Nvidia’s AI Chief: “AI Agents Will Solve the World’s Biggest Problems”

Hey, welcome to the Next Wave podcast. I'm Matt Wolf, and in today's episode, we're talking about AI agents. I had the opportunity to go spend a few days with Nvidia out at the Nvidia GTC conference, and in this episode, I'm going to deep dive with Amanda Saunders from Nvidia. She's been at the heart of the AI agent revolution that's happening right now while working with Nvidia, the company that's enabling the AI revolution. In this episode, we'll unpack ex exactly what agentic AI is and how it's reshaping industries from healthcare and telecom to sports coaching, as well as why that's just scratching the surface of what's possible. You'll also discover Nvidia's secret blueprint for easily building powerful AI agents, as well as get into some of the fears that people have around AI agents. Like, is an AI agent going to take your job and replace you? Yeah, we're going to go there. And after the interview, I'm going to share some cool clips from Nvidia's gtc, where I got Bob Petty on also from Nvidia, to break down exactly what Nvidia's DGX Spark is and how they're going to be putting AI supercomputers in normal people's homes. You can have an AI supercomputer in your home by this time next year. We're going to get into that in today's episode. So without further ado, here's my discussion with Amanda Saunders, followed by my tour of the DGX Spark with Bob Petty. Enjoy.

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Yeah, so my name is Amanda Saunders. I'm the Director of Enterprise AI Software here at Nvidia. So, you know, a lot of people think about Nvidia and all the amazing GPUs, CPUs, and DPUs that we make that go into these powerful systems. Well, actually, we have a lot of software that runs on that. So me and my team, we focus on how do we tell that story and bring it to customers. I've been at Nvidia 10 years now, and I've covered everything from the graphics side of our business into the data science. And now, of course, the hottest topic, generative AI.

Very cool. Let's talk about agentic AI, because that's the hot topic. It seems like in 2025, everybody's saying this is the year of agentic AI. How would you define agentic AI?

Absolutely. So agents are helpful software tools. They are digital employees that help us augment our work. And what's really important about agents and agentic AI is that it can perceive. So it sees the world around it, it sees the data that it has access to, it takes that information, it reasons about that information, it thinks about it, and then it can actually make actions based on that data. So it can perceive, it can reason, and it can act. And that's what really sort of sets it apart from generative AI that we had in the past is being able to take those actions, whether that action is alert a human or actually actively using a tool and making something happen. And so, yeah, I think they're really exciting because they're able to solve complex problems that we've never been able to do with software before.

Very cool. So what do you think makes agentic AI, like, powerful right now? Why not a year ago? Why not two years ago? Why is now the time everybody's talking about it?

You know, I think this has been a journey that we've been on. You know, it started with having to have the accelerated computing. You had to have the computing power to do that. And that's a problem that Nvidia has been working on for 30 years. And finally we've got to the place where we had the computing systems. We also then needed the software systems. And so it started with the open models that became available. I think LLAMA was a. Was a huge advancement and step forward, and more and more models have come out recently. Reasoning models, in fact, are one of the big pieces that have sort of driven that and sort of added to that, and then just the full ecosystem of tools and things that are required. I think AI is one of those really interesting fields where the more people use it, the more they want to use more of it and they want to do more. And so it's really spurring this incredibly fast growth that we're seeing, which is, you know, breakneck speed. But that's what's really driving us from, you know, the early onsets of generative AI, where you're just spitting out answers into these worlds now, where they're actually thinking things through. And Making really smart actions, which is just sort of cool.

Yeah. Or in my case, I got into AI and now I just make content about it because I'm obsessed with it. It's all I ever want to talk.

About because, yeah, once you get into it, you really, that's all you want to do is you figure out what it can do. You learn where it's, you know, boundaries are and then you want to do more and more and more. And as it continues to, you know, get better, you can do more and more things with it.

So.

Cool.

Well, can we walk through like a agentic workflow? Like give some examples of, like, this is the types of things an agent can do.

Yeah, absolutely. I think, you know, some of the most basic things that we see from agents are really about being able to talk to data. I think that's probably the first thing that we see most people try to do is they have their own personal data. They connect an LLM to that data and then they're able to ask questions. And I think, you know, more recently they're able to ask more and deeper questions.

Right.

