Good morning. My name is Michelle, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's GTC Financial Analysis Event. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question-and-answer session. If you would like to ask a question during this time, simply press star followed by one on your telephone keypad. If you would like to withdraw your question, please press the pound key. Simona Jankowski, you may begin your conference.
Thank you. Hi, everyone, and thank you for joining us for our GTC Financial Analyst event. We hope you are all able to view Jensen's GTC keynote address this morning. We're excited for this opportunity to spend some more time with the investment community unpacking all of our announcements. Before I go over introductions and today's agenda, let me remind you that during this presentation, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to our most recent Forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission.
All our statements are made as of today, October 5th, 2020, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. With that, we're ready to get started. We have six speakers for you today who will cover the highlights from this morning's announcements and what they mean for our business. NVIDIA founder and CEO, Jensen Huang, will kick us off with an overview of the NVIDIA computing platform and strategy. Next, we'll have four speakers who will cover important aspects of our data center market platform. We'll wrap up with our CFO, Colette Kress, before opening up to Q&A. We expect the entire program to wrap up in about two hours. With that, let me turn it over to Jensen.
Hi, everyone. This is a very special GTC because many new platforms and products we've been working on came together. The strategic themes NVIDIA is driving will reverberate throughout GTC. AI is the most powerful technology force of our time. Software writing software. The age of AI. The automation of automation. The age of AI will open large untapped markets. Accelerated computing is a full stack challenge. It starts with a great chip. The stack is a lot more complicated than that. Accelerated computing platforms takes on more than a chip, just as cloud computing platforms take more than a server. The new unit of computing is the data center, whether because cloud-native applications run across an entire data center or because edge computing will have a whole data center on the chip someday. The iPhone moment of industrial companies is here.
AI services will span cloud to edge. NVIDIA AI fully supports x86 today. We also wanna bring NVIDIA AI and accelerated computing to ARM, the world's most popular CPU, and offer our architecture to ARM's vast ecosystem. Simply, we are building the computing company for the age of AI. We're focused on five major domains. The applications in each domain share similarities in algorithm, use cases, system platforms, ecosystems, or/and market. These five domains are NVIDIA RTX, NVIDIA HPC, NVIDIA AI, NVIDIA Enterprise AI, and NVIDIA Edge AI. Let me say a few words about each one of them. NVIDIA RTX is inventing the future of graphics and digital worlds. RTX is a massive endeavor, reinventing real-time graphics, requiring us to fundamentally change every layer of the stack from top to bottom. RTX was high risk, but it is now clearly a home run.
Ampere is our second-generation RTX. We expected and prepared for great demand. The 30 series ramp is our fastest ever. The demand is exceeding our expectation. We also announced Omniverse, our physically based simulation and collaboration platform. A platform for creating digital worlds is in open beta. NVIDIA AI is in full throttle. Ampere swept the latest MLPerf training benchmark, demonstrating our growing lead. Ampere is our fastest ever data center ramp. This is the first-generation that required no explaining whatsoever to any of the customers, OEMs or cloud data centers of the need of NVIDIA GPUs in data centers. They only ask when they can take Ampere to market. Today, we announced the new NVIDIA RTX A6000 and NVIDIA A40 enterprise and data center Ampere-based GPUs. These are PCI Express based and complement the already in full production NVIDIA A100.
We also announced NVIDIA Jarvis conversational AI SDKs in open beta. NVIDIA Merlin Recommender SDK is in open beta. These are two of the most important AI models in the world today. We just also announced the NVIDIA Maxine SDK for cloud AI video processing. Maxine will help video conference services take advantage of NVIDIA GPUs and NVIDIA AI in the cloud. Live video is one of the most active, most busy traffic in the internet today. Finally, we announced that our stacks are so popular that we're working to bring all of them, inclusively in NGC, our cloud registry, into each cloud marketplace. Essentially like a store within a store. The next wave of AI is the enterprise. Enterprises will use AI to automate their company and use AI to bring automation to the products and services their companies build.
The latter is also called industrial IoT or Edge AI. It comes in several names. Imagine a John Deere autonomous tractor connected to a John Deere cloud service, or an autonomous Mercedes-Benz connected to their cloud service, or a connected air conditioner, street sweeper, or an entire building connected to AI services. This is the iPhone moment for the world's industries. AI will automate the world's largest industries. Breakthroughs in AI have made the automation software possible. NVIDIA Enterprise AI is about helping companies build modern, secure, NVIDIA-accelerated data centers and offering the software platforms to help each major industry apply AI. Manuvir will talk about NVIDIA EGX Enterprise, the new NVIDIA BlueField data center infrastructure on a chip platform, and our vision for enterprise AI.
NVIDIA Edge AI is about helping companies build modern, secure, NVIDIA-accelerated edge data centers in a box with software stacks that let customers operate their network like a fleet, and software platforms to help major industries create and operate AI services. Today, we announced Fleet Command, a software-as-a-service offering to help operate these fleets. Another recent example, AI-powered connected product and services, is our Mercedes-Benz partnership. Our development with Mercedes-Benz is in full throttle. Mercedes will be using our entire stack from infrastructure, AV computer in the car, to the driving application. Justin will talk about NVIDIA EGX Edge and the wave of partners joining us. NVIDIA HPC consists of supercomputing centers and industrial HPC. Industrial HPC demands accelerating a large number of domain-specific applications. For example, healthcare domain. Representing over a decade of work, we created a state-of-the-art suite of accelerated tools to help medical researchers discover life-saving drugs.
We announced NVIDIA Clara Discovery. Kimberly will talk about the great work we're doing in drug discovery and our partnerships there. This is our biggest GTC ever. Over 1,000 sessions, a record number of sponsors, a record number of startups participating. We announced 80 SDKs. These SDKs are the critical difference between a GPU chip and the NVIDIA accelerated computing platform on GPU. These SDKs run on NVIDIA's 1 billion CUDA GPUs. One architecture, gigantic install base for developers. These SDKs cover the range of NVIDIA's full stack computing platforms. Chips and architectures like CUDA, GPU, and DOCA for DPU. Systems or systems components, RTX, DGX, HGX for hyperscalers, EGX for enterprise and edge, AGX for autonomous machines. System software APIs for Windows, Android, QNX, Linux, Kubernetes, VMware. For PCs, cloud, enterprise edge.
Acceleration libraries and engine, like the CUDA-X libraries, Magnum IO, cuDNN, TensorRT, Triton Inference Server, the RTX stack, our Physics Engine, and of course, applications and application frameworks. Omniverse, NVIDIA DRIVE, Jarvis, Merlin, Isaac, our robotics stack, or Clara, our computational healthcare and life sciences stack. All of these stacks are optimized and containerized on NGC. The NVIDIA SDKs are created in service of our nearly 2.5 million developers, researchers, software companies who accelerated the 1,800 applications for the billions of computer users, the global computer makers, cloud service providers, solution partners, the 6,500 startups in Inception. We built GTC for them. We built our SDKs for them in service of them. They are what GTC is all about. To tell you more about our announcements at GTC, we've got some very special speakers lined up for you guys.
Let me first introduce Paresh Kharya to tell you about NVIDIA AI. Paresh?
Thank you, Jensen. Jensen talked about how AI is software writing software, and AI is already achieving results that no human-written software can. The interesting thing is this approach is extremely scalable. Larger, more complex models create more capable AI that's more accurate and applicable for many different types of tasks. The chart on your left basically shows the number of days it takes to train a model on a one petaflop supercomputer or a computer, and it continues to grow exponentially. It's now doubling every couple months. Think of it another way. If Moore's Law were still true, it would have delivered every 10 years what AI needs every 10 months. Larger models will need larger and larger supercomputers to train on, expanding the opportunity for NVIDIA. The capabilities of these advanced AI models and their potential to transform the industries is immense.
On the right, you see a few recent examples of it. NVIDIA's natural language understanding AI took the RACE reading comprehension challenge that consists of middle school and high school level tests. The average human score on it was 73%. NVIDIA's Megatron-BERT scored 91%. In addition, to reading comprehension, language models are also being used to predict the 3D structure of protein just by reading the amino acid sequences. This will have a dramatic impact in discovering drugs. Kimberly will talk a bit more about it later. Facebook AI Research developed a very large AI model based chatbot that exhibits knowledge, personality, and empathy. They call it BlenderBot. Half of the users tested preferred chatting with BlenderBot over humans. In another work, researchers at Caltech developed reinforcement learning-based drone flight control systems.
They can smoothly land drones with a payload on different surfaces, a critical problem to solve for making safe drone deliveries. Larger and advanced models bring transformative capabilities, and when deployed in applications, they also require GPUs for inference for the economics to work for companies. Take, for instance, a BERT-based model used by Microsoft to improve their search engine, Bing. The accuracy of the model resulted in the largest improvement in Microsoft's search engine. However, it was impossible to run it on CPUs. With our GPUs, Microsoft got 800 times higher throughput, and they could run these models in real time. This led them to switch to thousands of NVIDIA GPUs running on Azure to power their search. AI needs GPUs both for training and inference. Our Ampere architecture delivered for the first time a unified platform for AI training and AI inference.
It provided 20 times higher performance, a unified computing architecture for data analytics, training, and inference, and with Multi-Instance GPU, the ability to scale down 50 times in a single server. This was the reason why it was picked up so readily by the industry. A100, as Jensen talked about, has had the fastest ramp of any data center GPU in our history. Shortly after Jensen announced it in May, over 50 different server models were announced by the world's leading server makers based on A100. All leading hyperscalers announced plans to deploy A100 to the cloud. It's already available on Google Cloud, Microsoft Azure, and Oracle Cloud. Of course, having a mighty GPU architecture is at the foundation of NVIDIA AI platform. However, AI workloads push the limits of every aspect of the data center, computing, storage, networking, and software to run all of this.
