NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

Incredible, right? Just utterly incredible. There's no question that we're in a new computing era. There's just absolutely no question about it. Every single computing era, you could do different things that weren't possible before. And artificial intelligence certainly qualifies. This particular computing era is special in several ways. One, it is able to understand information of more than just text to numbers. It can now understand multi-modality, which is the reason why this computing revolution can impact every industry, every industry. Two, because this computer doesn't care how you program it, it will try to understand what you mean because it has this incredible large language model capability. And so the programming barrier is incredibly low. We have closed the digital divide. Everyone is a programmer now. You just have to say something to the computer. Third, this computer, not only is it able to do amazing things for the future, it can do amazing things for every single application of the previous era, which is the reason why all of these APIs are being connected into Windows applications here and there and browsers and PowerPoint and Word. Every application that exists will be better because of AI. You don't have to just AI this generation. This computing era does not need new applications, it can succeed with old applications. And it's going to have new applications. The rate of progress, the rate of progress, because it's so easy to use, is the reason why it's growing so fast. This is going to touch literally every single industry. And at the core, with -- just as with every single computing era, it needs a new computing approach. In this particular era, the computing approach is accelerated computing, and it has been completely reinvented from the ground up. The last several years, I've been talking to you about the new type of processor we've been creating, and this is the reason we've been creating it. Ladies and gentlemen, Grace Hopper is now in full production. This is Grace Hopper. Nearly 200 billion transistors in this computer [Foreign Language]. Look at this. This is Grace Hopper. This processor is really quite amazing. There are several characteristics about it. This is the world's first accelerated processor, accelerated computing processor that also has a giant memory. It has almost 600 gigabytes of memory that's coherent between the CPU and the GPU. And so the GPU can reference the memory, the CPU can reference the memory and unnecessary -- any unnecessary copying back and forth could be avoided. The amazing amount of high-speed memory lets the GPU work on very, very large data sets. This is a computer. This is not a chip. Practically the entire computer is on here. This uses low-power DDR memory just like your cell phone, except this has been optimized and designed for high resilience data center applications. Incredible levels of performance. This took us several year to develop, and I'm so excited about it. And I'll show you some of the things that we're going to do with it. Janine, thank you. Okay. So 4 petaflops transformer engine, 72 CPU cores, they're connected together by high-speed chip-to-chip link, 900 gigabytes per second. And so the local memory, 96 gigabytes of HBM3 memory, is augmented by LP DDR memory across this link, across a very, very large and high-speed cache. So this computer is like none other the world's ever seen. Now let me show you some of its performance. So I'm comparing here on 3 different applications. And this is a very important application. If you've never heard of it, be sure to look into it, it's called Vector database. Vector database is a database that has tokenized, that has vectorized the data that you're trying to store. And so it understands the relationships of all of the data inside its storage. This is incredibly important for knowledge augmentation of the large language models to avoid hallucination. The second is deep learning recommender systems. This is how we get news and music and all the texts that you see on your devices, recommend, of course, music and goods and all kinds of things. Recommender system is the engine of the digital economy. This is probably the single most valuable piece of software that any of the companies in the world runs. This is the world's first AI factory. There will be other AI factories in the future, but this is really the first one. And then the last one is large language model inference, 65 gigabytes. 65 gigabytes is a fair -- 65 billion parameters is a fairly large language model. And you can see that on a CPU, it's just not possible. The CPU is simply not possible. With Grace Hopper -- excuse me. With Hopper on an x86, it's faster. But notice it's memory limited. You could, of course, take this 400 gigabytes and cut it up into a whole bunch of small pieces, shard it and distribute it across more GPUs and distribute it across more GPUs. But in the case of Grace Hopper, Grace Hopper has more memory on this 1 module than all of these. Does that make sense? And so as a result, you don't have to break the data into so many pieces. Of course, the amount of computation of this is higher, but this is so much easier to use. And if you want to scale out large language models, if you want to scale out vector databases, if you want to scale out deep learning recommender systems, this is the way to do it. This is so easy to use, plug this into your data center, and you can scale out AI, okay? So this is the reason why we built Grace Hopper. The other application that I'm super