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Nvidia ​

Nvidia Investor Relations ​

Nvidia Financial Statements Summary Sheet ​

GPU Gemes ​

"GPU Gems" is a series of books published by Addison-Wesley that showcase cutting-edge techniques and insights into graphics processing unit (GPU) programming.

  • GPU Gems 1 (2004):
    The first volume established the series and covered foundational concepts in GPU programming.
  • GPU Gems 2 (2005):
    This volume delved deeper into advanced rendering techniques and shader programming.
  • GPU Gems 3 (2007):
    Focused on emerging trends in GPU programming, including parallel computing and real-time effects.

Nvidia GPU Architectures ​

  1. GeForce 256 (1999)

  2. GeForce 3 (2001)

  3. GeForce 6 (2004)

  4. GeForce 8800 (2006)

  5. Fermi (2010)

  6. Kepler (2012)

  7. Maxwell (2014)

  8. Pascal (2016)

  9. Volta (2017)

  10. Turing (2018)

  11. Ampere (2020)

  12. Ada Lovelace (2022)

  13. Hopper (2022)

  14. Blackwell (2024)

GB200 NVL72 connects 36 Grace CPUs and 72 Blackwell GPUs in a rack-scale design.

The GB200 Grace Blackwell Superchip is a key component of the NVIDIA GB200 NVL72, connecting two high-performance NVIDIA Blackwell Tensor Core GPUs and an NVIDIA Grace CPU using the NVIDIA® NVLink®-C2C interconnect to the two Blackwell GPUs.

Products ​

Investment Articles ​

Ultimately, my firm trimmed our Nvidia position (to a 10% allocation) and will happily buy lower should the assumptions in this analysis materialize.

Nvidia is in a much more complex 4th wave. If this is playing out, NVDA would see the $116 level break, which opens the door to a potential low at $101, $90, or $78.

NVIDIA and Mellanox today announced that the companies have reached a definitive agreement under which NVIDIA will acquire Mellanox. Pursuant to the agreement, NVIDIA will acquire all of the issued and outstanding common shares of Mellanox for $125 per share in cash, representing a total enterprise value of approximately $6.9 billion.

Jensen Huang ​

NVIDIA is the largest install base of video game architecture in the world. GeForce is some 300 million gamers in the world, still growing incredibly well, super vibrant.

We created a library called cuDNN. cuDNN is the world's first neural network computing library. And so we have cuDNN, we have cuOpt for combinatory optimization, we have cuQuantum for quantum simulation and emulation, all kinds of different libraries, cuDF for data frame processing ...

And so we just did it one domain after another domain after another domain. We have a rich library for self-driving cars. We have a fantastic library for robotics, incredible library for virtual screening, whether it's physics based virtual screening or neural network based virtual screen, incredible library for climate tech. And so one domain after another domain. And so we have to go meet friends and create the market.

There are two things that are happening at the same time, and it gets conflated, and it's helpful to tease apart. So the first thing, let's start with a condition where there's no AI at all. Well, in a world where there's no AI at all, general purpose computing has run out of steam still.
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And so the first thing that's going to happen is the world's trillion dollars of general purpose data centers are going to get modernized into accelerated computing. That's going to happen no matter what. And the reason for that is, as I described, Moore's Law is over.
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And now, what's amazing is, so the first trillion dollars of data centers is going to get accelerated and invented this new type of software called Generative AI. This Generative AI is not just a tool, it is a skill. And so this is the interesting thing. This is why a new industry has been created.
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For the very first time, we're going to create skills that augment people. And so that's why people think that AI is going to expand beyond the trillion dollars of data centers and IT, and into the world of skills. So what's a skill? A digital chauffeur is a skill, autonomous, a digital assembly line worker, robot, a digital customer service, chatbot, digital employee for planning NVIDIA's supply chain ...

There's not one software engineer in our company today who don't use code generators either the ones that we built ourselves for CUDA or USD, which is another language that we use in the company, or Verilog, or C and C++ and code generation. And so I think the days of every line of code being written by software engineers, those are completely over. And the idea that every one of our software engineers would essentially have companion digital engineers working with them 24/7, that's the future. And so the way I look at NVIDIA, we have 32,000 employees. Those 32,000 employees are surrounded by hopefully 100x more digital engineers.

And so the amazing thing is, when you want to build this AI computer, people say words like super-cluster, infrastructure, supercomputer, for good reason because it's not a chip, it's not a computer per se. And so we're building entire data centers. By building the entire data center, if you just ever look at one of these superclusters, imagine the software that has to go into it to run it. There is no Microsoft Windows for it. Those days are over. So all the software that's inside that computer is completely bespoke. Somebody has to go write that. So the person who designs the chip and the company that designs that supercomputer, that supercluster and all the software that goes into it, it makes sense that it's the same company because it will be more optimized, they'll be more performant, more energy efficient, more cost effective. And so that's the first thing.