Deep research has been a big topic. It's a big topic here at gtc. And the more information you can get out of that data and the more quickly you can query that. That's a pretty standard agent that we would see out there now, then they start to get a lot more complex. I think there's some really cool stuff going on in the telecom space where they're actually using agents to improve the network. So they can actually predict when there might be network outages coming and they can make recommendations to the human employees who maybe you want to make these changes because maybe there's a large show in town like GTC and there's going to be a lot of, a lot of traffic on that network. Here are the recommended changes so that you can actually still provide the service.

Is that kind of thing happening at GTC right now? Are they using that?

I wish they would.

I think the agents are just starting to be developed, so maybe next year we'll actually see that. But it's definitely something that we're going to see more of. Yeah, but, yeah, so those I think, are some varying levels of examples. And then there's obviously hundreds more in the health care space, you know, helping nurses and doctors become more efficient because we know they need the help to, you know, everyday people like you. And I, I mean, I, I use it for almost everything I do, whether it's in my personal life or my work life.

Are There any, like agents that, that might surprise people? Anything that's like really sort of, like, interesting that. Oh, I wouldn't have thought people would be using agents in that area.

It's an interesting question. I mean, the most interesting one for me on the agent side is really as you started to get into video and other types of data sources like text, I think people have gotten the handle on, but adding sort of video understanding and things coming through it. Actually, one of the really cool use cases that we recently did was Jensen got to throw out the first pitch at a baseball game.

I saw the clip of that.

And we used a video agent to be able to critique his. I remember that performance, which I just think is really cool. So you can imagine for, you know, athletes, whether they're amateurs or professionals, being able to use that is really helpful.

Yeah, yeah, I can see that. Like golfers and stuff like that. Analyze my swing, tell me what I'm doing wrong, stuff like that.

It's probably too much for the AI on my swing, but it still would be helpful to know.

Yeah, yeah. Well, let's talk about the Nvidia blueprints. Because when I was at ces, that was a big topic during CES was the Nvidia blueprints. And to me that's sort of like the beginnings of agents. Right. But they're sort of this pre built agent. You could probably explain it better than I can. So let's talk about that.

Exactly. We tried to name them blueprints so that people would get the idea that these are reference architectures. And what they do is they take the different, different building blocks that Nvidia is offering to make these agents. And it helps you with a recipe on how to put them together. So it starts out with our NIM microservices. And these are packaged, containerized, optimized models. So all the leading open models that are out there in the market, we take them, we containerize them, we add a standard API call to them so that people, you know, developers out there can start building and we package those up. So now you've got the model, now you need to connect that model to things. Right? A model. And it's only when we does so much.

So like a NIM would be like, you've got a video NIM that this one NIM can produce AI generated video. This NIM could produce text to speech. This NIM could do speech to text.

Exactly right. Okay, Exactly. So we actually have 100 NIM that are now available that you can download and use. And they do there are 40 different sort of domains or modalities that these different NIM can do. So, yeah, so you can take a bunch of them, piece them together. And so when we built those, everyone was like, this is great. We need the models, we need them to run really well on our Nvidia GPUs. But then they said, well, but how do we build them into the next thing? And that's where blueprints came along. So they are the recipe that allows you to take the nim. Start with this recipe, piece this together, and you'll get to an agent. And then what's even better is you can then customize them so you can add your own data sources, you can add your own pieces in, you can combine blueprints together. So maybe you started with a chatbot and you wanted to add a digital human on the front end. We have a blueprint for both of those. You piece them together and all of a sudden you have a digital avatar who can talk to you with a full face and expressions and probably even.

Your voice if you wanted to.

Yeah, absolutely. And you can start with pieces from Nvidia or you can start with pieces from the ecosystem. We try to make it really easy to piece it all together.

Super cool. Let's talk a little bit about, like, security. I know when it comes to AI agents, they can be used for both good and bad. What kind of things can we do to sort of protect and secure and make sure that people aren't going and using these agents for, you know, bad actor type stuff?

Absolutely. I think, you know, what's really cool about AI is where it raises concerns or security challenges, and it can actually also help answer them. So there's a blueprint out there for container security that does a lot of those pieces, but we also have other applications that can actually track activities and make alerts based on detecting some anomaly. Right. So that tends to be how you recognize that something's doing something that maybe wasn't supposed to. And AIs are just really good at that because they can consume a lot of information. So I think where AI potentially can raise some concerns, it's also in some ways the answer to addressing some of those concerns, which I think is really helpful.

