out with the blueprints they need to set this up and, you know, start on a successful AI journey. Just a position, is Launchpad more of a sandbox, more of a train, more of a school or more of an actual development environment? Yeah, think of it as, again, it's really for trial. Like hands-on labs to help people learn all the foundational skills they need to like build an AI practice and get it into production. And again, it's like you don't need to go champion to your executive team that you need access to expensive infrastructure and, you know, and bring in Red Hat to set up OpenShift. You just, everything's there for you, so you can instantly get started. Do kind of a pilot project and then use that to explain to your executive team everything that you need to then go do to get this into production and drive business value for the company. All right, great stuff, guys. Thanks so much for coming to theCUBE. Yeah, thanks, Thomas. Thank you for having us. All right, thank you for watching. Keep it right there, Dave Vellante and Paul Gillin. We'll be back right after this short break at the Red Hat Summit 2022.