excited about is the foundation of our own company. NVIDIA is a big customer of Cadence. We use all of their tools, and all of their tools run on CPUs. And the reason why they run on CPUs is because NVIDIA's data sets are very large. And the algorithms are refined over very long periods of time. And so most of the algorithms are very CPU-centric. We've been accelerating some of these algorithms with Cadence for some time. But now with Grace Hopper and we've only been working on it for a couple of days and weeks. The performance speed up, I can't wait to show it to you, is insane. This is going to revolutionize an entire industry. One of the highest compute-intensive industries in the world, of course, designing chips, designing electronic systems, CAE, CAD, EDA and of course, digital biology. All of these markets, all of these industries require very large amounts of computation, but the data set is also very large. Ideal for Grace Hopper. Well, 600 gigabytes is a lot, 600 gigabytes is a lot. This is basically a supercomputer I'm holding in my hands. This 600 gigabytes is a lot. But when you think about it, when we went from AlexNet of 62 million parameters 12 years ago and trained on 1.2 million images, it is now 5,000x bigger with Google's PaLM. 5,000x bigger with 340 billion parameters. And of course, we're going to make even bigger ones than that. And that's been trained on 3 million times more data. So literally, in the course of 10 years, the computing problem of deep learning increased by 5,000x for the software and 3 million times for the data set. No other area of computing has ever increased this fast. And so we've been chasing the deep learning advance for quite some time. This is going to make a big, big contribution. However, 600 gigabytes is still not enough. We need a lot more. So let me show you what we're going to do. So the first thing is, of course, we have the Grace Hopper Superchip, put that into a computer. The second thing that we're going to do is want to connect 8 of these together using NVLINK. This is an NVLINK switch. So 8 of this connect into 3 switch trays into 8 Grace Hopper pod. These 8 Grace Hopper pods, each 1 of the Grace Hoppers are connected to the other Grace Hopper at 900 gigabytes per second. 600 gigabytes, 900 megabytes per second, 8 of have them connected together as a pod. And then we connect 32 of them together with another layer of switches. And in order to build in order to this, 256 Grace Hopper superchips connected into 1 ExaFLOPS, 1 ExaFLOPS. You know that countries and nations have been working on ExaFLOPS computing, and just recently achieved it. 256 Grace Hoppers for deep learning is 1 ExaFLOPS transformer engine, and it gives us 144 terabytes of memory that every GPU can see. This is not 144 terabytes distributed. This is 144 terabytes connected. Why don't we take a look at what it really looks like? Play it, please. [Presentation]

This is 150 miles of cables, fiberoptic cables, 2,000 fans, 70,000 cubic feet per minute. It probably recycles the air in this entire room in a couple of minutes. 40,000 pounds, 4 elephants, 1 GPU. If I can get up on here, this is actual size. I wonder if this can play Crysis. Only gamers know that joke. So this is this is our brand-new Grace Hopper AI supercomputer. It is 1 giant GPU. Utterly incredible. We're building in now all of the -- every component is in production. And we're so excited that Google Cloud, Meta and Microsoft will be the first companies in the world to have access, and they will be doing exploratory research on the pioneering front, the boundaries of artificial intelligence, with us. We will, of course, build these systems as products. And so if you would like to have an AI supercomputer, we would, of course, come and install it in your company. We also share the blueprints of the supercomputer with all of our cloud suppliers, so that our cloud partners so that they can integrate it into their networks and into their infrastructure. And we will also build it inside our company for us to do research ourselves and do development. So this is the DGX GH200. It is 1 giant GPU. Okay. 1964, the year after I was born, was a very good year for technology. IBM, of course, launched the System/360, and AT&T demonstrated to the world the first picture phone and coated, compressed, streamed over copper telephone wires and twisted pair and on the other end, decoded. Picture phone, little tiny screen, black and white. To this day, this very experience is largely the same, of course, at much, much higher volumes. For all of the reasons we all know well, video calls is now one of the most important things we do. Everybody does it. About 65% of the Internet's traffic is now video. And yet the way it's done is fundamentally still the same. Compress it on the device, stream it and decompress it on the other end. Nothing changed in 60 years. We treat communications like it goes down to a dumb pipe. The question is, what would happen if we applied generative AI to that? We have now created a computer. I showed you, Grace Hopper. It can be deployed broadly all over the world easily. And as a result, every data center, every server will have generative AI capability. What would happen if instead of decompression, streaming and recovering compression, decompression, what if the cloud performed generative AI capability to it? Let's take a look. [Presentation]