Right. So this sort of like, sci fi movie scenario of like the AI rising up against us, I mean, is this something that people should be concerned about, or is this something that you feel there's pretty good guardrails in place for?

I think the builders of these apps need to make sure those guardrails are in place. But I think, yes, in general, I think the tools exist, and then it's just about us as the application builders being smart about how we deploy them. In general, I think agents are really powerful. They're incredible tools that help humans, but on their own, they're not about to sort of go off the rails. It really takes a human to take it there. So I think, again, it is a tool. It is something that can work alongside you, but I don't see the rise of the machines quite yet.

So, along similar lines, I think one of the fears a lot of people have is, is an agent going to take my job? So, you know, what are your thoughts in that sort of realm? I mean, for me personally, I see all of AI as like a tool, something that makes me a lot more efficient and makes it so I can accomplish more in a day. But I'm curious, like, what's your take? Do you think that, you know, people need to be concerned about that?

I think the best thing I've ever heard is an agent's not going to take your job, but somebody using an agent might. And that's. That's always the thing that I think gets framed. No, I think exactly that, like, we as humans have the capacity to do so much more. There's just often so many hours in the day.

Right.

And so we don't, you know, accomplish, you know, with agents, the work that we were trying to do and then just stop. No, it allows us to go and do more and try more. And so I think, you know, for me, it's not about replacing your job. It's about making you more effective at your job and being able to do more and, you know, be more powerful. I think, you know, healthcare for me is one of those industries that this is driven out there. There's actually a great partner of ours in the mental health space, and they're using agents to free up therapist time from all the administrative work that they have to do. We know their jobs are hard enough as it is. If we can free that time up, it allows them to spend more time with their patients, actually deeply understanding and working with them, as opposed to worrying about calendars and taking notes and scheduling. So I think those are great examples where it's. No, it's something there that's going to help your job. It's not going to take your job.

Yeah. I bumped into somebody on the street while I was walking yesterday, and they had a very similar concept for their company. They work with therapists and they create chatbots. Specifically for the therapist, so that their customers can go and have the initial conversation with the chatbot, but then the conversations get passed along to the therapist. The therapist can kind of, you know, detect when some bigger issue that they need to address comes up, but it's not replacing the therapist. It's just, hey, I can now handle more patients, you know, and give them.

More of my dedicated time.

Right.

And I think there's something actually interesting as you start to see chatbots, and particularly those with digital avatars, is humans will open up in some ways to one of these, you know, chatbots or these avatars in ways that they may not necessarily feel comfortable opening up. So I think it's just giving us new avenues to do things that we were already doing anyway, and I think that's pretty cool.

Yeah, this is sort of switching gears a little bit. But do you think there's, like, a compute bottleneck for agents? Are we going to be able to scale agents and get to agents as fast as people would want to? There was a narrative. I feel like the narrative sort of faded a little bit, but there was a narrative maybe six months ago that there's a wall and AI is hitting a wall. Do you think there's a wall? Do you think we're going to hit some compute bottlenecks?

I think, you know, this is our life's work at Nvidia, is to make sure that we have the computer to drive the world's agents. And so I think a lot of the announcements we talked about today, both on the hardware and on the software side, are really focused on making these as efficient as possible. And I think what's really cool is, as you're looking at this AI space, when it first comes out, it's usually big, it's boxy, it's maybe not the most efficient. And then over time, that efficiency comes in and allows us to do the next big leap. And again, that starts out big and heavy and again gets more and more efficient over time. So I think I'm not seeing a bottleneck, but I do know it's an area we need to continue to drive, because I do think the compute requirements are going to continue to grow. Cool.

Well, so let's extrapolate out a little bit. So if you're looking like five, ten years down the road, what do you sort of envision with AI agents? Where do you think this is all headed? And I know that's hard because it's sort of exponential technology, and it's really hard for humans to grasp exponentials.