We are addressing this challenge in democratizing AI with three pillars of NVIDIA AI platform: data processing and training, inference, and AI application frameworks. First, for data processing, feature engineering, and training, our platform can run at any scale from one GPU, to multiple GPUs, to multiple nodes across a data center, running any framework, training any type of models or being available on any cloud. While some companies are just starting to do deep learning, nearly every company is doing data processing with exponentially growing scale. Our work with RAPIDS is now transforming the data analytics landscape. It now accelerates Spark, the world's leading data analytics platform used by 16,000 enterprises and 500,000 data scientists. Earlier today, Jensen announced that Cloudera is now accelerated on NVIDIA AI.
Cloudera has 2,000+ customers and runs on 400,000 data center servers. Manu will touch a bit more on this in his presentation. Second, inference is a great computing challenge. It requires a lot of software to make it work, and we break it down into four steps, each very complex. Starting with pre-trained state-of-the-art models for key use cases that are available from NGC, our cloud registry hub for our GPU accelerated software. They provide an easy ramp to enterprise customers to infuse AI in applications. They can then use our transfer learning toolkit to refine these models with their own datasets to optimize for their domains.
NVIDIA TensorRT, our optimizing compiler, then helps optimize these models to run for inference on our GPUs. Finally, Triton Inference Server actually helps them run these models. Applications can just send queries and the constraints like the response time they need or the throughput they need to scale to thousands or millions of users. Triton takes care of the plumbing to run these models. Our third pillar is AI application frameworks. They package up our stack and provide end-to-end workflows for incorporating AI into specific application domains for different industries and use cases. This helps democratize the complex AI development pipelines and helps enterprises jumpstart the adoption of AI for their use cases. Our application frameworks target some of the most challenging AI applications in large markets like self-driving cars, robotics, drug discovery, conversational AI, and recommendation systems.
The scale of inference on the cloud is huge, and we are just at the starting point of AI-infused services, so opportunities to service inference is massive. Running AI inference on NVIDIA is the most performant and cost-effective, and so we continue to see a rapid shift to inference running on NVIDIA GPUs. I talked about TensorRT a bit earlier, our optimizing compiler for inference. The latest version now has over 2,000 optimizations. TensorRT has been downloaded 1.3 million times and is used by 16,000 enterprises. In terms of GPU compute for inference, we've gone from practically negligible four years ago to shipping over 166 ExaOPS in the last 12 months. This is more than six times what we shipped last year.
Since AWS launched their first GPU-accelerated cloud instance 10 years ago, every cloud now offers NVIDIA GPU. The aggregate throughput has been increasing 10 times every two years. The chart on the right shows the growth of the aggregate NVIDIA GPU inference compute in the cloud. We estimate that the aggregate GPU inference compute now exceeds that of all cloud CPUs. With this trend, in 2- 3 years, NVIDIA GPU will represent 90% of the total cloud inference compute. Any AI application and service can now rely on NVIDIA inference. We are past that tipping point. NVIDIA inference is enabling transformative capabilities for our customers. Microsoft, earlier today Jensen announced, is adopting NVIDIA AI on Azure to power smart experiences in Microsoft Office, the world's most popular productivity application. The first features include smart grammar corrections, question and answers, and text predictions.
With NVIDIA GPUs, Microsoft was able to cut down the responsiveness to less than one-fifth of a second. That enables real-time grammar corrections. GPUs also provide high throughput so they can efficiently scale to service $0.5 trillion queries they expect to service in a year. Second, cybercrime cost global economy nearly $1 trillion, about 1% of the global economy. American Express alone does $8 billion transactions in a year, totaling about $1 trillion for their 115 million credit card holders. Using NVIDIA AI, American Express uses advanced AI on tens of millions of transactions every day, and it takes just two milliseconds to detect fraud instantly. AI fraud detection is going to help save financial industry hundreds of billions of dollars each year, and NVIDIA AI makes this possible.
Finally, Tencent's Platform and Content Group has numerous recommendation systems to support their applications, videos, news, music, applications, et cetera. They have thousands of models that handle hundreds of billions of queries per day. NVIDIA GPU inference enables the use of more and more advanced models in production for Tencent. While I talked about just a handful of customers of NVIDIA AI inference, today NVIDIA AI inference is operating services for companies in a broad range of industries, from automotive to consumer internet, to cloud based companies, to robotics, medical, retail and financial services, industrial customers, and so on. In many cases, only NVIDIA AI inference makes it possible to deploy advanced AI for production use cases, and in all cases, it saves money for customers. NVIDIA accelerated AI inference adoption has reached the tipping point.
With that, I would like to hand over to our next speaker, Manuvir Das.
Thank you, Paresh. Good morning and good afternoon, everyone. As Jensen mentioned at the beginning of this call, a data center is the new unit of computing. This is because the amount of data that is available to and processed by every application running in the data center is growing dramatically. This means that applications can no longer fit within an individual server and must be spread across the data center. Paresh has talked about the work that NVIDIA has done over many years now to accelerate particular classes of applications that are running in the data center. He talked about the three pillars with AI training and inference, as well as the different frameworks.
In this section, I want to talk to you about the next opportunity for NVIDIA, the next phase of our work in accelerating the data center, which applies not just to particular classes of applications, but to every application workload running in an enterprise data center. In particular, we are talking about technology that will be introduced into every one of the tens of millions of servers deployed in enterprise data centers. If you consider what has been happening in enterprise data centers over the last decade, the infrastructure has moved towards a software-defined model as guided by the advancements that have happened in the public cloud. On the right-hand side, you can see a variety of data center infrastructure functions which have traditionally been performed by hardware. For example, a firewall located at the edge of the data center or complicated networking infrastructure.
As well as operations that have been performed manually by armies as humans to manage their infrastructure. Over the last few years, all of these functions have been moved into software, which is now running on every application server. On the left-hand side, I have a picture showing you the stack that is running within every application server. The box on the top represents the actual application workloads running in virtual machines or containers on a bare metal environment. The box below that represents the infrastructure functions that are now deployed in software that is running on every application server. Software-defined networking, software-defined storage, software-defined security, infrastructure management. These are typically deployed in a layer like a hypervisor. A great example of this is VMware, which is used by almost every enterprise customer for their virtualized environments.
Every one of these servers is connected to the data center network, communicating and moving data between one server to another. This is accomplished with a network interface card or a NIC. As you know, NVIDIA is now working with Mellanox as well as part of our family. Mellanox has worked on the ConnectX family of NICs for many years now, which are state-of-the-art in terms of NICs that transfer data across the data center, and they're equipped with powerful acceleration engines to make the networking and IO operations proceed fast. What is the challenge with this model? Here you will see a representation of these different infrastructure functions that are running in this, in this layer of infrastructure. The challenge we have is that as the amount of data grows, the east-west traffic between servers in the data center is growing dramatically.
If you look at where we are at today, we already see that more than 30% of the resources on the CPU in every server is being occupied with these infrastructure functions, leaving less resources available to the actual applications themselves. This problem is only going to grow because the amount of data is growing exponentially, and therefore the amount of resources required by these different functions is also growing over time. There will be less and less room available on the CPU of the server itself to run the applications. A new solution is needed. A new piece of hardware needs to sit alongside the CPU on every server in order to take on this increasing load. We are therefore introducing the next concept in computing that we refer to as the Data Processing Unit or the DPU.
The role of the DPU is to take these software functions, offload them from the CPU and put them into a new kind of chip that we call the DPU. It is an extension of the chip that we already have in the NIC. In particular, along with the acceleration engines that we have on this chip, we have now introduced CPU in the form of powerful ARM cores that can host these infrastructure functions. As you can see now from the picture on the right-hand side, by moving these functions over to this new chip, the DPU, all of the resources of the host CPU are now available to run the applications, whether they are running on virtual machines or containers or bare metals.
It's important to stress here that it is not just about moving the functionality to the DPU, because the same situation of the burden growing as the amount of data growing would apply on the picture on the right-hand side as well. However, the DPU is sitting within the NIC. It is already processing every packet of data that is flowing through the network. Therefore, these infrastructure functions that operate on the data can now be much more efficient. They can be dramatically accelerated when they are performed on the NIC itself rather than on the CPU, leading to the same effect that we obtained on applications when we moved them from the CPU to the GPU. It is not just about offloading.
It is about a dramatic acceleration, which leads to massive reductions in the amount of infrastructure needed to run the same amount of application workload, leading to TCO benefits for the customer. As Jensen mentioned in his keynote this morning, we have introduced the BlueField-2 as our flagship DPU for this purpose. It provides a variety of acceleration engines for different functionality to do with IO storage security. It's got 7 billion transistors on it. To give you one example of the power of this technology, if you consider the different activities performed by the DPU, and you were to perform them on the host CPU, you would require upwards of 125 x86 cores to perform the same functionality, which is not really practical.
Here again is the reason why we believe that as computing moves forward, every server will be equipped with one of these DPUs to make all of this processing feasible. As we move forward with our journey on GPUs at NVIDIA, we introduced the CUDA platform, which was the software abstraction that allowed our ecosystem of 2.3 billion developers to proceed forward with a single API, even as we advanced the technology inside our GPUs. We are doing exactly the same thing with the DPU. Jensen today announced DOCA, Data Center Infrastructure on a Chip Architecture, which is our abstraction and interface for developers to access the capabilities of our DPU. It's based on open APIs, P4 for packet processing, DPDK for the network, SPDK for the storage.