The second thing is, AI is about algorithms. And we're really, really good at understanding what is the algorithm, what's the implication to the computing stack underneath and how do I distribute this computation across millions of processors, run it for days, with the computer being as resilient as possible, achieving great energy efficiency, getting the job done as fast as possible, so on and so forth. And so we're really, really good at that.

And then lastly, in the end, AI is computing. AI is software running on computers. And we know that the most important thing for computers is install base, having the same architecture across every cloud across on-prem to cloud, and having the same architecture available, whether you're building it in the cloud, in your own supercomputer, or trying to run it in your car or some robot or some PC, having that same identical architecture that runs all the same software is a big deal. It's called install base. And so the discipline that we've had for the last 30 years has really led to today. And it's the reason why the most obvious architecture to use if you were to start a company is to use NVIDIA's architecture. Because we're in every cloud, we're anywhere you like to buy it. And whatever computer you pick up, so long as it says NVIDIA inside, you know you can take the software and run it.

GTC(GPU Technology Conference) ​

GTC 2025 ​

GTC is coming back to San Jose on March 17–21, 2025.

Nvidia Quarterly Earnings ​

Q4 FY2025 ​

Hugging Face alone hosts over 90,000 derivatives freighted from the Llama foundation model. The scale of post-training and model customization is massive and can collectively demand orders of magnitude, more compute than pretraining. Our inference demand is accelerating, driven by test time scaling and new reasoning models like OpenAI's o3, DeepSeek-R1, and Grok 3. Long-thinking reasoning AI can require 100x more compute per task compared to one-shot inferences.

NVIDIA's automotive vertical revenue is expected to grow to approximately $5 billion this fiscal year.

France's EUR 100 billion AI investment and the EU's EUR 200 billion invest AI initiatives offer a glimpse into the build-out to set redefined global AI infrastructure in the coming years.

Now, as a percentage of total Data Center revenue, data center sales in China remained well below levels seen on the onset of export controls. Absent any change in regulations, we believe that China shipments will remain roughly at the current percentage. The market in China for data center solutions remains very competitive.
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China is approximately the same percentage as Q4 and as previous quarters. It's about half of what it was before the export control. But it's approximately the same in percentage.

We expect networking to return to growth in Q1.

(Gaming) However, Q4 shipments were impacted by supply constraints. We expect strong sequential growth in Q1 as supply increases. The new GeForce RTX 50 Series desktop and laptop GPUs are here.

As Blackwell ramps, we expect gross margins to be in the low 70s. Initially, we are focused on expediting the manufacturing of Blackwell systems to meet strong customer demand as they race to build out Blackwell infrastructure. When fully ramped, we have many opportunities to improve the cost, and gross margin will improve and return to the mid-70s, late this fiscal year.

Jensen Huang:
And when you have a data center that allows you to configure and use your data center based on are you doing more pretraining now, post-training now, or scaling out your inference, our architecture is fungible and easy to use in all of those different ways.

Jensen Huang
We know several things, Vivek. We have a fairly good line of sight of the amount of capital investment that data centers are building out toward. We know that going forward, the vast majority of software is going to be based on machine learning. And so, accelerated computing and generative AI, reasoning AI are going to be the type of architecture you want in your data center.

We have, of course, forecasts and plans from our top partners. And we also know that there are many innovative, really exciting start-ups that are still coming online as new opportunities for developing the next breakthroughs in AI, whether it's agentic AIs, reasoning AI, or physical AIs. The number of start-ups are still quite vibrant and each one of them needs a fair amount of computing infrastructure. And so, I think the -- whether it's the near-term signals or the midterm signals, near-term signals, of course, are POs and forecasts and things like that.

Midterm signals would be the level of infrastructure and capex scale-out compared to previous years. And then the long-term signals has to do with the fact that we know fundamentally software has changed from hand-coding that runs on CPUs to machine learning and AI-based software that runs on GPUs and accelerated computing systems. And so, we have a fairly good sense that this is the future of software. And then maybe as you roll it out, another way to think about that is we've really only tapped consumer AI and search and some amount of consumer generative AI, advertising, recommenders, kind of the early days of software.

The next wave is coming, agentic AI for enterprise, physical AI for robotics, and sovereign AI as different regions build out their AI for their own ecosystems. And so, each one of these are barely off the ground, and we can see them. We can see them because, obviously, we're in the center of much of this development and we can see great activity happening in all these different places and these will happen. So, near term, midterm, long term.