[Foreign Language]. Okay. So all of the words coming out of my mouth, of course, was generated by AI. So instead of compression, stream and decompression, in the future, communications will be perceive, stream and reconstruction, regeneration. And it can be generated in all kinds of different ways. It can be generated in 3D of course. It can regenerate your language in another language. So we now have a universal translator. This computing technology could be, of course, placed into every single cloud. But the thing that's really amazing, Grace Hopper is so fast, it can even run the 5G stack. A state-of-the-art 5G stack could just run in software in Grace Hopper, completely free, completely free. All of a sudden, a 5G radio runs in software, just like a video codec used to run in software. Now you can run a 5G stack in software. Of course, the layer 1 PHY layer, the layer 2 MAC layer, and the 5G core, all of that computation is quite intensive, and it has to be timing-precise, which is the reason why we have a BlueField-3 in the computer. So that kind of precision timing networking. But the entire stack can now run in a Grace Hopper. Basically, what's happening here, this computer that you're seeing here allows us to bring generative AI into every single data center in the world today. Because we have software-defined 5G, then the telecommunication network can also become a computing platform like the cloud data centers. Every single data center in the future could be intelligent, every data center could be software-defined whether it's Internet-based, networking-based or 5G communications-based. Everything will be software defined. This is really a great opportunity. And we're announcing a partnership with SoftBank to partner to re-architect and implement generative AI and software-defined 5G stack into the network of SoftBank data centers around the world. Really excited about this collaboration. I just talked about how we are going to extend the frontier of AI. I talked about how we're going to scale out generative AI, to scale out generative AI to advance generative AI. But the number of computers in the world is really quite magnificent, data centers all over the world. And all of them over the next decade will be recycled and reengineered into accelerated data centers and generative AI-capable data centers. But there are so many different applications in so many different areas. Scientific computing, data processing, large language model training that you've been -- we've been talking about generative AI inference that we just talked about. Cloud and video and graphics. EDAs, SDA, which we just mentioned, generative AI for enterprise and of course, the edge. Each one of these applications have different configurations of servers, different focus of applications, different deployment methods. And so security is different. Operating system is different, how it's managed is different, where the computers are will be different. And so each one of these diverse application spaces will have to be reengineered with a new type of computer. Well, this is just an enormous number of configurations. And so today, we're announcing in partnership with so many companies here in Taiwan, the NVIDIA MGX. It's an open modular server design specification and is designed for accelerated computing. Most of the servers today are designed for general purpose computing. The mechanical, thermal and electrical is insufficient for a very highly dense computing system. Accelerated computers take, as you know, many servers and compress it into 1. You save a lot of money, you save a lot of floor space, but the architecture is different. And we designed it so that it's multi-generation standardized so that once you make an investment, our next-generation GPUs and next generation CPUs and next-generation DPUs will continue to easily configure into it so that we kind of best time to market and best preservation of our investment. We could -- configurable into hundreds of configurations for different diversities and different diverse applications and integrate into cloud or enterprise data center. So you could have either busbar or power regulators, you could have cabling in the hot aisle or cabling in the cold aisle. Different data centers have different requirements, and we've made this modular and flexible so that it could address all of these different domains. Now this is the basic chassis. Let's take a look at some of the other things you could do with it. This is the Omniverse OVX server, that has x86, 4 L40s, BlueField-3, 2 CX7s, 6 PCI Express slots. This is the Grace Omniverse server. Grace, same, 4 L40s, BF3, BlueField-3 and 2 CX7s, okay? This is the Grace cloud graphic server. This is the Hopper NVLINK generative AI inference server, and we need sound effects like, like that. And then Grace Hopper 5G Aerial server, okay, for telecommunications, software-defined telco. Grace Hopper 5G Aerial Server Short. And of course, Grace Hopper Liquid Cooled, okay, for very dense servers. And then this 1 is our dense general-purpose Grace superchip server. This is just CPU and has the ability to accommodate 4 CPU -- 4 Grace CPUs or 2 Grace superchips, enormous amounts of performance. And if you were -- if you're a data center is powered limited, this has incredible capabilities. In a power limited environment, running PageRank, and there's all kinds of benchmarks you can run, but we ran PageRank in iSO performance, in iSO performance, Grace only consumes 580 watts for the whole server versus the latest generation CPU servers, x86 servers, 1,090 watts. It's basically half the power at the same performance or another way of saying, at the same power, if your data center is power constrained, you get twice the performance. Most data centers today are Power Limited. And