I mean, I can barely keep six months out, you know, and new things keep popping up. I mean, I think certainly the. The biggest things that we're going to see are teams of agents. And that's going to be, you know, that we're already seeing it today, it's starting now, and it's going to continue to grow. And so, you know, the way that I see it is, you know, to when we first got agents and we first got, you know, chatbots and things, you could ask a question and it would respond to a question, and then you could ask it to do a function, and it could now do that function, and then you can ask it to do a bigger job. And these agents are just getting more and more sophisticated. So five to 10 years out, we may be able to give something as simple as design me my retirement home. And it will come back with all of the pieces involved in that without a human ever having to do another prompt to follow it up. And I think that could be pretty cool. So that might even be just two or three years. But, yes, I certainly see that. I think that's where we're going.

Yeah. So you mentioned teams of agents. That's really fascinating. Like, do you have any examples of, like, what sort of agents would team up to work together and what sort of tasks will accomplish?

Yeah, absolutely. So I work in marketing, right. And so a lot of my job is about doing messaging, writing blogs, getting imagery, building demo videos, things like that. And today, you know, you start with one, you know, app that can help you write, and then you go to another app that helps you build images, then you go to another app that helps you write the code for the website. And then, you know. Right. And they're all separate and each one of them returns to me and I move to the next step. I think in the future, what we're going to see is those agents will all be connected through function calls. And we actually at GTC announced a blueprint that's going to help us do this. It's called iq. And so by connecting all these together, again, as a human, I'll be able to start put in my overall request. I need to build a new website for a new announcement that's coming out, and it will be able to do all those functions together. And I think what's really cool about this is it's going to allow us to design agents that solve specific tasks, but by combining them together in that sort of that composable way, they'll be able to do bigger and bigger job functions.

Very cool. Like what sort of bigger world problems do you see, like AI and AI agents solving for us?

I mean, I think networking is a really interesting one because anything that has that much data that humans can't solve, I think is amazing. You know, digital twins and simulation of those types of environments. Whether it's, you know, the climate and weather, whether it's, you know, the businesses and things that we run, I think all of these are areas where agents are just going to add to the ability to work with these. So, yeah, certainly, I think those are big overall. And then I think businesses, of course, it's going to be about, you know, providing those tools so that their employees are just that much more effective.

Yeah.

And that's, that's not a big world problem, but it's a very common, serious world problem.

Yeah. One of the things that I think was really fascinating and I've heard I make jokes about how I feel like I'm on like the Jensen tour because I've actually seen his last, like five keynotes. But one of the things that I've seen him talk about that is really fascinating to me is the Earth 2 where it's got the, they basically can map out the weather patterns and figure out weather events a lot earlier. So I'm really excited to see the sort of overlap of like the Earth 2 concept and agents and solving some of the more like bigger climate type issues as well.

I mean, I think one of the things when we introduced Earth2 is this idea that we think a lot about these problems, we have conversations about them, but it's really hard as humans to sort of visualize what some of the changes that we make in government and things like that are going to do in the next five to 10 years. We're very immediate creatures, so that's where we're focused. But I think imagine being able to go to Earth 2 and through an agent say, hey, if we made these changes, what would happen? Or if we took these steps, how could that affect the world and what more can we do? And maybe there are things that we're not even recognizing that the agent could recommend. Because I think that's what's really cool about these agents is they're not humans, they don't think the way we do. And so by giving them all that data and giving them an Earth simulation, they might be able to uncover things that we've never thought of.

Yeah.

Yeah, I think that's pretty cool.

Yeah, I think that's amazing. What are some of the things in the AI world? Agents or otherwise that have you personally really excited. What sort of stuff do you use? What do you play with? What's your AI stack that you use?

I mean, I use as many as I can get my hands on. We have a lot of them in Nvidia. I mean, one of my favorites, and I know Jensen talked about it a little bit on stage, is Perplexity.

Yeah, I love Perplexity.

I love Perplexity because I think it changes the dynamic of humans and how we interact with. Rather than searching for information and having to spend the thought process on that. It's really about who can ask the best questions. And I think that's a really powerful change and power dynamic that Perplexity is given to the user is now if you can ask great questions, you can find great information. So I love that tool. We have AI agents in our company that help us with everything from our benefits and understanding how to make the right decisions for each employee, which I think is pretty cool into how we do our job, whether that's video creation, image creation, certainly content, which you have to write a lot of. So, yeah, so all of those I think are great. And then, you know, I take them into my personal life in terms of organizing and planning. I'm very organized at work and I don't always save a lot of that organization for my personal life. And so I can hand it off to, you know, any source of chatbots. I think ChatGPT is excellent for this. It's just pretty cool. And lately deep research, so I've been using a lot of that. And again, we just introduced a blueprint that's going to allow deep research on your own personal things. And so I can imagine that's going to be pretty powerful.