Jensen Huang
As you know, the first Blackwell was we had a hiccup that probably cost us a couple of months. We're fully recovered, of course.
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And the click after that is called Vera Rubin and all of our partners are getting up to speed on the transition of that and so preparing for that transition. And again, we're going to provide a big, huge step-up. And so, come to GTC, and I'll talk to you about Blackwell Ultra, Vera Rubin and then show you what we place after that. Really exciting new products to come to GTC piece.

Jensen Huang
Well, we built very different things than ASICs, in some ways, completely different in some areas we intercept. We're different in several ways.

One, NVIDIA'S architecture is general whether you've optimized for unaggressive models or diffusion-based models or vision-based models or multimodal models, or text models. We're great in all of it. We're great on all of it because our software stack is so -- our architecture is sensible. Our software stack ecosystem is so rich that we're the initial target of most exciting innovations and algorithms. And so, by definition, we're much, much more general than narrow.

We're also really good from the end-to-end from data processing, the curation of the training data, to the training of the data, of course, to reinforcement learning used in post-training, all the way to inference with tough time scaling.

So, we're general, we're end-to-end, and we're everywhere. And because we're not in just one cloud, we're in every cloud, we could be on-prem. We could be in a robot. Our architecture is much more accessible and a great target initial target for anybody who's starting up a new company. And so, we're everywhere.

And the third thing I would say is that our performance in our rhythm is so incredibly fast. Remember that these data centers are always fixed in size. They're fixed in size or they're fixing power. And if our performance per watt is anywhere from 2x to 4x to 8x, which is not unusual, it translates directly to revenues. And so, if you have a 100-megawatt data center, if the performance or the throughput in that 100-megawatt or the gigawatt data center is four times or eight times higher, your revenues for that gigawatt data center is eight times higher. And the reason that is so different than data centers of the past is because AI factories are directly monetizable through its tokens generated. And so, the token throughput of our architecture being so incredibly fast is just incredibly valuable to all of the companies that are building these things for revenue generation reasons and capturing the fast ROI. And so, I think the third reason is performance.

And then the last thing that I would say is the software stack is incredibly hard. Building an ASIC is no different than what we do. We build a new architecture. And the ecosystem that sits on top of our architecture is 10 times more complex today than it was two years ago. And that's fairly obvious because the amount of software that the world is building on top of architecture is growing exponentially and AI is advancing very quickly. So, bringing that whole ecosystem on top of multiple chips is hard.

And so, I would say that -- those four reasons. And then finally, I will say this, just because the chip is designed doesn't mean it gets deployed. And you've seen this over and over again. There are a lot of chips that get built, but when the time comes, a business decision has to be made, and that business decision is about deploying a new engine, a new processor into a limited AI factory in size, in power, and in fine. And our technology is not only more advanced, more performance, it has much, much better software capability and very importantly, our ability to deploy is lightning fast.

And so, these things are enough for the faint of heart, as everybody knows now. And so, there's a lot of different reasons why we do well, why we win.

Jensen Huang
With respect to geographies, the takeaway is that AI is software. It's modern software. It's incredible modern software, but it's modern software and AI has gone mainstream.
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And so, I'm fairly sure that we're in the beginning of this new era. And then lastly, no technology has ever had the opportunity to address a larger part of the world's GDP than AI. No software tool ever has. And so, this is now a software tool that can address a much larger part of the world's GDP more than any time in history.

And so, the way we think about growth, and the way we think about whether something is big or small has to be in the context of that. And when you take a step back and look at it from that perspective, we're really just in the beginning.

Jensen Huang
And so, the second part is how do we see the growth of enterprise or not CSPs, if you will, going forward? And the answer is, I believe, long term, it is by far larger and the reason for that is because if you look at the computer industry today and what is not served by the computer industry is largely industrial.

So, let me give you an example. When we say enterprise, and let's use the car company as an example because they make both soft things and hard things. And so, in the case of a car company, the employees will be what we call enterprise and agentic AI and software planning systems and tools, and we have some really exciting things to share with you guys at GTC, build agentic systems are for employees to make employees more productive to design to market plan to operate their company. That's agentic AI.

On the other hand, the cars that they manufacture also need AI. They need an AI system that trains the cars, treats this entire giant fleet of cars. And today, there's 1 billion cars on the road. Someday, there will be 1 billion cars on the road, and every single one of those cars will be robotic cars, and they'll all be collecting data, and we'll be improving them using an AI factory.

Whereas they have a car factory today, in the future, they'll have a car factory and an AI factory. And then inside the car itself is a robotic system. And so, as you can see, there are three computers involved and there's the computer that helps the people. There's the computer that build the AI for the machineries that could be, of course, could be a tractor, it could be a lawn mower.