These are open APIs on which the developer ecosystem can build a variety of software-defined infrastructure that can now run on the DPU as opposed to running on the host CPU. As you know very well, NVIDIA believes very strongly in the power of AI. The power of AI can be brought to bear on data center infrastructure as well. We are embarking on a journey, as Jensen announced, to take the silicon of the DPU and add to it Tensor Cores from the silicon of GPUs as well, so that we can infuse AI into data center infrastructure. This leads to a complete roadmap now for NVIDIA, from which the BlueField-2 is only the starting point. Here's a chart that shows our roadmap.
At the bottom left, you will see the BlueField-2, which has certain capabilities in terms of network traffic as well as its computational power. All the DPUs are based on one architecture, which is DOCA. You can see the timeline for our advancement. On the Y-axis, we refer to the computational capability. A year from after BlueField-2, we will have the BlueField-3, followed by the BlueField-4 two years after the BlueField-2. As shown on the Y-axis here, the BlueField-4, which is a combination of the silicon of the DPU as well as our state-of-the-art GPUs, will have 600 times the computational capability of the BlueField-2. It is a significant advancement. We are not waiting two years to bring this capability.
Before we get to the point where we integrate the silicon, we are producing new form factors where we combine the DPU and the GPU onto a single form factor, onto a single card. This is referred to as the BlueField-2X, which you can see vertically above the BlueField-2. It will follow the BlueField-2 by only a few months, but it has 85 times the computational power of the BlueField-2. It is a significant advancement, and it will put AI-infused data center infrastructure on a chip in the hands of the customer base much before BlueField-4 is available. I mentioned earlier that these infrastructure functions that are performed in software today typically live within a layer like a hypervisor. As NVIDIA and VMware have announced over the last few days, we have a major new partnership with VMware to bring this to the enterprise customer base.
As you know, the majority of enterprise customers today use the VMware platform for their virtualized data centers. What we are announcing and the work we are doing together is to take that VMware platform and to move its functionality onto this NVIDIA BlueField DPU so that the host CPU can be freed up to perform and run more of the applications. At the same time, we have also announced a partnership with VMware to bring accelerated AI applications to the VMware platform so that we can truly democratize AI and make AI available in a seamless manner to every enterprise customer. This is a picture of our full stack of collaboration. As you can see on the top here, we are talking about traditional enterprise applications that are already running on the VMware platform, as well as the workloads that Paresh talked about that are accelerated by NVIDIA GPU.
All of these workloads can now be run on one infrastructure using the VMware platform, which then in turn runs on GPUs as well as DPUs present on every server to provide now both of these forms of acceleration. Both the application acceleration for the domains of workloads accelerated by GPUs, as well as the DPU-based acceleration that applies to every server and every workload. At the same time, Paresh talked about data analytics as the next frontier of workloads that can be accelerated. We have also announced a partnership now with Cloudera. Cloudera is the predominant platform used by all enterprise customers for their Spark deployments. Cloudera's platform will now be powered by NVIDIA and also accelerated by GPUs as well as in this model by DPUs. With that, I'll hand it over to Justin to talk about the edge.
Great. Thank you, Manuvir. Good morning to everybody. While the big bang of AI originally happened in the cloud, AI is really about to transform every industry with a wave of new AI infrastructure announced today using the NVIDIA EGX Edge AI platform. I wanna talk to you about some of those announcements and put this in context. Today, the Internet connects billions of people to giant cloud data centers, but in the future, there's gonna be trillions of devices connected to millions of edge data centers. This is gonna create an Internet of Things that's a 1,000 times bigger than today's Internet of people. From smart retail to manufacturing and service robots, self-driving cars, smart streets and cities, computing is gonna extend from the cloud data centers to every corner of the world.
AI will sense, infer, and act accordingly at the edge. The amount of data generated by high-resolution sensors is just simply too much to have the data moved back to the cloud. Some things just can't be done from the cloud, as actions need to be immediate. The software powering this new internet will not be written by humans, but by computers learning from the data. This new way of computing is called AI, and the age of AI is about driving tremendous acceleration and demand for computing precisely at the time that Moore's Law has slowed down. This requires a new approach in computing, as legacy architectures just can't keep up. NVIDIA's accelerated computing platform is really the platform that will power the future of every industry. Today, we made several new announcements with the NVIDIA EGX Edge AI platform.
The EGX NVIDIA EGX is designed to make it easy for the world's enterprises to quickly stand up state-of-the-art Edge AI servers. NVIDIA EGX can control factories of robots, perform automatic checkout at retail, orchestrate a fleet of inventory-moving robots, and help nurses monitor patients. EGX is a full-stack solution consisting of the AI computer, system software, AI frameworks, and fleet orchestration and management software. Security is really a top feature of EGX. Every aspect from secure and measured boot of the operating system, protecting data in motion and at rest, securing the applications and AI models with signing and encryption to tamper-proof the infrastructure that will automate the future of industries. Today, we announced early access of NVIDIA Fleet Command. It's software as a service for deploying and managing AI services at the edge.
Fleet Command simplifies setup and management by ensuring a simple one-touch authentication to connect a new node, so you don't need Linux admins, you know, floating around in edge locations. This allows store associates or warehouse managers without IT experience to quickly set up new systems. One of the most important features of EGX, though, is the rich ecosystem of partners bringing AI to every industry. EGX servers are certified by all leading OEMs and now include NVIDIA BlueField-2 and Ampere GPUs. This ensures that customers can quickly find and easily buy hardware that's optimized for AI performance and can be securely managed and updated through leading enterprise platforms.
To fully accelerate the runtime of EGX servers, we're working with leading enterprise platform companies, including VMware, Red Hat, Canonical, SUSE, and others, to test and validate that the performance of CUDA-X can be delivered for AI training and inference through all of their platforms. We're building industry-focused, AI-optimized frameworks to make it easier for developers to bring new innovation to every industry. Every industry can benefit from EGX. With our network of partners, EGX can help companies in manufacturing, in healthcare, retail, logistics, and transportation. OEM partners, software partners, industry-focused solution makers can all participate as it's an open platform. This is truly the iPhone moment for the world's industries. NVIDIA EGX will make it easy to create and deploy new Edge AI services. Now, let me share with you the type of customers we've been working with to build and refine the EGX Edge AI platform.
Previously, we spoke about how we're working with leading companies like Walmart, Procter & Gamble, BMW, and Siemens to bring a range of new or to bring AI to a range of industries to drive higher business efficiency. Let me highlight a few more that we've announced today. KION Group is the largest logistics and automation supply chain solutions provider globally, operates over 6,000 automated warehouses worldwide. With Dematic, they're developing smart cabinets and adaptive speed conveyors to increase distribution center efficiency and throughput. With STILL, they're developing automated forklifts. KION is looking to simplify the management and deployment of AI applications to Fleets of GPU-accelerated systems used for optimizing warehouse efficiency using NVIDIA Fleet Command.
If we look at the retail industry, they lose 1.5% of sales, or over $60 billion per year to shrinkage. AI can instantly detect mis-scans at checkout. Kroger is one of the largest supermarket chains in the U.S., and operates close to 2,800 stores. With Everseen, a vision AI application, they're reducing customer errors and providing faster customer checkout to improve the shopping experience. Every healthcare provider in the world wants to reduce operating costs while improving patient care. At Northwestern Medicine, Whiteboard Coordinator is being used to operate a network of thousands of sensors, cameras, and microphones with perception and conversational AI to help nurses monitor patients, reducing the load on nurses while improving care. That's our EGX platform.
It's a full-stack, open platform, state-of-the-art computing designed for security from the ground up. We have a broad ecosystem of partners to help enterprises around the world create AI services. This ranges from software vendors like VMware, Red Hat, Canonical, and SUSE, to industry-focused SIs, including IBM and Accenture, as well as every major OEM and ODM, such as Dell, HPE, Cisco, Lenovo, Fujitsu, and many more. We're working with hundreds of ISVs who are leveraging the power of our AI frameworks to build industry-focused AI for every industry. Every enterprise company understands the power of AI. They no longer need to be convinced why they need it, but they need a broad partner ecosystem to show them how to become an AI-driven enterprise.
Powered by the world's most widely adopted accelerators and our broad range of ecosystem partners, we're working to bring AI to every industry with NVIDIA EGX Edge AI platform. Let me hand it over to Kimberly Powell, who runs our healthcare business.
Great. Thank you, Justin. I hope everyone is well this morning. The healthcare industry is at an extraordinary moment in time. The global pandemic has created the biggest threat to humanity in our lifetime. The race to discover new therapies has never been more critical. Today, the healthcare industry is producing more biomedical data in a couple of months than the last several hundred years. This is a perfect storm to catalyze AI in drug discovery. This is why we're so excited to announce NVIDIA Clara Discovery, a suite of state-of-the-art tools to tackle the most pressing and future challenges in drug discovery.
The end-to-end drug discovery process is incredibly complex, starting with biology to understand human disease and why we get sick in the first place, to chemistry to combine molecules that can inhibit or enhance biological behavior, next to patients and uncovering biomarkers that are the medical signs associated with our disease or our response. The pharma industry is huge, at $1.5 trillion large, it's still very much an unsolved problem, taking well over 10 years, costing $2 billion, still has such a high failure rate of 90%. Drug discovery draws on every computer science domain from accelerated computing, data science and machine learning, deep learning, and natural language processing. Clara Discovery brings together more than a decade worth of work and working with the industry's most popular GPU-accelerated tools like Schrödinger for computational chemistry.