So for people that want to sort of stay in the loop on AI that are curious, maybe they're worried about it, like, what sort of advice would you give them to sort of stay on top of things?

My best advice on AI is use sounds really obvious, but I think one, I think it actually alleviates a lot of concerns when you understand how the technology works and by using it, you can see what works today, where the limitations are and how it kind of functions. And I think it lets people see it as that tool versus.

Something that might be scary. So that's really the first piece of advice I think that's really important. And then I think it's about trying to identify where many of those concerns might be coming from, whether that's data or security or things like that, and understand how AI can also help with those. And so that tends to be, you know, one of, one of my best pieces of advice.

Cool. Yeah. Well, let's talk about llama 3 and Nemotron.

Yeah. So Llama Nemotron.

Llama Nemotron. Let's talk about Llama Nemotron.

Absolutely. So Llama Nematron was a really cool announcement. So Nvidia, we work with all of the leading model builders that are out there, including Meta, who released Llama. And what's really cool about Llama is, is there are, I think it's 85,000 derivatives of this model.

Right.

And so of course in video we've got a lot of really smart people who know how to optimize and make models more efficient. And when Reasoning came out and when Deep SEQ introduced this reasoning wave to the open source, we sort of said, well, how can we bring this and make it really efficient for people who want to deploy this on Nvidia? And so by starting with the Llama model, which is an incredible model for we brought in our expertise to train it so that a model that previously couldn't do reasoning now could actually think through problems. And so we found the data set to go be able to train the model on how to do this new task and we trained it and then we made the data source open, which I think is really cool. So if others want to do their own training or if they want to train a different model or anything like that, that data source is available. But it's just teaching a model a new skill is I think a really powerful thing to see because it shows how quickly the space is evolving.

So is there anything actually happening like underneath Llama or is it just you've got the Llama model but now there's a new layer on top of it that knows how to think. Is there did any like new training happen to the model?

Yes. So yeah, the model was absolutely. So we post trained the model. So this is what a lot of companies out there are doing today is they take a base model and they actually train it with new data. And in this case sometimes you're training it so it has more information on a particular topic. This is particularly popular when you've got domains and industries that have specific languages that they speak, things like maybe the finance industry. But in this case we actually were training it on a skill and that skill is to think through problems. And I think this is what's really interesting about reasoning is the way a reasoning model works is it's starts by thinking through the question that you're asking it, and it breaks down that question into multiple steps and multiple parts, and then it actually goes through and comes up with answers for each of those parts. And then it checks to say, okay, now that I've got those answers, does this actually come back with the right question? And it continues to do that until it gets to this highly accurate response. And so not only are reasoning models really good for improving accuracy, which we all know that when an AI model is useful, when it's accurate, but it also allows us to solve problems we've never solved. So a great example for me on this is I love puzzles. I love all sorts of puzzles, but particularly Sudoku, because I hear it's good for your brain. Sudoku is a problem that humans can solve, but actually traditional LLMs couldn't. There are actually almost 80 different decisions that go into solving a Sudoku puzzle, and each one of those affects the other decision because obviously, based on the rules that you have to follow, reasoning models. And Llama Nebotron is a great example of this, can solve Sudoku where a llama model on its own couldn't. And so, again, that's the type of thing. It doesn't sound like solving Sudoku puzzles going to change the world, but when you look at a Sudoku puzzle, there's actually a lot of things that go on in the world that are related to that. I have a supply chain. I've got to ship items and get them to different stores around the country. I happen to know there's a snowstorm coming in, and I need to make sure that my trucks are taking the most efficient routes. All of those are steps that impact the other decisions that are being made. And so it's actually quite a complex problem that relates to, in some ways, to Sudoku.

Yeah. Well, it's so interesting, too, because you take a normal model and it can't tell you how many Rs are in Strawberry. Give it a thinking model and it will actually count. And then double check, did I do that right? And then answer and get the right answers. It's so fascinating because it seems like such a simple problem to a human brain.

But then that's. I think the thing is, we look at models and we think of them like we personify them as humans, and they're not right. And so, yeah, no, reasoning models are really cool for that. And we've seen a lot of those great examples that come out of what these reasoning models can do. And it is. It's just. I think it's really interesting to watch.