this is really a terrific capability. There are all kinds of different servers that are being made here in Taiwan. Let me show you one of them. This is from Supermicro, get my exercise in today. I am the sound effect, okay. All right so this is a Supermicro server, you've got BlueField-3, got the CX7, you got the Grace Hopper. Okay. But there are so many systems. There are so many systems. So this is Grace Hopper, that's Supermicro. There are so many systems. Let me show you some of them. And all of our partners, I'm so grateful. You're working on Grace, Grace Hopper, Hopper, L40s, L4s, BlueField-3s, just about every single processor that we're building are configured into the servers of all different types. And so this is Supermicro. This is Gigabyte. The 10s, I think it's like 70 different server configurations. This is Ingrasys, this is ASRock, Tyan, Wistron, Inventec. It's beautiful servers, PEGATRON. We love servers. I love servers. They're beautiful. They're beautiful to me. QCT, ASUS, Wiwynn, ZT Systems. And this ZT system, what you're looking at here is one of the pods of our Grace Hopper AI supercomputer. So I want to thank all of you. I want to thank all of you for your great support. Thank you. We're going to expand AI into a new territory. If you look at the world's data centers, the data center is now the computer, and the network defines what that data center does. Largely, there are 2 types of data centers today. There's the data center that's used for hyperscale where you have application workloads of all different kinds. The number of CPUs, the number of GPUs you connect to it is relatively low. The number of tenants is very high. The workload is heterogeneous. The workloads are loosely coupled and you have another type of data center. They're like supercomputing data centers, AI supercomputers, where the workloads are tightly coupled. The number of tenants far fewer and sometimes just 1. Its purpose is high throughput on very large computing problems, okay? And it's basically a stand-alone. It's basically a stand-alone. And so supercomputing centers and AI supercomputers and the world's cloud, hyperscale cloud are very different in nature. Ethernet is based on TCP. It's a lossy algorithm, and it's very resilient. And whenever there's a loss, packet loss, it retransmits. There's error correction that's done. It knows which one of the packets are lost and request the center to retransmit it. The ability for Ethernet to interconnect components of almost from anywhere is the reason why the world's Internet was created. If it required too much coordination, how could we have built today's Internet? So Ethernet's profound contribution, it's this lossy capability, its resilient capability. And because so it basically can connect almost anything together. However, a supercomputing data center can't afford that, you can't interconnect random things together because that $1 billion supercomputer, the difference between 95% networking throughput achieved versus 50% is effectively $500 million. So the cost of that 1 workload running across that entire supercomputer is so expensive that you can't afford to lose anything in the network. InfiniBand is our -- relies on RDMA very heavily. It is a flow control so it's a lossless approach. It requires flow control, which basically means you have to understand the data center from end to end, to switch to the NIC to the software so that you can orchestrate the traffic with adaptive routing so that you could deal with congestion control and avoid the oversaturation of traffic in an isolated area, which results in packet loss. You simply can't afford that because in the case of InfiniBand, it's lossless. And so one is lossy, the other one is lossless, very resilient, very performant. These 2 data centers have lived separate lives. These 2 data centers have lived separate lives. But now we would like to bring generative AI to every data center. The question is how. The question is, how do we introduce a new type of Ethernet that's, of course, backwards compatible with everything but it's engineered in a way that achieves the type of capabilities that we can bring AI workloads to the world's any data center. This is a really exciting journey. And at the core of this strategy is a brand-new switch that we've made. This is the Spectrum-4 switch. And this switch -- everything I'm showing today are very heavy. This is the Spectrum-4 switch, 128 ports, 400 gigabits per second. 128 ports of 400 gigabits per second, 51.2 terabits per second. This is the chip. It's gigantic, 100 billion transistors, 90 millimeters by 90 millimeters, 800 balls on the bottom. This is a 500-watt chip. This switch is 2,800 watts. It's air cooled. There are 48 PCBs that connect the switch together, 48 PCBs that build up the switch. And the switch is designed to enable a new type of Ethernet. Remember what I said, InfiniBand is fundamentally different in the sense that we build InfiniBand from end to end so that we could do adaptive routing so that we could do congestion control so that we can isolate performance, and we could keep noisy neighbors apart so that we could do in-fabric computing. All of these capabilities are simply not possible in a lossless approach of the Internet, and of course, of Ethernet. And so the way that we do InfiniBand is design it from end to end, just the way supercomputers are built, this is the way AI supercomputers are built. And we are going to do the same thing now for the very first time for Ethernet. We've been waiting for the critical part and the critical part is the Spectrum-4 