Where there weren't any tools, we built our own with NVIDIA Clara Parabricks for genomics, Clara Imaging for pathology and radiology, BioMegatron and BioBERT for natural language processing, and NVIDIA RAPIDS for GPU-accelerated machine learning. This suite of tools is powering the next generation of computational drug discovery to accelerate discovery from months to minutes, and using AI in this new trove of data to improve success rates of discovering new life-saving drugs. The U.K. is an epicenter for healthcare research. Cambridge is home to where Francis Crick and James Watson discovered the structure of DNA. It's home to the world leaders in the pharmaceutical industry with a rich university and startup ecosystem focused on healthcare. Researchers and scientists in the U.K. need a state-of-the-art computing infrastructure. Because there's no more important time, NVIDIA is building the U.K.'s fastest supercomputer we're calling Cambridge-1.
It's a 400 petaflop AI performance supercomputer based off of NVIDIA's DGX SuperPOD. It will become the fastest supercomputer in the U.K., and it will be in the top 30 on the TOP500, and also the top 3 in the Green500. Cambridge-1 will host our collaborations already underway with the U.K. AI and healthcare researchers in academia, industry, and startup. Our first partners are GSK, AstraZeneca, King's College London, Guy's and St Thomas' NHS Foundation Trust, and Oxford Nanopore Technologies, all which are already using NVIDIA GPU computing. Cambridge-1 lets them do experiments too large for their infrastructure or a resource while they're building up their own. Cambridge-1 will accelerate the use of AI in the vast and wide healthcare ecosystem.
Also too exciting to announce today is GlaxoSmithKline is leading the way in the pharmaceutical industry in adopting artificial intelligence and data-driven drug discovery. We're partnering with GSK in one of the world's first AI drug discovery labs. GSK has been pushing the frontiers in drug discovery and data-driven drug discovery for years, using genomics to improve target selection early in the drug discovery process. In a recently established GSK's London-based AI hub, GSK and NVIDIA together will expand the use of biomedical data in the field of digital pathology, radiology, genomics, natural language processing using Clara Discovery to optimize computational discovery applications. In addition to GSK's investment in DGX A100 systems, GSK will also be able to access NVIDIA's new Cambridge-1 supercomputer. Just to conclude, we are in a perfect storm for AI in healthcare.
With the race against time in the global pandemic, the explosion of biomedical data, and the utility of AI, we can accelerate healthcare research and discovery from months to minutes, 16x across many domains used in drug discovery, and harness the biggest AI breakthroughs in natural language processing to tap into invaluable biomedical literature and clinical data. Healthcare vocabulary is domain specific. Complex diseases, proteins, and drug names. A case in point is in COVID-19. This is a tipping point for AI in healthcare, and we're delighted to be building the industry's computational platform and partnering with the world leaders in healthcare. Thank you.
Okay, this is Colette Kress. I'm just gonna do a quick overview for you about what GTC Fall 2020 is all about. First, you've seen us talk so much about NVIDIA AI and NVIDIA's overall momentum in terms of gaining across so many different pieces. One thing that bounds us all together is really about CUDA and the overall development platform. We have more than 20 million CUDA downloads a year, more than 6 million in this last year. What we are seeing also is an expansion of NVIDIA and being able to power so much of the overall enterprise. You've seen Manuvir discuss in terms of what we're seeing with our collaboration with VMware and Cloudera, as well as the introduction of the NVIDIA DPU data center on-a-chip architecture software as well.
You also got to hear about NVIDIA Edge AI for the Internet of Things out there on the edge, and our Fleet Command recurring revenue service that we will be adding later. Here with Kimberly, we've heard about our focus in terms of NVIDIA Healthcare as a very key part in both drug discovery and key partnerships with important areas in the U.K., such as GSK. This is allowing us to broaden our overall ecosystem, our customer adoption, every server, every storage OEM, hundreds of ISVs, thousands of enterprise. Just keep in mind, here at GTC, we are also exposed to more than 2 million, 2.3 million developers focused on our overall computing platform. I'm gonna take this next opportunity to discuss an important piece of the high-level view that sizes up our market opportunities for NVIDIA Compute.
First, NVIDIA RTX targets the large and growing markets of gaming and professional visualization. Our trailing 12-month revenue in these two markets is close to $8 billion and representing an 18% CAGR over the last five years. Computer graphics is the first and holistically the largest application of NVIDIA GPUs. Our graphics growth going forward will be fueled by the expanding universe of gamers, creatives, and professionals, which already number over 1 billion around the world. One day, we expect every human will be a gamer or connected to others in virtual worlds. Our RTX platform and the Ampere architecture launch this year was a giant step to making that future a reality and a foundation for our growth in graphics over the next decade.
While gaming was historically our largest revenue driver, last quarter, for the first time, it was eclipsed by our data center platform, driven by AI. Last quarter was also the first to include our Mellanox acquisition. Let me take this moment to update you on our total addressable market opportunity for data center, which has significantly expanded with the inclusion of Mellanox. We see a $100 billion TAM by 2024 across the four main markets within data center, including high-performance computing, hyperscale and cloud, enterprise, and edge. As you may recall, last year, we sized our data center TAM at $50 billion by 2023. Let me help you understand the drivers for this expansion. First, with Mellanox, we are now addressing the large data center networking market with a particular focus on high-performance hyperscale and software-defined environments.
This adds over $20 billion to our TAM. Second, as you heard from Manuvir's presentation, we are introducing a new class of processor in the data center called the Data Processing Unit or DPU. The DPU offloads a substantial amount of the processing currently done by the CPUs, as well as processing done by other data center infrastructure today. In other words, data center on a chip. This new process adds more than $10 billion to our estimated TAM in this period. Third, you heard from Justin's presentation, we are enabling emerging Edge AI market with our EGX computing platform. This adds approximately $10 billion to our TAM. Each of these opportunities is uniquely enabled by the combination of NVIDIA Compute and Mellanox networking, and we are delighted to have Mellanox team on board. That concludes our prepared remarks for today's presentations.
We will now turn it back over to the operator. We will open up the line for Q&A.
At this time I would like to remind everyone to, inorder to ask a question please press star then the number one on your telephone keypad. Again, that would be star one on your telephone keypad. Your first question today comes from Aaron Rakers from Wells Fargo. Your line is open.
Thank you for taking the questions and doing the presentation. I just wanna, Colette, if I can just touch on briefly the TAM assumptions that you're making. I guess first of all, on just kind of the general kinda GPU TAM, can you give us any kind of framework of how you're thinking about attach rate on GPUs for the data center? kind of a similar question, as you think about the time horizon, the $10 billion opportunity on Data Processing Units, what's the underlying assumptions of kinda the industry's move to having every server incorporating some form of a DPU in them? Thank you.
Yeah. Thanks, Aaron, for the question. First, let me start off with it's a great place to look at in terms of the overall server environment out there and the use of overall GPUs and acceleration as well as AI in many of those servers. Nothing has changed in terms of our view over the future. Very similar to everybody would be a gamer. We do expect everything in the overall data center environment to be accelerated, and the movement of AI is getting us there. It is early in these days to talk about that attachment. We have discussed already our continued growth in that overall attachment, but coming after a very small base of folks that have been focused on that AI.
Our expansion that you've seen us do here in today's presentation in terms of GTC as a overall is really expanding to all of the different types of workloads that are out there, as well as the different customers, whether they be enterprise, whether they be focused on the edge or in the core of all the workloads within that data center, to really have a stronger attachment as we go forward. In summary, nothing's changed. Our goal in terms of getting all of the servers to be accelerated with the use of AI is still there.
Thank you.
Your next question will come from C.J. Muse from Evercore. Your line is open.
Good afternoon. Good morning. Thank you for the presentation today, and thank you for taking the question. I guess if I could ask two. First for Jensen , you discussed a number of AI platforms in your keynote this morning. Just to help us prioritize where we should be focused, what do you think are the biggest revenue opportunities looking out over the next 1 to 2 years? Then perhaps a question for Kimberly . You know, you announced a partnership with GSK, working with AstraZeneca. You got Cambridge-1. Really curious how you think about your go-to-market strategy and the revenue model for your medical business. Thank you.
Yeah, C.J. The AI started in research, and the first five years of the work that we did and most of our conversations that we had was related to the groundbreaking work that was being done, superhuman image recognition, superhuman speech recognition, and now near human natural speech synthesis. The ability to process data at a scale no humans can so that it could predict recommendations. Conversational AI because of recent breakthroughs in natural language understanding. The first five years was really focused on groundbreaking work, and the early works of self-driving cars, of robotics, et cetera. The first wave of economic growth of AI, the economic impact of AI was in the cloud, and I would expect the next couple of years to still unquestionably still be in the cloud.
The vast majority not. I would expect that the next couple years, not only will the cloud grow in very significant percentages, but off of a quite a large base now. It's a multi-billion dollar business. As we said, it is now past the tipping point where any service, any application can take advantage of NVIDIA GPU inference because it's in every single cloud and it's in such large abundance. The amount of computation, aggregate computation of NVIDIA GPU for inference, now exceeds and surpassed CPU. If it's growing at a factor of 10 per couple of years, in a couple of years, 90% of the world's total inference compute capacity would be GPU accelerated, NVIDIA GPU accelerated.
