Nvidia reported first quarter results that topped Wall Street expectations on both the top and bottom lines. The tech giant also announced a 10-for-1 stock split and is raising its dividend.

For Q1 of fiscal 2025, Nvidia (NVDA) reported revenue rose 262% to $26.0 billion, with its Data Center being the biggest contributor. Revenue for that unit soared 427% year-over-year to $22.6 billion.

There have been concerns that with the company's new Blackwell platform coming later this year, customers may be holding off on purchasing Nvidia's Hopper products. In an exclusive interview, Nvidia founder and CEO Jensen Huang said that's not the case. "Hopper demand grew throughout this quarter after we announced Blackwell, and so that kind of tells you how much demand there is out there. People want to deploy these data centers right now. They want to put our GPUs to work right now and start making money and start saving money. And so that demand is just so strong," Huang says.

One point Huang made is just how big inference is. Nvidia describes AI inference as "the process of using a trained model to make predictions on never-seen-before data." Huang tells Yahoo Finance's Julie Hyman and Dan Howley Nvidia is in a "great position" regarding inference because of how complicated the problem is. Inference is going to be "a giant market opportunity for us," Huang adds.

Watch the video above to hear how Huang says automakers like Tesla (TSLA) are using his company's products to power the future of autonomous vehicles.

This post was written by Stephanie Mikulich.

For more Yahoo Finance coverage of Nvidia:

Nvidia stock pops 4% after earnings beat forecasts, announces stock split and dividend hike

Nvidia CEO Jensen Huang is the 'man of the year': Investor

Why this analyst says Nvidia is not a stock to buy

How Nvidia earnings are impacting the chip market

Beyond the Ticker: Nvidia

Video Transcript

JULIE HYMAN: I'm Julie Hyman, host of Yahoo Finance's Market Domination. Here with our tech editor, Dan Howley. NVIDIA's done it again, the chip giant blowing past analyst expectations in its strong fiscal first quarter.Data center revenue alone, soaring by 427% year over year. And the company also gave another bullish sales forecast which shows that AI spending momentum continues apace. On top of all that, the company also announced a 10-for-1 forward stock split and raised its dividend. Joining us now, Nvidia founder and CEO Jensen Huang, fresh off the conference call. Jensen, welcome. Thank you so much for being with us.

NVIDIA CEO JENSEN HUANG: I'm happy to be here. Nice to see you guys.

HYMAN: You too. I wanna start, uh, with Blackwell, which is your next-generation chip. It's shipping this year, you said on the call. You also said on the call, we will see a lot of Blackwell revenue this year. So if we're looking at about $28 billion in revenue in the current quarter, and Blackwell is a more expensive product than Hopper the chip series out now, what does that imply about revenue in the fourth quarter and for the full year?

HUANG: Well, it should be significant. Yeah. Blackwell, Blackwell, uh, and, and as you know, we guide one quarter at a time. And, uh, but what I, what I could tell you about, about Blackwell is this. This is a giant leap in AI, and it was designed for trillion parameter AI models. And this is, as you know, we're already at 2 trillion parameters, uh, models sizes are growing about doubling every six months. And the amount of processing, uh, between the size of the model, the amount of data, uh, is growing four times. And so the ability for, uh, these data centers to keep up with these large models really depends on the technology that we bring, bring to them. And so the Blackwell is, is, uh, designed, uh, also for incredibly fast inferencing and inference used to be about recognition of things, but now inferencing, as you know, is about generation of information, generative AI. And so whenever you're talking to Chat GPT and it's generating information for you or drawing a picture for you, or recognizing something and then drawing something for you, that generation is a brand new, uh, inferencing technology is really, really complicated and requires a lot of performance. And so Blackwell is designed for large models for generative AI, and we designed it to fit into any data center. And so it's aircooled, liquid cooled, x86, or this new revolutionary processor we designed called Grace, Grace Blackwell superchip. And then, um, uh, you know, supports, uh, infinite band data centers like we used to, but we also now support a brand new type of data center, ethernet. We're gonna bring AI to ethernet data centers. So the number of ways that you could deploy Blackwell is way, way higher than the, than Hopper generation. So I'm excited about that.

DAN HOWLEY: I wanna talk about the, the inferencing Jensen. You know, some analysts have brought up the idea that as we move over towards inferencing from the, the training that there may be some in-house companies, uh, uh, processors from companies that those made from Microsoft, Google, Amazon may be more suited for the actual inferencing. I guess, how does that impact Nvidia then?

HUANG: Well, inferencing used to be easy, you know, when people started talking about inference, uh, generative AI didn't exist, And now generative AI is, is, uh, uh, of course is about prediction, but it's about prediction of the next token or prediction of the next pixel or prediction of the next frame. And all of that is complicated. And, and generative AI is also used for, um, understanding the... in order to generate the content properly, you have to understand the context and what, what is called memory. And so now the memory size is incredibly large, and you have to have, uh, context memory. You have to be able to generate the next token really, really fast. It takes a whole lot of tokens to make an image, takes a ton of tokens to make a video, and takes a lot of tokens to be able to, uh, reason about a particular task so that it can make a plan. And so the generative AI, um, era really made inference a million times more complicated. And as you know, the number of chips that were intended for inference, uh, kind of, kind of fell by the wayside. And now people are talking about building new chips. You know, the versatility of Nvidia's architecture makes it possible for people to continue to innovate and create these amazing new AIs. And then now Blackwell's coming.

HYMAN: So in other words, you think you still have a competitive advantage even as the market sort of shifts to inferencing?