switch. The entire system consists of several things. So our new Ethernet system for AI is this. The Spectrum-4 switch and the BlueField-3 Smart NIC or DPU. This BlueField-3 is 400 gigabits per second NIC. It connects directly to the Spectrum-4 switch in combination of 4 things: the switch, the BlueField-3, the cablings that connect them together, which are super important and the software that runs it all together represents the Spectrum-4. This is what it takes to build a high-performance network, and we're going to take this capability to the world's CSPs. The reception has been incredible. And the reason for that is, of course, every CSP, every data center would like to turn every single data center into a generative AI data center. There are some people that need -- they deployed Ethernet throughout their company. And they have a lot of users for that data center. The ability to have the capabilities of InfiniBand and isolating it within their data center is very difficult to do. And so for the very first time, we're bringing the capabilities of high-performance computing into the Ethernet market. And we're going to bring to the Ethernet market several things. First, adaptive routing. Adaptive routing basically says based on the traffic that is going through your data center, depending on which one of the parts of that switch is overcongested, it will tell BlueField-3 to -- and will send it to another port. BlueField-3 on the other end, would reassemble it and present the data to the CPU -- present the data to the computer to the GPU without any CPU intervention. All completely in RDMA. Number one, adaptive routing. Second, congestion control. Congestion control, it is possible for certain different ports to become heavily congested, in which case the telemetry of the switch. Each switch will see how the network is performing and communicate to the senders, please don't send any more data right away because you're congesting the network. That congestion control requires basically an overriding system, which includes software, the switch, working with all of the end points to overall manage the congestion or the traffic and the throughput of the data center. Now it's really important to realize that in a high-performance computing application, every single GPU must finish their job so that the application can move on. In many cases where you do all reductions, you have to wait until the results of every single 1. So if 1 node takes too long, everybody gets held back. This capability is going to increase Ethernet's overall performance dramatically. So Spectrum-X, really excited to roll this out. The world's applications, the world's enterprise has yet to enjoy generative AI. So far, we've been working with CSPs and the CSPs, of course, is going to be able to bring generative AI to many different regions and many different applications and industries. The big journey is still ahead of us. There are so many enterprises in the world, and everybody because of the multi-modality capability that I was mentioning before, every industry can now benefit from generative AI. There are several things that we have to do. Number one, we have to help the industries build custom language models. Not everybody can use the language models that are available in a public service. Some customers need language models that are highly specialized for their particular modality, for example, proteins or chemicals. Each one of these industries have proprietary information, and so how can we help them do that? We have created a service called NVIDIA AI Foundation. It is a cloud service that captures NVIDIA's AI expertise and makes it possible for you to train your own AI models. We will help you develop your own AI models with supervised fine tuning, with guard railing, with proprietary knowledge bases and reinforcement learning human feedback so that this AI model is perfect for your application. We then deploy this model to run on NVIDIA AI enterprise. This is the operating system that I was talking to you about earlier. This operating system runs in every single cloud. This allows -- this very simple system with NVIDIA AI foundation for training large language models and deploying the language model into NVIDIA AI enterprise, which is available in every single model, every single cloud and on-prem. Allows every single enterprise to be able to engage. Now one of the things that very few people realize is that today, there's only 1 software stack that is enterprise secure and enterprise grade. That software stack is CPU. And the reason for that is because in order to be enterprise grade, it has to be enterprise secure and has to be enterprise managed and enterprise supportive across its entire life, across its life cycle. There are so much software in accelerated computing. 