I think that the as you've seen in other type of platforms, once it reaches the tipping point, the acceleration of adoption actually goes up. For obvious reasons, because people feel really, really safe now that they could take advantage of it because they can always count on the NVIDIA architecture in every cloud, in abundance of it in every single cloud. I think the next couple of years, you'll see NVIDIA AI growing in the cloud, at service providers, into large numbers and ever larger numbers. The next wave is enterprise. Enterprise we've described in two ways. Enterprise is helping to automate companies. Which is a lot of the work that Manuvir was talking about.
It requires us to re-architect the data center. The system software has to be re-architected once. The reason for that is because enterprise software and the enterprise data center infrastructure is very different than that of clouds. We have to work with partners, particularly VMware, to do a lot of computer science around the current stacks. Then of course, the data analytics applications. We're gonna grow in enterprise, even before we finish with VMware, because many of the early adopters are perfectly comfortable building multiple infrastructures. That's gonna get turbocharged incredibly when the work that we do with VMware over the next several quarters come to market. Meanwhile, we're preparing the ecosystem as we speak.
The next wave is edge and autonomous. The enterprises and the companies are comfortable and have mastered processing large amounts of data, they're also collecting a giant amount of data, and that data is connected to sensors or web services or applications or products. Before you know it, these things that are out all over the world today, it could be anything. It's, you know, a lawnmower, a refrigerator, an elevator, air conditioner, you name it. They're all gonna be connected to either 5G or Wi-Fi. That allows them to collect ever more data and turn all of these products into essentially smartphone-connected smart devices. The iPhone moment is coming.
That's the wave after that. We're preparing for each one of these waves. They'll, you know, we hope that they happen just bam, bam, and just keep on happening over the course of the next five years, surely. We're, you know, you're looking at one of the largest computing opportunities ever. Kimberly's got the next question. Go ahead, Kim.
Thank you. Thanks, Jensen. C.J., thanks for your question. A bit about healthcare's go-to-market strategy. It's much like all of NVIDIA's enterprise go-to-market strategy in industries. If you think about what we're building with Clara, we are building a domain-specific computing platform for healthcare, for computational healthcare. We deliver what we call a full stack of silicon systems and software. That software, if you think about what I described in Clara Discovery, there are aspects of drug discovery where there is still just an insatiable demand for compute.
Doing the, that, you know, looking at exabytes of genomic data in the near future and trying to use artificial intelligence to extract associations out of these very large populations of genomic data, or looking at the vast chemistry space of 10 to the 60 potential combinations and using a combination of search to docking to simulation, all done in silico today, we still can totally consume the world's fastest supercomputers in doing that work. We're doing that with COVID today. These systems of COVID are more complex than we've ever simulated. There's still an insatiable demand there. Then as we move into these new budding areas of natural language processing, as Paresh talked to you about, the fact that they are growing in complexity and size to train these models every 10 months, that's no different.
I mean, these models, biomedical specific natural language models, take tens of thousands of GPU hours to train. We have, you know, just incredible amount of opportunity ahead of us, and that's what we're really inspired to do with the Clara platform. We hope to catalyze and quickly disseminate the capabilities through building out of Cambridge-1 and enabling the, you know, the industry-leading researchers with it. Longer term, over time, we have plenty of opportunities to monetize across all three systems, silicon and software.
Thank you both.
Your next question will come from Vivek Arya from Bank of America Securities. Your line is open.
Thanks for the presentations and thanks for the question. I actually had two as well, one for Colette first and then one for Jensen. Colette, was hoping if you could give us a sense of the supply situation for Ampere and on both the data center and the gaming side. You know, good to have lots of demand, but just how is the supply situation working out? There, if you could talk specifically to the gaming side as well. Jensen, my question is, where does ARM fit into your data center vision?
From what we heard today, if more of the workload and value are going to the preprocessing or the DPU or the SmartNIC, which I realize contains, right, some embedded CPU, but more of the value is going to that DPU and various kind of accelerators and GPU. Does it matter to you one way or another whether it is ARM or x86 that's the CPU architecture of choice? In other words, is it really critical to owning ARM, or do you think you can achieve similar levels of success by just optimizing DPU and accelerators? That's where most of the value in the data center is shifting anyway.
Vivek, thanks for the first question to discuss in terms of supply. We're very comfortable with the supply and where we are in terms of that supply for our outlook that we have provided. When we turn to our overall data center and we look at the overall Ampere architecture, keep in mind the A100 and that going forward is a very complex product. It will probably take multiple quarters to really work through all the supply needs and get that to market in its full capacity. You also focus in terms of Ampere architecture for overall gaming. We're in the initial stages of ramping that. It will take some months for us to fully supply to the channel, but we are right on track in terms of providing that.
Sure, we'd love to have more supply sooner when we're ramping, but we're also executing to our plan, so we feel very comfortable with the supply and what that means to our outlook.
We can achieve extraordinary success and all of the success we've talked to you guys about without ARM. With ARM, there are some really exciting things we can do. Let me highlight two of them. The first one is extending NVIDIA's architecture accelerated computing to the ARM ecosystem. You might notice that accelerated computing is surely here. It is past the tipping point. Everybody acknowledges that this is the way to go forward, that Moore's Law has ended. It ended last week. It didn't revive this week. In order to extend computing further, we have to bring accelerated computing to all computing devices, including ARM. The benefit of owning ARM is that we could also offer NVIDIA accelerated computing in soft IP form, not just hardened IP form, but soft IP form.
NVIDIA is an IP company. We're not a chip company. NVIDIA's an IP company because I'm pretty sure that TSMC makes the chips. I'm pretty sure that what we delivered to them was effectively an email as their completion of a $multi-billion project. NVIDIA is a soft IP company. We're IP company, and the benefit of having ARM is to have this team that has a vast network to the ecosystem of ARM and all the devices that they're in, the billions and billions of units that are sold every year. We can extend ARM with accelerated computing and AI computing that NVIDIA is renowned for. Second, we are going to bring a lot of platform technology to ARM in a whole bunch of new data center environments.
It could be high performance computing, cloud data centers, enterprise data centers, as we were talking about earlier with DPUs, edge data centers, a whole bunch of new technologies that we're rolling out. As we turn the CPU core of ARM, which is world-class, I mean, this is absolutely the most energy efficient CPU core in the world. As we turn the CPU core into computing platforms, we're gonna deliver a lot of value to ARM. We're gonna create a lot of value in ARM beyond the mobile device. These are all markets that I'm talking about that are really nascent. As we, as we create all of that value around the ARM platform, it would be great to own it first.
I think we have two enormous opportunities to extend NVIDIA's accelerated computing to a large ecosystem around the world. Secondarily, we're gonna create a lot of value around data centers and servers and computing platforms that are very nascent to ARM, and we would love to own it as we create the value around it.
Thank you.
Your next question comes from Timothy Arcuri from UBS. Your line is open.
Thanks a lot. I had two as well. I guess the first question is for Colette. How much of the $100 billion TAM is China? That'd be my first question. Then the second question for Jensen, and really it's, you know, expectations around your share of that $100 billion TAM. Is there a way to sort of handicap what would be a reasonable share assumption within that non-gaming TAM? I guess I ask because if I sort of take what your non-gaming and your non-Mellanox revenue is this year relative to the TAM that you set forth a few years ago, it seems like you're maybe mid-teen to like 20% of that TAM. I guess the question is, would you be disappointed with, say, you know, 20% share of that TAM by, you know, 2024?
I guess asked a different way, is it really that you expect to gain share within that rising TAM, or the story really is riding the growth in the TAM? Thanks.
Let me first start with the question regarding our TAM and a regional breakout. We don't have the ability at this time to really look at that opportunity by region. Keep in mind, for each of the areas that we're focused on, we have the opportunity to take that to each and every single region. We can look at it by the additions that we have added with Mellanox, with the DPU and with the edge, or we can look at it in terms of the types of customer markets that we will also be addressing. This is an opportunity for us to focus on both our hyperscales and the massive expansion in terms of the cloud. That will be a considerable portion of our overall TAM as a whole.
Additionally, you can think about the overall enterprise opportunity. A lot of growth, just announced today in terms of key areas of the enterprise that we can have a focus on. High-performance computing has been a big part of us for more than 10, 12 years. Again, expansion in terms of bringing further acceleration and AI to that is also an area. Now the edge, and you can think about those devices, even in all of the regions, has the opportunity to grow. We've expanded each and every single one of those different areas today in terms of our increase to $100 billion with the Mellanox, the DPU, and the overall edge. Yes, a region such as China or the U.S. can definitely benefit from that.
We address the entire TAM except for x86 CPUs. That's really the simplest way to think about that. In almost all computing platforms that we serve, accelerated computing is the most important part of it. Going forward, if we believe in the thesis, two important thesis. The first one, all applications in the future will be infused by AI. For example, something like Microsoft Office will be infused by AI. I would suggest that AI would be in every computer, and I would suggest that every computer will be accelerated. I am certain every computer will be accelerated, in fact, just as I was convinced 30 years ago that every PC would be accelerated with GPUs. I'm certain of that.
I am certain of the fact that CPUs alone will not do the job. That is complete certainty. I believe in the thesis of AI, I believe that AI will be in every application. AI will open new markets and create new applications that weren't writable before, that no humans know how to write. That I believe in, and therefore accelerated computing is going to be everywhere. In all the platforms I win, accelerated computing, the GPU, and the networking, frankly, dominates the vast majority of electronics inside those computing platforms. Second, I believe in the world of zero trust. I believe that protecting data centers at the perimeter is historic. It's like building a big wall. It makes no sense.