HUANG: We have a great position in inference because inference is just a really complicated problem, you know, and the software stack is complicated. The type of models that people use is complicated. There's so many different types. That's just gonna be a giant market opportunity for us. The vast majority of the world's inferencing today as, as people are experiencing in their data centers and on the web, um, vast majority of the inferencing today is done on Nvidia. And so we, I expect that to continue.

HUANG: Hopper demand grew throughout this quarter after we announced Blackwell, and so that kind of tells you how much demand there is out there. People want to deploy these data centers right now. They want to put our GPUs to work right now and start making money and start saving money. And so, so that, that demand is just so strong. Um, you know, it, it's really important to take a step back and realize that what we build is not a GPU chip. We call it Blackwell, and we call it GPU, but we're really building AI factories. These AI factories have CPUs and GPUs and really complicated memory. The systems are really complicated. It's connected by NVLink. There's an NVLink switch, there's InfiniBand switches, InfiniBand NICs, and then now we have Ethernet switches and Ethernet NICs, and all of this connected together with this incredibly complicated spine called NVLink. And then the amount of software that it takes to build all this and run all this is incredible. And so these AI factories are essentially what we build. We build it as a, as a holistic unit, as a holistic architecture and platform, but then we disaggregate it so that our partners could take it and put it into data centers of any kind. And every single cloud has slightly different architectures and different stacks, and our, our stacks and our architecture can now deeply integrate into theirs, but everybody's a little different. So we build it as an AI factory. We then disaggregate it so that everybody can have AI factories. This is just an incredible thing. And we do this at very hard, very high volume. It's just very, very hard to do. And so every, every component, every every part of our data center, uh, is the most complex computer the world's ever made. And so it's sensible that almost everything is constrained.

HOWLEY: Jensen, I wanna ask about the, uh, cloud providers versus the, the other industries that you said are, are getting into the, the generative AI game or, or getting Nvidia chips. You, you had mentioned that, uh, in, uh, comments in the actual release and that we heard from, uh, CFO Colette Kress, uh, that 40%, mid 40% of data center revenue comes from those cloud providers. As we start to see these other industries open up, what does, what does that mean for Nvidia? Will, will the cloud providers kind of, uh, shrink, I guess their share, and then will these other industries pick up where those cloud providers were?

HUANG: I expect them both to grow, uh, a couple of different areas. Of course, uh, the consumer internet service providers this last quarter, of course, uh, big stories from Meta, the, uh, the incredible scale that, that, um, uh, Mark is investing in. Uh, Llama 2 was a breakthrough. Llama 3 was even more amazing. Uh, they're creating models that, that are, that are activating, uh, large language model and generative AI work all over the world. And so, so the work that Meta's doing is really, really important. Uh, you also saw, uh, uh, Elon talking about, uh, the incredible infrastructure that he's building and, and, um, uh, one of the things that's, that's really revolutionary about, about the, the version 12 of, of, uh, Tesla's, uh, full self-driving is that it's an end-to-end generative model. And it learns from watching videos, surround video, and it, it learns about how to drive, uh, end-to-end and using generative AI, uh, uh, predict the next, the, the path and the, and the, uh, how to steer the, uh, uh, how to understand and how to steer the car. And so the, the technology is really revolutionary and the work that they're doing is incredible, I gave you two examples. Uh, a a startup company that we work with called Recursion is built up a supercomputer for generating molecules, understanding proteins and generating molecule molecules for drug discovery. Uh, the list goes on. I mean, I, we can go on all afternoon and, and just so many different areas of people who are, who are now recognizing that we now have, uh, software and AI model that can understand and be learned, learned almost any language, the language of English, of course, but the language of images and video and chemicals and protein and even physics, and to be able to generate almost anything. And so it is basically like machine translation and, uh, that capability is now being deployed at scale in so many different industries.

HYMAN: Jensen, just one more quick last question. I'm glad you talked about the auto business and, and what you're seeing there. You mentioned that automotive is now the largest vertical, enterprise vertical, within data center. You talked about the Tesla business, but what is that all about? Is it, is it self-driving among other automakers too? Are there other functions that automakers are using, um, within data center? Help us understand that a little bit better.

HUANG: Well, Tesla is far ahead in self-driving cars. Um, but every single car, someday we will have to have autonomous capability. Uh, it's, it's safer, it's more convenient. It's more, more fun to drive. And in order to do that, uh, it is now very well known, very well understood, that learning from video directly is the most effective way to train these models. We used to train based on images that are labeled. We would say, this is a, this is a car, you know, this is a car, this is a sign, this is a road. And we would label that manually. It's incredible. And now we just put video right into the car and let the car figure it out by itself. And, and this technology is very similar to the technology of large language models, but it requires just an enormous training facility. And the reason for that is because there's videos, the data rate of video, the amount of data of video is so, so high. Well, the, the same approach that's used for learning physics, the physical world, um, from videos that is used for self-driving cars is essentially the same um, AI technology used for grounding large language models to understand the world of physics. Uh, so technologies that are, uh, like Sora, which is just incredible, um, uh, and other technologies, VO from, from, uh, uh, Google, incredible the ability to generate video that makes sense that are conditioned by human prompt that needs to learn from video. And so the next generation of AIs need to be grounded in physical AI needs to be under, needs to understand the physical world and the, the best way to teach these AIs how the physical world behaves is through video, just watching tons and tons and tons of video. And so the, the combination of this multimodality training capability is going to really require a lot of, uh, computing demand in the years to come.

HYMAN: Jensen, as always, super cool stuff and great to be able to talk to you, Dan, and I really appreciate it. Jensen Huang, everybody founder and CEO of Nvidia.