4 -- over 4,000 software packages is what it takes for people to use accelerated computing today in data processing and training and optimization all the way to inference. So for the very first time, we are taking all of that software, and we're going to maintain it and manage it like Red Hat does for Linux. NVIDIA AI enterprise will do it for all of NVIDIA's libraries. Now enterprise can finally have an enterprise-grade and enterprise-secure software stack. This is such a big deal. Otherwise, even though the promise of accelerated computing is possible for many researchers and scientists is not available for enterprise companies. And so let's take a look at the benefit for them. This is a simple image processing application. If you were to do it on a CPU versus on a GPU, running on enterprise NVIDIA AI enterprise, you're getting 31.8 images per minute or basically 24x the throughput, or you only pay 5% of the cost. This is really quite amazing. This is the benefit of accelerated computing in the cloud. But for many companies, enterprises is simply not possible unless you have the stack. NVIDIA AI Enterprise is now fully integrated into AWS, Google Cloud and Microsoft Azure and Oracle Cloud. And so when you go and deploy your workloads in those clouds and you want software that is enterprise grade or you have customers that need enterprise-grade software, NVIDIA AI Enterprise is ready for you. It is also integrated into the world's machine learning operations pipeline. As I mentioned before, AI is a different type of workload, and this new type of software, this new type of software has a whole new software industry. And this software industry, 100% of them, we have now connected with NVIDIA AI Enterprise. Now let me talk to you about the next phase of where AI meets the digital twin. Now why does AI need a digital twin? I'm going to explain that in a second. But first, let me show you what you can do with it. In order for AI to have a digital twin, in order for AI to understand heavy industry, remember, so far, AI has only been used for light industry, information, words, images, music, so on and so forth. If we want to use AI for heavy industry, the $50 trillion of manufacturing, many of that you're part of. The trillions of dollars of health care. All of the different manufacturing sites, whether you're building chip fabs or battery plants or electric vehicle manufacturing factories, all of these would have to be digitized in order for artificial intelligence to be used to automate, to design, to build and to automate the future of your business. And so the first thing that we have to do is we have to create the ability for their world to be represented in digital, okay? So number 1 is digitalization. Well, why does it -- how would you use that? So let me give you just a simple example. In the future, you would say to your robot, I would like you to do something, and the robot will understand your words, and it would generate animation. Remember, I said earlier, you can go from text to text. You can go from text to image. You can go from text to music. Why can't you go from text to animation? And so of course, in the future, robotics will be highly revolutionized by the technology we already have in front of us. However, how does this robot know that the motion that it is generating is grounded in reality? It is grounded in physics. You need a software system that understands the laws of physics. Now you've actually seen this already with ChatGPT. Whereas AI, NVIDIA AI would use NVIDIA Omniverse in a reinforcement learning loop to ground itself, you have seen ChatGPT do this using reinforcement learning, human feedback. Using human's feedback, ChatGPT was able to be developed by grounding it to human's, well, sensibility and align it with our principles. So reinforcement learning with human feedback is really important. Reinforcement learning for physics feedback is very important. Let me show you. Everything that you're about to see as a simulation. Let's roll it, please. [Presentation]

Everything was a simulation, nothing was art. Everything was simulation. Isn't that amazing? In the last 25 years, I come to Taiwan, you sell me things. Omniverse will be the first thing I'm going to sell you. And this is -- because this will help you revolutionize your business and turn it into a digital business and automate it with AI. You will build products in digital first before you make it physical. You will build factories and plan it in digital first before you make it in physical. And so in the future, Omniverse is a big deal. Now I'm going to show you very quickly Omniverse in the cloud. Omniverse, the entire stack, is so complicated. And so we put the whole thing into a cloud managed service, and it's hosted in Azure. This particular experience you're going to have, the computer is in California. And [ Sean ]? I'm sorry, I took so much time, so you're going to have to...

I'll go quick. So this is -- let's take a look at the Omniverse Cloud. So this is just a web browser, and we're looking now into Omnibus factory Explorer. It's running 10,000 kilometers away in our Santa Clara headquarters and we're leveraging the power of our data center now to visualize this factory floor. We're using real factory data from Siemens and Autodesk Revit to take a look. It's a cloud application, so we could have multiple users collaborating. Let's go ahead and bring up LOE screen. And we can see that we have these 2 users in this environment. And [ Jeff ], on the left there is going to look at some markup. We have this task to perform. We need to move this object. So we can have [ Louis ], just go ahead and grab that conveyor belt, move it over. And as he does so, you'll see that it's reflected accurately and completely in real time on [ Jeff's ] screen. So we're able to collaborate with multiple users. And even in bringing up this demo, we had users from around the globe working on the process. East and West Coast, United States, Germany, even Sydney and course here in Taipei, to put this together. Now as we -- if we're modifying our production line, of course, one of the things we want to do is add the necessary safety equipment. So we're able to simply drag and drop items into Omniverse and modify this production environment and begin tweaking this and optimizing for performance even before we break around with construction.