The future of security is about trust, it's about zero trust, it's about securing every single transaction, every single node, every single application. Therefore, you need to take what it used to be security appliances at the perimeter of the data center and put it into the servers, every single one of them, every single network. That's the reason why the networking chip is going to be the most important security chip in the future, because that's where all the input and output comes from. That's exactly where you wanna put it, that's exactly why the DPU is invented. Security is gonna force every single computer on the planet to have something like a DPU.
I believe that every single server node will be accelerated by a DPU, and every single server application will be accelerated by a GPU. Therefore, the vast majority of the world's TAM, minus the x86 CPUs, will be our opportunity.
Thank you, Jensen. Thanks.
Yeah, thank you.
Your next question comes from Stacy Rasgon from Bernstein Research. Your line is open.
Hi, guys. Thanks for taking my questions. I have two as well. First, to go back to your chart showing GPU inference workloads in the cloud versus exceeding all CPUs. I guess given that trend, how does your cloud revenue today split between inference and training? Like, what are the trajectories of growth are relative for those two relative to each other? The second question, can you tell us a little more about how the revenue model for the VMware partnership works? Is it just working to incentivize broader GPU use in enterprise, or is there a more direct revenue share, something about that? Thank you.
Let me see if I can...
Do you want me to take both of them?
Yeah. Why don't you give it a shot?
Okay. The reason why our inference, well, first of all, you know that our inference is. Hello?
Hello? Yes.
Yeah, we've got some reverb. That's okay. The first thing that's of course, Stacy, you know that our inference acceleration business has been growing very, very quickly, for all the reasons that we've already talked about. We turbocharged it even more this time with Ampere because Ampere is our first universal GPU. We used to have essentially three GPUs in data center. One of them would be very heavy duty training systems to build these large models, AI models. The second is a cloud training platform, which is based on PCI Express. Like the new GPU they just that we announced today, the NVIDIA A40, it's going to go into all the clouds, it's easy to deploy.
It's easy for them to put into all their PCI Express servers because it's based on PCI Express. The A100 is based on SXM2, a different type of networking and a different type of system architecture. In fact, very different. The second, which is cloud-based training, was our second GPU. Our third was our inference GPU. Well, what we did with Ampere is we combined it into one architecture. You could literally now use the Ampere, the A100 SXM base for both training as well as cloud training, as well as inference. One single architecture.
If you would like to have PCI Express versions, because your cloud data center is able to facilitate a lot more PCI Express servers, we have the A40 GPU, which now allows you the A100 and the A40 GPU, which are, depending on sizes, that allows you to do both training and inference. We now have an architecture that's universal. It does training, it does inference, it does computer graphics, it does all the things that we, you know, that we do very well, all into one architecture.
That's the reason why our inference performance is going to not only continue to grow at the historic rate, which was really, really high, both the number of units and the fact that we're increasing throughput by a factor of 20, you know, generationally. We now have the ability to put a lot more unified, aggregated GPUs in the cloud that are inference-based. That strategy was a really good strategy. You know, Stacy, what people want is they wanna make sure that if they were to use a cloud that develops software for a type of technology, they develop software for a capability. It could be video decoding, it could be, you know, whatever it is, x86 or ARM or in our case, NVIDIA's accelerated AI.
Whatever capability they use in a cloud is available in every cloud, so they have the flexibility, which that is established. Number two, abundance of capacity. That's why the tipping point of our aggregated AI inference throughput is such a big deal. I think at this point, because of the rate that we're growing, it's a foregone conclusion it's gonna be the vast majority of compute in the cloud. Gosh, Colette, what was the second question?
The second question was on VMware.
Oh, VMware.
Revenue and monetization.
Yeah. Go ahead. Yeah, VMware. There are three ways that we benefit. I'll leave the most important one last. The first way we benefit, of course, is VMware running on VMware, as you know, is the data center operating system. They represent 70% of the world's data centers. This operating system is really computationally complex these days. The reason for that is because of software-defined data centers. The networking stack, the storage stack, the security stack, the virtualization stack, it's all running in VMware. The first thing I'm gonna do is I'm gonna offload and accelerate and isolate the data plane from the application plane. That offload alone creates really fantastic opportunities for our DPU.
It is cheaper, it is purely faster, and unquestionably more secure to have VMware running on x86 plus a DPU instead of x86 without a DPU. Okay. The first is creating an opportunity for our DPU. Second is every one of the VMware stacks goes with a virtualization stack of our GPUs. That hypervisor, there's the VMware hypervisor, and then there's essentially the NVIDIA hypervisor for virtualizing all of our GPUs. The virtualization of GPUs is really complex, and it opens up CUDA, opens up our graphics stack, RTX, it opens up cuDNN, opens up all of the capabilities that is buried underneath the hypervisor otherwise. The second thing is it opens up the virtualization stack, we call it vGPU, for all of our data centers. Okay?
The third, and this is really the biggest one, which is the ability for enterprises to be able to accommodate all three domains of computing, from scale-out, virtualized to microservices. All three of these harmoniously and basically transparently in their data center using VMware. The ability for enterprise IT to easily adopt NVIDIA-accelerated AI is now you don't even have to think about it. It was just like before. You know, VMware has never fully, transparently, integrated NVIDIA GPUs or any accelerators aside from the CPU until now. This transition is a big deal, and it opens up great opportunities for VMware into all of the worlds of AI.
It opens up great opportunities for us to be able to transparently, seamlessly, easily integrate NVIDIA AI into all the world's data centers. Three ways.
Got it. Thank you, guys.
Yeah. Thanks a lot, Stacy.
Your next question will come from John Pitzer from Credit Suisse. Your line is open.
Yeah, good morning, guys. Thanks for the presentations. Two questions here. Jensen, both in your presentation and I believe Paresh's presentation, you talked about software writing software and sort of this iPhone moment. I'm wondering if you could just help elaborate on that a little bit. Is the analogy here that you're sort of the iOS and you'll be collecting a recurring revenue stream on some of these sort of AI apps the same way Apple does? If so, should we expect to hear about, you know, other Mercedes-likes deals coming down the pipeline? As you think about monetizing software, is that part of your $100 billion incremental TAM that Colette talked about this morning, or is that above and beyond? Secondly, for Colette, you talked about kind of Ampere being extremely strong, and you guys clearly guided to that in October.
You also guided gross margins to be under a little bit of pressure as you ramped a new product. I'm just curious, is the strength that you're seeing just that demand is outstripping supply or you're having some supply issues as well? How should we think about some of your gross margin comments? Thanks.
Our $100 billion TAM does not include many of the things, almost all of the software things that we've talked to you guys before. It didn't even include what I mentioned just now to Stacy about our virtual compute. It doesn't include GeForce NOW. It doesn't include Drive. It doesn't include Fleet Command. It doesn't include our software stacks for enterprise. We'll have plenty of opportunities to talk to you guys about that in the future. Today, we wanted to just stay very focused, keep it nice and conservative, and we have plenty to talk about. NVIDIA is a full stack accelerated computing company, as you know.
Our opportunities, and we're an open computing platform. We work with our network of partners in any way they would like. For some, they would like the whole stack, and the reason for that is because ours is just so good. We're so good at it. For some, they would like to develop some parts of it and use ours. We work with them to figure out which part of it they would like to use of their own and which part of it they would like to use ours.
Some people would like to build all of their own and use a lot of our open source tools or our libraries, like CUDA-X or our RAPIDS open source stacks to build their data analytics services. We're an open computing platform that works across the multiple layers, from the technology to the system, SDKs to the application frameworks. We wanna be able to work with the entire world's industries and democratize AI and bring accelerated computing to as many places as we can. What we've only captured so far in our TAM is the hardware stuff. The hardware stuff, which is-
Jensen
-plenty big as you know.
Just on the Mercedes deal, we run numbers, it's not hard to envision kind of a software recurring revenue stream which is as large as the hardware revenue stream. Is that how we should think about the potential for service and software recurring revenue relative to that $100 billion TAM?
Yeah, that's a great way to think about it, and if not more. The reason for that is this. I don't mean for you guys to go make any changes in your models, okay? Just listen to the strategy and think about the implications and think about where we're going is. I'm building it all in public and all these pieces are being talked about at GTC. You guys know where we're going. There is no question that in the case of autonomous vehicles, the business model that we've created with Mercedes is really quite extraordinary. It's potentially the best one.
The reason why is because, first of all, it's incredibly hard to be able to create a computing platform that integrates into the most safety concerned, the most safety conscious companies and industries, with a combination for all of the heritage and all of the existing, you know, the existing technologies. To be able to integrate into that, infuse into that, harmoniously is a great challenge. That's the beginning. That took us 10 years to learn. Now that we're inside the car and we're the computing platform, we can create the applications that sit on top of it.
Unlike a smartphone, you're not going to be able to create an application in the cloud and download and it works on every single Android phone. It's just not gonna be like that. The reason for that is because of safety. These applications are safety concerned first, and so each one of the application opportunities belong to the car companies. This is one of the reasons why it's such an extraordinary thing that if Daimler could figure out a way to make all of the car software defined and turn them into application platforms, they'll grow that application platform 2.5 million cars a year. They'll grow instantaneously. Over the course of a decade, you could just imagine the economic opportunity that they're gonna create.
Our business model with them is to share that. So we're doing a lot of the hard work, of course, as well. That's a significant opportunity. For us, we would like to do this. You know, we're an open platform company, and we would like to do this with other car companies. There will be other opportunities. The reason for that is very simple. The number of companies in the world that could create an end-to-end self-driving car stack that is world-class and that you can deliver on real streets is very, very few that integrates into the existing car industries. Very, very few.