It could be a human or robot that's being developed today. It could be a building. It could be a warehouse. These physical systems require new type of AI we call physical AI.

They can't just understand the meaning of words and languages, but they have to understand the meaning of the world, friction and inertia, object permanence, and cause and effect. And all of those type of things that are common sense to you and I, but AIs have to go learn those physical effects. So, we call that physical AI.

That whole part of using agentic AI to revolutionize the way we work inside companies, that's just starting.This is now the beginning of the agentic AI era, and you hear a lot of people talking about it, and we got some really great things going on. And then there's the physical AI after that, and then there are robotic systems after that. And so, these three computers are all brand new. And my sense is that long term, this will be by far the larger of a mall, which kind of makes sense.

The world's GDP is represented by either heavy industries or industrials and companies that are providing for those.

We delivered $11.0 billion of Blackwell architecture revenue in the fourth quarter of fiscal 2025, the fastest product ramp in our company’s history. Blackwell sales were led by large cloud service providers which represented approximately 50% of our Data Center revenue.

Gaming revenue for the fourth quarter was down 11% from a year ago and down 22% sequentially, due to limited supply for both Blackwell and Ada GPUs.

Net gains from non-marketable and publicly-held equity securities for the fourth quarter were $727 million, reflecting fair value adjustments and sales of equity investments.

Q3 FY2025 ​

Vivek Arya:
Jensen, my main question, historically, when we have seen hardware deployment cycles, they have inevitably included some digestion along the way. When do you think we get to that phase? Or is it just too premature to discuss that because you're just at the start of Blackwell? So, how many quarters of shipments do you think is required to kind of satisfy this first wave? Can you continue to grow this into calendar '26? Just how should we be prepared to see what we have seen historically, right, a period of digestion along the way of a long-term kind of secular hardware deployment?

Jensen Huang:
The way to think through that, Vivek, is I believe that there will be no digestion until we modernize $1 trillion with the data centers. Those -- if you just look at the world's data centers, the vast majority of it is built for a time when we wrote applications by hand and we ran them on CPUs. It's just not a sensible thing to do anymore. If you have -- if every company's capex -- if they're ready to build data center tomorrow, they ought to build it for a future of machine learning and generative AI because they have plenty of old data centers.

And so, what's going to happen over the course of the next X number of years, and let's assume that over the course of four years, the world's data centers could be modernized as we grow into IT, as you know, IT continues to grow about 20%, 30% a year, let's say. But let's say by 2030, the world's data centers for computing is, call it, a couple of trillion dollars. We have to grow into that. We have to modernize the data center from coding to machine learning.

That's number one. The second part of it is generative AI. And we're now producing a new type of capability the world's never known, a new market segment that the world's never had. If you look at OpenAI, it didn't replace anything.

It's something that's completely brand new. It's, in a lot of ways as when the iPhone came, was completely brand new. It wasn't really replacing anything. And so, we're going to see more and more companies like that.

And they're going to create and generate, out of their services, essentially intelligence. Some of it would be digital artist intelligence like Runway. Some of it would be basic intelligence like OpenAI. Some of it would be legal intelligence like Harvey, digital marketing intelligence like Rider's, so on and so forth.

And the number of these companies, these -- what are they called, AI-native companies, are just in hundreds. And almost every platform shift, there was -- there were Internet companies, as you recall. There were cloud-first companies. There were mobile-first companies.

Now, they're AI natives. And so, these companies are being created because people see that there's a platform shift, and there's a brand-new opportunity to do something completely new. And so, my sense is that we're going to continue to build out to modernize IT, modernize computing, number one; and then number two, create these AI factories that are going to be for a new industry for the production of artificial intelligence.

Q2 FY2025 ​

Our sovereign AI opportunities continue to expand as countries recognize AI expertise and infrastructure at national imperatives for their society and industries.

Remember that computing is going through two platform transitions at the same time and that's just really, really important to keep your mind focused on, which is general-purpose computing is shifting to accelerated computing and human engineered software is going to transition to generative AI or artificial intelligence learned software.

And for NVIDIA's software TAM can be significant as the CUDA-compatible GPU installed-base grows from millions to tens of millions. And as Colette mentioned, NVIDIA software will exit the year at a $2 billion run rate.

Q1 FY2025 ​

We are fundamentally changing how computing works and what computers can do, from general purpose CPU to GPU accelerated computing, from instruction-driven software to intention-understanding models, from retrieving information to performing skills, and at the industrial level, from producing software to generating tokens, manufacturing digital intelligence.

~ Jensen Huang

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