That was so cool. This is in California, 6,264 miles away or something like that. 34 milliseconds by speed of light 1 way. And it's completely interactive. Everything is ray traced. No artist necessary, you bring everything, the entire CAD into Omniverse. Open up a browser, bring your data in, bring your factory in, no artist necessary. The lighting just does what the lighting does, physics does what the physics does. If you want to turn off physics, you can. If you want to turn on physics, you can. And multiple users, as many as you like, can enter the Omniverse at the same time and work together. One unified source of data across your entire company. You could virtually build, you can virtually design and build, and operate your factory before you break ground and not make the mistake, which usually in the beginning of the integration creates a lot of change orders, which cost a lot of money. Thank you very much, [ Sean ]. Good job. Not only -- noticed just now, it was humans interacting with Omniverse. Humans interacting with Omniverse. In the future, [ Sean ] will even have a generative AI, and AI interact with him in Omniverse. We could, of course, imagine in the very beginning, there was [ Jin ]. That could be a character. That could be one of the users of Omniverse, interacting with you, answering questions, helping you. We can also use generative AI to help us create virtual worlds. So for example, this is a bottle that's rendered in Omniverse. It could be placed in a whole bunch of different type of environments, it can render beautifully physically. You can place it just by giving it a prompt by saying, I would like to put this life -- these bottles in a lifestyle photograph style backdrop for a modern, warm farmhouse bathroom, change the background, everything is all integrated and rendered again, okay? So generative AI will come together with Omniverse to assist the virtual -- the creation of virtual worlds. Today, we're announcing that WPP, the world's largest advertising agency and advertising services company is partnering with NVIDIA to build a content generation engine based on Omniverse and generative AI. It integrates tools from so many different other partners, Adobe Firefly, for example, Getty, Shutterstock. And it integrates into this entire environment. And it makes it possible for them to generate unique content for different users for ad applications, for example. So in the future, whenever you engage in particular ad, it could be generated just for you. But yet the product is precisely rendered because, of course, the product integrity is very important. And so every time that you engage a particular ad in the future, today, it was retrieved. And today, the computing model, when you engage information, it is retrieved in the future. When you engage information, much of it will be generated. Notice the computing model has changed. WPP generates 25% of the ads that the world sees. 60% of the world's largest companies are already clients. And so they made a video of how they would use this technology. [Presentation]

No problem. We continue. Okay. So that's WPP. You could see that, that was an example -- if you think about it a second, that's an example of a company using digital information that was created in design and using that digital information all the way in marketing. I'm going to show you now how we're going to use Omniverse and AI here in Taiwan, and we're going to use it for manufacturing. Manufacturing as you know, is one of the largest industries in the world. We're going to use Omniverse to teach an AI. And then we're going to use Metropolis, our AI deployment, edge deployment system, to deploy the AI. Okay, run it. [Presentation]

Did you see that the whole factory is an Omniverse? It's completely digital. Imagine if you have digital information in your hands. What can you do with it? Almost everything. And so this is one of the things that's really exciting. What you just saw is basically every factory in the future will be digital, of course, first. Every factory will be a robot. Inside the factories, there will be other robots. The factory is orchestrating. We are also going to build robots that move themselves. So far, the robots that you saw are stationary. Now we're going to also have robots that move. Everything that move in the future will have artificial intelligence and will have robotic capability. And so today, we're announcing our robot platform, NVIDIA Isaac AMR is now available as a reference design for anybody who wants to build robots, just like we did with our high-performance computing. NVIDIA builds the whole stack. And then we disaggregate it so that if you would like to buy the chip, that's fine, if you like to buy the system, that's fine, if you like to use your software that's fine. If you like to use your software, that's fine. If you like to use your own algorithm, that's terrific. If you'd like to ours, that's terrific. However you would like to work with us, we're open for business so that we can help you integrate accelerated computing wherever you like. In the future, we're going to do the same with robotics. We built the entire robotic stack top to bottom from the chip, to the algorithms, we have state-of-the-art perception for multi-modality sensor, state-of-the-art mapping, state-of-the-art localization and planning and a cloud mapping system, everything has been created. However you would like to use it, you can use pieces of it. It's open available for you, including the cloud mapping systems. So this is Isaac AMR. It includes starts with the chip called Orin. It goes into a computer, and it goes into the NVIDIA Orin -- Nova Orin which is a reference system, a blueprint for AMRs. This is the most advanced AMR in the world today. And that entire stack has been built and let's take a look at it. [Presentation]