I think this is a great opportunity, and we're gonna continue to scale up. You're gonna see other examples like that. You're gonna see other examples like that. We'll always have free community versions. That's just one of the policies we have. The community versions will be free. The developer version will be free. There'll always be free versions.
For some companies, they wanna make sure that we're on the hook on it, and they wanna make sure that we have some kind of an enterprise agreement and an enterprise business model that allows them to get in front of, you know, open source or allows them to get in front of, you know, developers and others so that they can get their software enhanced or software debugged.
Let me see if I can touch base on your second question that was regarding overall Ampere demand and the impacts in terms of our gross margins. So far, the gaming demand for our RTX 30 series has just been off the charts. We had expected a really great holiday season. We knew that our overall platform that we were bringing was the best generation-to-generation performance ever. We've got a great release of fall games that are coming out. The work from home is even bringing more and more to the gaming and the entertainment and social arena. We're racing to catch up to that demand, but the ramp is going well. The yields are very good, so all of that is intact.
When we think about our gross margin, let's remind what we did in terms of our gross margin outlook for Q3. Our outlook for Q3, as usual with most quarters, mix is our driver of overall gross margin. We expect a very strong sequential increase for our gaming, with that gaming piece of our business being so strong, we took a slight sequential dip in terms of our guidance for gross margin. Everything seems to be in place, intact, no change in terms of our gross margin in terms of what we're seeing.
Very helpful. Thanks, guys.
Yeah, thank you.
Your next question will come from Mark Lipacis from Jefferies. Your line is open.
Hi. Thanks for the presentation today. The question, I think this one is for Jensen. To provide an integrated data center scale architecture, I'm trying to make sure I understand how far across the data center value chain and how deep in the value chain NVIDIA you feel NVIDIA has to go in order to deliver that. I, you know, I think it's pretty clear NVIDIA's not a chip company but a platform company. Maybe if you could compare, you know, what you feel you need to deliver across that value chain today to today's data center value chain.
Is this, is NVIDIA effectively becoming the equivalent of, you know, companies that are selling processors in the data center today, companies that are selling servers, you know, and the software, you know, the networking companies, the, you know, on the, on the software, you know, from the OS to the app, you know, application side, how far up that the software stack you're going? If you could provide maybe if you had an analog in today's data center versus the data center scale architecture you're delivering in the future, maybe that would be helpful to level set. Thank you.
Sure. The first thing, there are three questions I'll hit right away, and then I'll explain. One, we're not like a company that exists today because AI is a problem and an opportunity, a challenge and an opportunity, unlike any software that's ever been written before. Otherwise, why could we do all the things that we're doing right now? Number 1. Number 2 , we are not doing integrated data center. We're not doing that. Number 3, we innovate as much as we need to and as little as we can. We innovate as much as we need to, as little as we can, which is the guiding principle of NVIDIA, to do as little as we can. We're not trying to do everything.
We're only trying to do the things that we have to. We're only doing the things that the world relies on us to do. We're only doing the things that we do. If we don't do it, the world just doesn't have it. It is absolutely the case that if we don't do what we do today, the world doesn't have it. It is absolutely the case that when Gelsinger and I worked on the VMware partnership with us, if the two of us don't do it just doesn't get done. It just won't get done, ever. We have to go and do the things that we do because unless we do it, the world doesn't have it. I answered those three questions very quickly.
Let me now get back off a little bit and explain one. The way that AI is written, the way that AI develops software, it's a computer that's off learning from data. It's learning from data that we collect. We ask, we coach it, and we influence it. On the type of neural network architecture and the type of data that we presented and the way that by which we want it to learn, we coach it. We're like a coach. We're like a teacher. It goes off, and it runs for days and weeks, and it does it over and over and over again on giant amounts of data, and it writes the software. When it's done writing that software, we can't read it. It's unreadable.
It's like a neural, it's like a brain dump of somebody's brain, and it's not readable. It requires a new type of computer to run it. From the way that the software is written, the methodology by which it's written, the infrastructure pressure it creates, and during the talk, a lot there were some really, first of all, the four speakers were fantastic today. I really appreciate their work. You could hear in their phrases, the tip of the iceberg of the challenges in computing that we're solving. The software is different, the way it's written is different, the tools are different, the pressure on the infrastructure is different, and the deployment of it is different.
In no time in history did we see that all of a sudden the data center now, the enterprise data center, if it wants to be hybrid cloud, the enterprise data center has to manage three computing environments. That's never happened before. One computing environment is bare metal scale-out, distributed computing like a supercomputer. Number two is virtualized multi-tenant. Virtualized, easily manageable, easily scalable, easily secured multi-tenant. Third, containerized microservices deployed far out at the edge. You'll never visit it again. You drop the server, you connect it to your network, and hopefully you never go back to that warehouse, and you never go back to that storeroom ever again. You manage it from one pane of glass very, very far away. These three types of computing domains has never happened in one company before.
We've gotta go make it happen. It's never done it for AI. It's never done it for GPUs. We have to go create the necessary pieces. When we are done creating it, this is very, very important, when we're done creating it, we open it up as SDK. We open it up as SDKs. Chip SDKs, system SDKs, HCX, EGX, AGX. They're all SDKs. They're hardware components that OEMs can integrate. We put on top of it our entire software SDK. We put our libraries on top of that. For the application developers, we'll even create tools like application frameworks, which are basically AI skills that we pre-train. All of this stuff is put into the cloud. All of this stuff is put into the cloud. It's all certified, it's all optimized.
It runs in the data center, it runs in the cloud. We, as a result, can connect up a network of partners, of software developers, system makers, solution makers, cloud service providers, a network of partners all over the world, and they can all run NVIDIA AI. They can all run NVIDIA accelerated computing. They can all run NVIDIA Clara. They can all run NVIDIA RAPIDS. They can all run NVIDIA Isaac, Jarvis, Merlin, all of the SDKs that we created. I hope that's helpful. I know we look very different. However, accelerated computing needs to look different because Moore's Law is finished. Number 2, AI is different because it's not written by a human, it's written by a machine. The world has changed. That's why a new type of company needs to be created.
Very helpful. Thank you.
All right. Thanks a lot.
Your next question will come from Matt Ramsay from Cowen. Your line is open.
Yes, thank you very much. Good afternoon, good morning. Jensen, I had a couple of questions for you. On the first one, I noticed in the BlueField DPU roadmap, eventually you guys integrate sort of the full AI engine into the DPU. So I wondered if you could talk a little bit about on the acceleration side, what AI opportunities and low-hanging fruit might be available, particularly in the security domain. Then the second question is, you now have AI acceleration, you have a DPU acceleration. For an integrated stack, I mean, whether you buy ARM or don't ARM, buy ARM, it seems like you could make a CPU.
Maybe you could talk a little bit about the pros and cons of going after that one piece of the TAM that you're not addressing today. Thanks.
Yeah, excellent question. Number 1, give you an example. Intrusion detection. Intrusion detection will be distributed, not at the perimeter. The data center will be protected at every single transaction, at every single node, at every single application. It will not be protected only at the doors. The reason for that is, don't forget, all of the intruders are largely inside the building already. In the future, you also have public clouds. The entire data center is open to the world. You can't allow to have east-west intrusion. The moment an intruder goes inside a data center, goes sideways, east-west into the data center, imagine the damage they could do. Security is incredible. Every single node will become a super firewall. Firewall technology today, intrusion detection technology today based on AI. We've gotta put AI processing right at the network. Number 1. Number 2, network shaping.
You know, network traffic shaping. That's an AI problem. It's an optimization problem that cannot be done with a simple set of equations. It's a heuristics problem. It's a dynamic problem. It's one of those computer science problems that goes, "Well, it depends." You know, what's the solution? Well, it depends. The well, it depends requires intelligence, and so we want to put intelligence right at the network. What computer science is and what we've described in the past as in-network computing. These are two examples of in-network computing, okay? Very big deal. We're super excited about doing that.
This is one of the reasons why, one of the reasons. If you go back to the early days and when I talked about the acquisition of Mellanox, I talked about in-network computing, I talked about how the network itself will become a fabric where we do a lot of computation, a lot of AI. This is the first step. There are several things that we can't do. There are many things we can do with ARM. There are many things we can do with ARM, and we can build a CPU. However, there will never be another CPU built like ARM, ever, and it's not a computer science problem anymore. It started out as a computer science problem 30 years ago. An energy-efficient architecture that's designed like no other. That was visionary.
The reason for that is because if you're energy efficient and the world hits the wall because of the end of Moore's Law, you've got more runway. There's more runway in ARM than there is in x86. There's just more. I mean, that's just the bottom line. The architecture's more energy efficient, therefore they got more runway. However, Moore's Law has ended for them as well. We need to bring accelerated computing to ARM. ARM, number 1, is just a genius of an architecture, but it's also a genius of a business model. The reason for that is because they wanted ARM to be the most popular CPU in the world. They want it to be used in all kinds of things, from, you know, well, from everything to everything, cars to phones to televisions to you name it.
That required a business model that allowed them to license their IP in soft form, soft, flexible form, that fits into other people's chips, because many computers in the future are full. It's just the whole data center's on one chip. The whole computer's on one chip. The phone's on one chip. TV's on one chip. There's no such thing as two chips, you know? Just on one chip. The second thing that they have, because of the business model, created the third thing, which is ultimately the most valuable thing today, which is their vast ecosystem. The execution machine of ARM that knows how to build soft IP, their IP, for smartphones, for embedded systems, microcontrollers, to data centers, and now increasingly PCs.