Yeah, it's so cool to actually see the thought process because you'll actually see the models think through something and then go, wait, that's probably not right. Let me think that through again. And you actually see that text come out of it, thinking through. And that to me is fascinating.

It's fascinating. And it's a great example of the compute story we were talking about, which is, you know, we do need more compute so it can think. The more it thinks, the more compute it requires. So it's this really interesting cycle that we're watching these things go through. One of my favorite things to do with reasoning models is to ask them to describe things to me. Like a five year old.

Yes.

And it will come up with a description. It will check if a 5 year old would understand that description. It will then make changes. And it's really interesting to see what models think five year olds understand. So at some point I'll have to go test it with a real.

Yeah, read this. Does this make sense to you?

Does this actually make sense? Exactly, exactly.

Very cool. Well, along the same line, real quick, let's talk about hallucinations. Do you see like a path to zero hallucinations? Do we want to get rid of hallucinations completely? Like, what are your thoughts on that?

I think, you know, there's certainly things we can do to reduce hallucinations. And some of those are, you know, as simple as putting a guardrail in place that says if you're not 100% confident in the answer or 99% confident in the answer, don't answer the question.

Right.

That's sort of its stops the model from doing things that it shouldn't. And Nemo guardrails, which is one of the products that Nvidia offers, helps with building those. But to your point about do we want to stop hallucinations altogether? Of course we want it to give the right answers, but in being creative, we're asking it to come up with new things. So you have to be able to distinguish between a hallucination and a creative generative response.

Right, Right.

So I think that's where this balance plays. So there are absolutely steps that can be taken and depending on how targeted and focused you want the model to be, you can put more and more of those sort of guardrails or those policies in place that will keep it from hallucinating.

Right?

Yeah.

Because I think in a lot of scenarios, hallucinations are a feature, not a bug. Right. If you wanted to write a short story for you, you Want it to hallucinate that short story for you?

Exactly. So I think that's where we have to understand what's the hallucination versus what's the model doing, what is it's supposed to do?

Right.

And again. And that's where it becomes what's the use case? What are you trying to have it do? And really think those through and then find the right tools from Nvidia or others to be able to actually go and do that.

Very cool. Yeah. So if somebody wanted to get started with agentic AI, they want to start playing with agents and testing the waters. What do they do? What are our steps?

Well, so from Nvidia we have something called build.invideo.com we made the URL super easy. If you're trying to build something, we have a one stop shop for you. It's got all the models on there so that you can test them out whether it's the new Llama Nematron model with reasoning. You can actually turn reasoning on and off. It also has all the blueprints so you can actually test them and experiment from them. And then from there there are also steps to go deploy them and test them out and build them yourself. So I think that's a great starting point.

Very cool. And for the more technical people that maybe are trying to develop something like is there a place they can go play with the APIs? What do we do there?

Build.Nvidia.com Same place. It's literally whether you're an enthusiast, whether you're a developer, whether you're actually trying to build something to put in production, this is the one stop shop because everything's on there. You can take it as far as you want. You can play around with the UI, you can play around with the APIs, you can actually download and deploy these models on any Nvidia hardware. It's your one stop shop for everything.

Yeah. Actually that brings me to another question. Does this stuff work on older Nvidia hardware? If you have a 3080 or a 4070, can I use this stuff on those as well?

Absolutely. The only restriction is does it fit within the memory of the gpu? But if it fits, it ships. So yes, this will run on GPUs that are out there in the market today.

Very cool. Awesome. Well, thank you very much. This has been amazing. Fascinating. I love talking AI and nerding out, especially agents. Agents is the hottest topic. So. So really appreciate you taking the time with me.

Absolutely. I could also take. So yeah, have A good time.

Thank you.

Hi, this is Bob Petty, Vice President and General Manager, Enterprise Platforms at Nvidia. Our first effort with AI developers, right, whether they're enthusiasts, prosumers or professionals, was basically using our RTX Pro cards or GeForce cards running Linux and for many people don't run Linux. We did Windows subsystem for Linux. WSL2 worked with Microsoft to eliminate a lot of what was intended for just an emulator and really turn it in, eliminate the latency blocks so that you could use it to truly develop AI and evaluate AI not just from an accuracy, but also from a latency standpoint. So historically we've gone out with AI workstations or AI PCs for that.