Nova cannot tell that it is not in the real environment. Nova thinks it is in the real environment. It cannot tell. And the reason for that is because all the sensors work, physics work, it can navigate, it can localize itself. Everything is physically based. So therefore, we could design the robot, simulate the robot, train the robot, all in Isaac. And then we take the brain, Isaac Sim, then we take the brain, the software and we put it into the actual robot. And with some amount of adaptation, it should be able to perform the same job. This is the future robotics, Omniverse and AI working together. The ecosystem that we have been in the IT ecosystem is $0.25 trillion per year. $250 billion a year. This is the IT industry. For the very first time in our history together, we finally have the ability to understand the language of the physical world. We can understand the language of heavy industry. And we have a software tool, we have a software system called Omniverse that allows us to simulate, to develop, to build and operate our physical plants, our physical robots, our physical assets, as if they were digitally. The excitement in the hard industries, the heavy industries has been incredible. We have been connecting Omniverse all over the world with tools companies, robotics company, sensor companies, all kinds of industries. There are 3 industries right now as we speak, that is putting enormous investments into the world. Number one, of course, is chip industry; number two, electric battery industry; number three, electric vehicle industry. Trillions of dollars will be invested in the next several years, trillions of dollars will be invested in the next several years. And they would all like to do it better, and they would like to do it in a modern way. For the very first time, we now give them a system, a platform, tools that allows them to do that. I want to thank all of you for coming today. I talked about many things. It's been a long time since I've seen you, so I had so much to tell you. There was too much. It was too much. Last night, I said, this is too much. This morning I said, this is too much. And now I realize, it's too much. [Foreign Language]. I told you several things. I told you that we are going through 2 simultaneous computing industry transition, accelerated computing and generative AI. Two, this form of computing is not like the traditional general-purpose computing. It is full stack. It is data center scale because the data center is the computer, and it is domain specific. For every domain that you want to go into, every industry you go into, you need to have the software stack. And if you have the software stack, then the utility, the utilization of your machine, the utilization of your computer will be high. So number two, it is full stack data center scale and domain specific. We are in full production of the engine of generative AI, and that is HGX H100. Meanwhile, this engine that's going to be used for AI factories will be scaled out using Grace Hopper, the engine that we created for the era of generative AI. We also took Grace Hopper and realized that we can extend on the one hand, the performance, but we also had to extend the fabric so that we can make larger models trainable. And we took Grace Hopper connected to 256 node NVLINK and created the largest GPU in the world DGX GH200. We're trying to extend generative AI and accelerated computing in several different directions at the same time. Number one, we would like to, of course, extend it in the cloud so that every cloud data center can be an AI data center. Not just AI factories and hyperscale, but every hyperscale data center can now be a generative AI data center. And the way we do that is the Spectrum-X. It takes 4 components to make Spectrum-X possible. The switch, the BlueField-3 NIC, the interconnects themselves. The cables are so important in high-speed communications, and the software stack that goes on top of it. We would like to extend generative AI to the world's enterprise. And there are so many different configurations of servers and the way we're doing that with partnership with our Taiwanese ecosystem, the MGX modular accelerated computing systems. We put NVIDIA in the cloud so that every enterprise in the world can engage us to create generative AI models, and deploy it in a secure way, in an enterprise-grade, enterprise secure way in every single cloud. And lastly, we would like to extend AI to the world's heavy industries, the largest industries in the world. So far, our industry, our industry, that all of us have been part of, has been a small part of the world's total industry. For the very first time, the work that we're doing can engage every single industry and do that by automating factories, automating robots, and today, we even announced our first robotics full reference stack, the Nova Orin. I want to thank all of you for your partnership over the years. Thank you.