Their number of CPU cores, the engine they have behind it that creates the soft IP, productizing it and delivering it to customers, helping them integrate it into their chips, that's phenomenal. That's phenomenal. As a result, they've created this ecosystem of thousands of chip companies. They shipped 22 billion chips last year. NVIDIA shipped 0.1 billion. The difference between NVIDIA and ARM is 22 billion, as you can, you know. That just kind of puts it in perspective, the reach of their ecosystem. That's the value to us. You can't do that by building another CPU. It doesn't matter what another CPU is. I don't care what it is. I just don't think the ecosystem will ever be as rich as this one. It took 30 years to build.
It took enormous character and enormous vision to have built it. The team that built it is phenomenal. They love Cambridge. They work in Cambridge. They, you know, it's a great computer science team. I love the work that they've done. That ultimately is the asset that we're buying, is that combination of all that and the ecosystem that they've built up that just simply won't get replicated again.
Your next question will come from Harlan Sur from JP Morgan.
Morning. Thank you for taking my question. You know, one of the powerful dynamics that the team is creating for itself is leveraging the entire portfolio to target vertical markets. Question for Kimberly, for the vertical market focus, like healthcare, the drug discovery opportunity that you talked about is primarily focused on high-performance computing platforms like DGX. Moving across the portfolio, how is the team leveraging the edge and inferencing portfolio like EGX and Jetson product families within your healthcare franchise? How involved is your team in helping to define next-generation hardware and system platforms?
Thanks, Harlan. Thanks for the question. In drug discovery, you're right, the DGX systems, as it's a full stack architecture, it does literally cross every computer science domain. Yes, it's very heavy and upcoming in artificial intelligence, but of course it takes advantage of accelerated computing. It's also going to be very instrumental in data science and machine learning and data analytics. As we move into these gigantic data sets of genomic data sets or even doing the compound screening, what we do is we generate huge outputs of that analysis that needs analytics to really pull out the necessary information.
We literally leverage every corner of NVIDIA's, you know, extreme and powerful and, you know, world-class computing architectures, whether it be accelerated computing, data science, machine learning, deep learning and natural language processing. The other areas, and Justin touched on it briefly, you know, we have for decades been working in edge devices, frankly, revolutionizing the medical instruments that care for us and see inside our bodies and extract our DNA and build out the three billion letters that make us up. These instruments are one of the edge platforms that in the future, just like our phones and our cars, they want to be software-defined.
The sensor technology that is created in these edge medical instruments is incredibly powerful, and we can continue to get amazing insights and new information out of the sensor technology by applying artificial intelligence. It can't remain the old way of deploying these instruments, where you would sell a couple million dollar CT scanner, and it would refresh every 10, sometimes 15 years. We can't carry on that way anymore. Edge computing is absolutely going to be vital to what is going to be the software-defined future of medical instruments. And then you can imagine not only the new instruments will leverage everything we've built in our Jetson platform, but it will also leverage what we're building in our EGX platform.
Being able to remotely manage provision and securely operate edge nodes that need to be updated with new AI applications over time is extremely vital. The example Justin gave was at Northwestern Medicine, where actually there are plenty of sensors that already exist in the healthcare environment, microphones, cameras, that can now be coupled with artificial intelligence and conversational AI so that brand-new services can enter the healthcare and hospital environment, just like we would expect, just like we have at our homes. We can talk to smart speakers. We're unlocking and enabling that environment in the healthcare system today using, you know, what is DeepStream for, you know, that's used in smart cities, what is Jarvis that is being used in a lot of our conversational AI platforms.
We can leverage all that technology and create a domain-specific, you know, application framework to literally overnight allow application developers to develop new applications that can be deployed and then leverage the fleet command system of EGX to deploy them in the tens of thousands of installations and environments that they're gonna wanna live at the edge. Whether it's new instruments, augmenting existing instruments with compute, coupling all of them, the amazing sensors that already live in our healthcare environments with AI, and then being able to manage securely and deploy applications with EGX, the future is incredibly bright. We see smart hospitals now and these applications popping up literally out of the woodwork, of course, as you can imagine, to respond to the great demand that the pandemic is putting on the healthcare system.
Cool. Thanks, Colette.
Your next question will come from Rajvindra Gill from Needham & Company. Your line is open.
Yes, thank you, and thanks for this excellent presentation. Just when we're thinking about this new cloud class of data center products, the DPU, how do we think about kind of the pricing dynamic in terms of the revenue opportunity relative to other class of chips that you're selling? Just trying to understand how this will be kinda integrated and what the go-to-market strategy will be for that.
Yeah, there are two pillars that we could look towards. You know, one, of course, is the baseline, which is what a SmartNIC goes for. It's a few hundred dollars. Then the other is the amount of CPU offload that you provide such that the application performance of the server gets a boost. Effectively, you're going to add a DPU to a server, and my expectation over time is that you'll double the performance of the server. That kinda put the value on it, you know? Somewhere between those two is pricing, and, you know, we'll work it out as we go.
Thank you.
Yeah. Thanks a lot.
Your next question will come from Ambrish Srivastava from BMO. Your line is open.
Hi. Thank you very much. Jensen, I had a question on inferencing, and then I had one on gaming as well. Maybe, Colette, you could answer that. On the inferencing, you shared a pretty revealing piece of data in terms of crossing over CPU, and then you gave a projection for a 90% market share. Intel is the incumbent. Could you please just help us understand kinda in terms of how much of the training you could translate into NVIDIA inferencing, which gives you the confidence that you get to 90% share? Also, what are you assuming the competition does in that market? I know, Jensen, you don't take competition lightly.
On the gaming side, Colette, I know in the past such events, you guys have been very kind in giving us, the components of the CAGR in terms of units. ASP, I was wondering if you could help us out there as well. You also said that gaming, Sorry, I'm asking multi-part question, but you said this is fastest ramp ever. If you could just give a little bit more details around that will be helpful. Thank you.
It wasn't actually about share, it was just about compute, aggregate compute in the cloud. For example, in my PC right now, in my PC, I have a decent GPU and the aggregate computational throughput of my PC is 99% my GPU. In fact, the aggregate CPU versus GPU compute inside a data center, inside a supercomputer or high-performance computer is about 99% GPU. The reason for that is because that's its job. Its primary job isn't GPU acceleration. Its primary job is to do compute. It's not to say that the CPU is not useful. That's just not its job.
Its job is to manage the application, you know, manage the operating system, manage, orchestrate the processing of the applications. You know, figuring out who gets priority. Those things are really important. Moving things around, launching things, those are really important. If those single-threaded performance, single-threaded application code is not properly processed, then the single-threaded part of your code becomes the critical path, otherwise known as Amdahl's law. So that's all I meant, is that the vast majority of the cloud going forward will be accelerated. In fact, that is a foregone conclusion at this point. It's a corollary to Moore's Law has ended, and therefore, you have to look for another approach to accelerate applications because Moore's Law has ended.
Yet, on the other hand, the emergence of this new type of application called AI that requires so much computation at exactly the time when CPU performance is not gonna double every couple years, and you can't just wait for it. So, the world has to lift their code and refactor it and take advantage of acceleration. It happened at a perfectly good time. The reason for that is because of cloud computing. Because of cloud computing, the world had to refactor its application, and it disaggregated it. When it disaggregated it, whenever you disaggregate and you containerize certain modules, certain microservices, you might as well accelerate it. You must because you're infusing it with AI anyway.
I think that the confluence of both the end of Moore's Law and the beginning of AI, and the emergence of this new type of data center scale computing we call it, are, you know, are all working in its favor. Colette?
Let me see if I can answer your question regarding our split in gaming between our ASP growth and our unit growth. Both of these are very important to our overall growth, and over this five-year period of time, both of them have contributed. One of the key things to note in terms of what is both influencing our units and our ASP growth is the onset of laptops, notebooks, gaming. High-end gaming notebooks for this market have really grown quite well, and they have great ASPs for us as well. We continue to uplift overall ASPs as our new gamers coming on board tend to take on the RTX, tend to take on our higher performing overall GPUs just to start off with.
We still provide a slew of different overall price points to attract every single gamer, but you can see our ASP is probably, over this period, reaching double digits growth. There is still a great opportunity as we go forward. We also announced that right now the overall Ampere architecture for gaming is growing quite well. The launch is probably the best launch in history. We are in an opportunity to do it a little bit different than what we did with overall Turing. Turing was an opportunity for us to address ray tracing for the very first time and really start that market in essentially a chicken and the egg type of market. The hardware availability began the beginning of the overall software that was there.
We also had to take a little bit of a pause in terms of some of that launches, and really address the full scope of GPUs over a longer period of time. We're excited both in terms of the performance improvement that you have with the overall Ampere as well as the great price point. So far the launch, the ramp is growing quite well. We're just really pleased in terms of how things are going.
Okay. Thank you for the detail. Thank you.
This will bring us to the end of today's question and answer session. I turn the call back over to Jensen for closing remarks.
This is an amazing time for the computer industry and the world. The age of AI has begun. NVIDIA is in full throttle to bring this capability to the world. The breakthroughs of AI can now bring automation to the world's largest industries. This new type of software requires a new type of computer to write the software, validate the software, deploy the software. AI is understandably complex. From the chips, systems, software, algorithms to applications, AI requires reinventing every layer of the computing stack. NVIDIA is in full throttle, building the full stacks for each computing domain and each computing environment, and from cloud, PC, enterprise, autonomous machine to edge. NVIDIA's in full throttle building the computing company for the age of AI. Thanks for joining us at GTC.
Thank you, everyone. This will bring us to the conclusion of today's conference call. You may now disconnect.