Right.

If you're running intel or AMD CPU, put those in there. The new RTX 6000 Pro Blackwell is 96 gigabyte frame buffer. So you can run 70B models. Fine tune, do a lot of it. A lot of the infrastructure that people are buying today in the cloud or through some of our server partners is Grace Blackwell. It was Grace Hopper. Now Grace Blackwell.

Right.

Grace being the ARM cpu. Right, right.

And that's kind of the same technology that all the big, big like OpenAI those types of companies are using.

Exactly. And the beauty of that is the ARM CPU uses up so much less power. So if the majority of the workload was in the GPU and the CPU is kind of a traffic manager, you wouldn't be able to do what you needed to do for as little as power as possible. So you can put more GPUs in there. So hence Grace Blackwell.

Right.

Well, you can certainly develop AI on Windows workstations with RTX Pro or GeForce, that's all great. But if you need an ARM port of your software or getting familiar, that's where Spark fits the gap.

And this is the same thing as the project digits that was announced.

This is project digits.

Yeah.

We finally chose the name Spark. We had people send in a lot of comments. But it, as Jensen mentioned in the keynote, what was a box that was like this several years ago? 20 core CPU, one petaflop is now in this little 5x5 by less than 2 inch box. It's got the C2C memory between the Grace processor and the Blackwell GPU. It was like 256 gigabytes a second. You won't have that on a traditional workstation because you're going to go over the PCI bus. Right, Right. So high memory bandwidth between the CPU and the GPU 128 gigs of memory available from us online. But that's really for the enthusiasts who want the gorgeous bezel.

So what kind of things can I do with this now that I can't do with my 5090 at home? Good question.

So from a frame buffer size, there are so many more models that you can run on this. You can do fine tuning 70B models. You can you put two of these together with this cable. This is a ConnectX Ethernet. Put two of these together, you can run a 400 billion parameter model. Can't do that on the 5090.

Right.

You can't do the 70B on a 5090.

Right.

And so the size of the model is very dependent. The other thing with the 5090, your memory bandwidth between your CPU and GPU is throttled by the PCI bus. And this, this has cache coherent high speed memory bandwidth. So it really enables you to test what your code might look like running on one of the data center providers OEMs out there. Because same C2C memory cache coherency speed. You can do more than just say it works. You can get it to the point where you can remove a lot of the bottlenecks. Whether you're doing a vision language model or you know, multimodal model. That's the biggest benefit. One is getting your code ready for what's the predominant AI infrastructure out there. But the other is testing in a way that simulates how it's going to run, you know, when you run it on a node there. And then the idea is you're not wasting data center time or cloud time, just debugging or eliminating bottlenecks. When you're here, you deploy and you scale from one GPU to ngpu. So.

The main purpose of this was really to help spread the Grace Blackwell ecosystem. They're going as fast as we can make them, but they're not necessarily accessible to enthusiasts who want to use FP4 features of Blackwell, which you can do on 5090 but want to use it with some of the more popular models that have higher parameters parameter sizes. So EZ Box the 1 TB version of storage 128 gigs is $29.99. The 4 TB version is $39.99. You can reserve on Nvidia.com Our initial go to market partners are Dell, HP, ASUS. They've got their own branded, branded boxes without the gold foil. And then we'll expand that.

Yeah, I remember Jensen said something to the effect effect of like imagine it's a cloud computer just sitting on your desk. It's not going to the cloud. You know, you don't have to worry about Internet connections, anything like that. It's just a cloud computer sitting on your desk that can do all of the inference right there. On the bigger model, it's an AI.

Supercomputer on your desk and there's a bigger one that we'll walk to in a second. But the other thing about this is we're not suggesting that everybody just replace their existing, you know, laptop or workstation. You got a GeForce laptop or RTX Pro laptop. You, you plug this guy into it. So you might do all, everything you need to on a 5090, run your games and everything. You're doing AI development or want to write AI inferencing that helps you on your, your 5090. Yeah, plug that into it. It's a. And that's why Jensen. So the, you know, the MacBook and the, and the Spark. Right, because it's really meant to be both a plug in to Uber assist.

Right.

You know, maybe less capability capable machines whether they have a GPU in them or not.

And it's not using all the processing on your computer. So you can be running AI models on this. Full blame cyberpunk on your computer.

Exactly. Yeah, exactly. And you know, just format factor wise, cost wise, we think it'll be easy for people to do it as an add on. But certainly there's, you know, there's a great GPU in here. We've, we're running games on this. I wouldn't want it as my GeForce laptop, but I'd probably want to connect it to my GeForce if I was, you know, doing AI tuning for game development or things like that.

Right, right.

So that's, that's DGX Spark. We just walk this way. This is the RTX Pro line. Our 6000 line is the one that's, you know, somewhat akin to the 5090 on the G4 side. The reason we have this Pro line, the manufacturing of it is a very precise bom. It's not built by many different aics. There are computing benefits on here that we perceive the gaming community doesn't need. So some of the high end compute performance here is going to be much better. The AI inferencing performance is about the same. Big difference is frame buffer.

Right. Doesn't this one have like 96 gigs of video?

Yeah, 96 gigs of RAM. And if you want to get full power, we've got the 600 watt version, we've got a 300 watt version that most desktops can take today. And then you've got the same technology in the server version that would go in a rack here. So we used to call these in the past we had a 40s or L 40s based on ADA Lovelace, the Ampere Lovelace we're going to call that B40 based on Blackwell, but kind of aligned around RTX Pro Workstation Max Q. Max Q is that optimal? PowerPoint and the server Edition. So again, same infrastructure you can code and develop and then deploy on your RTX server and a rack in the data center meant to save time. So these are our DGX stations, initial partners, Dell, hp, Asus Lambda and Super Micro. We show these to here because these are their boxes. This is what it will look like if you look in here. This is a GB300 board. So a much more powerful Grace processor and then extremely powerful B300. B300 is the same GPU in the latest DGX. So 784 gigabytes of memory again. So the benefit is you're doing ARM development, a lot of memory and very, very high speed memory bandwidth like Spark. So you're not just seeing that code works, you're seeing how well it works before you chew up time on your data center rack.

Now does this need like a separate cpu, like a Intel AMD kind of thing?

No, it's on both the Spark and the Station. We're providing the gray cpu, so gray CPU and the Blackwell GPU there. The graphics out. We don't put a big graphics card in here, so this One has a 4000. That's a small form factor. The reason being we want this to plug into a standard 15amp wall outlet. Might need to be a dedicated one because you know, it's at 1650 watts I guess. And we're going to come pretty close to that, which is why the manufacturers are liquid cooling this and they started doing that in the gaming side. So some of the Alienware chassis you seem to liquid liquid cool, right? So they're well adept at that. They will liquid cool this so we can stay, you know, thermally be good and not need to take up a lot more power with a lot of fans. So again, if you're an enthusiast just getting started, even an enterprise where you know the size of your models and what you want to get done, easily connect that. If your full time job is like prepping AI, developing AI to deploy to the data center, you have your choice of logging it to the data center. Maybe you get a virtual workstation delivered back to you and you do your job or putting this at your desk. And this is literally. It's like one of those B300 nodes. It is a server node in a desktop with graphics out right there. From a data privacy standpoint, from IP protection standpoint, I'm not sending anything anywhere, right. It's right there. And that's more important than just privacy and IP protection. It's just the time and the cost of transport of data. You might run a, you know, a 600 billion parameter model, but the data that you're running it on, whether, let's say it's Cosmos, vlm, all the videos that you're going to be processing and the amount of data you'd want to, to just sit here versus upload all that data or even if your own dedicated data center and run it there and then download that. So you got ingress and egress, cost of data transport all happening right here. That guy will be available in the summer time frame. Okay, Reservable today. This guy will be late summer. We have a Founders edition for that because it's cool and enthusiasts are going to want, want something on their desk, Right? This one is only available for the OEMs. Way for us to scale our enterprise business is through the OEMs. And so if you were to go to the Dell booth today or the HPI booth or asus, you'll see their versions of these here. You'll see their versions of the Spark with the Dell Blue and the Nvidia Green LED and the HPE Blue. And that's the way to expand the ecosystem for Grace ARM development, Grace Blackwell Development. Expand the access to technology that normally is only available if you've got the capital expense to put one of these racks in and now it's at your desktop.

Very cool. Yeah, amazing. Well, thanks, Bob. This has been really informative and I appreciate it. Thank you.

No problem. Thank you.

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