NVIDIA (NVDA) Q1 2023 earnings call transcript

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2023. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2023.

The content of today's call is NVIDIA's property. It can be reproduced or transcribed without our prior written consent. During this call, 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 the disclosure in today's earnings release. Our most recent Forms 10K and 10Q and the reports that we may file on Form 8K with the Securities and Exchange Commission.

All our statements are made as of today, May 26, 2022, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find the reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website.

With that, let me turn the call over to Colette.

Thanks, Simona. We delivered a strong quarter, driven by record revenue in both data center and gaming, with strong fundamentals and execution against a challenging macro backdrop. revenue of $8.3 billion was a record, up 8% sequentially and up 46% year-on-year. Data Center has become our largest market platform, and we see continued strong momentum going forward. Starting with gaming, revenue of $3.6 billion rose 6% sequentially and 31% year-on-year, powered by the GeForce RTX 30 Series product cycle.

Since launching in the fall of 2020, the RTX 30 Series has been our best gaming product cycle ever. The gaming industry has grown tremendously, with 100 million new PC gamers added in the past two years, according to Nuzu. And, NVIDIA RTX has set new standard for the industry, with demand from both first-time GPU buyers as well as those upgrading their PCs to experience the 250-plus RTX optimized games and apps, double from last year.

We estimate that almost a third of the GeForce gaming GPU installed base is now on RTX. RTX has brought tremendous energy into the gaming world and has helped drive a sustained expansion in our higher-end platforms and installed base, with significant runway still ahead. Overall, end demand remained solid, though mixed by region, and demand in Americas remained strong. However, we started seeing softness in parts of Europe related to the war in the Ukraine and parts of China due to the COVID lockdowns. As we expect some ongoing impact, as we prepare for a new architectural transition later in the year, we are projecting gaming revenue to decline sequentially in Q2.

Channel inventory has nearly normalized, and we expect it to remain around these levels in Q2. The extent in which cryptocurrency mining contributed to gaming demand is difficult for us to quantify with any reasonable degree of precision. The reduced pace of increase in Ethereum network hash rate likely reflects lower mining activity on GPUs. We expect a diminishing contribution going forward.

Laptop gaming revenue posted strong sequential and year-on-year growth, driven by the ramp of the NVIDIA RTX 30 Series lineup. With this year's spring refresh and ahead of the upcoming back-to-school season, there are now over 180 laptop models featuring RTX 30 Series GPUs and our energy-efficient, thin and light Max-Q technologies, up from 140 at this time last year. Driving this growth are not just gamers, but also the fast-growing category of content creators from whom we offer dedicated NVIDIA studio drivers.

We've also developed applications and tools to empower artists, from Omniverse for advanced 3D and collaboration, to Broadcast for live streaming, to Canvas for painting landscapes with AI. The creator economy is estimated at $100 billion and powered by 80 million individual creators and broadcasters. We continued to build out our GeForce Now cloud gaming service. Gamers can now access RTX 3080 class streaming, our new top-tier offering with subscription plans of $19.99 a month. We added over 100 games to the GeForce Now library, bringing the total to over 1,300 games.

And last week, we launched Fortnite on GeForce Now with touch controls for mobile devices streaming through the Safari web browser on iOS and the GeForce Now Android app. Moving to pro-visualization. Q1 revenue with $622 million was down sequentially 3% and up 67% from a year ago. Demand remained strong as enterprises continued to build out their employees' remote office infrastructure to support hybrid work.

Sequential growth in the mobile workstation GPUs was offset by lower desktop revenue. Strong gear-on-gear growth was supported by the NVIDIA RTX Ampere architecture product cycle. Top use cases include digital content creation at customers such as Sony Pictures Animation and medical imaging at customers such as Medtronic.

In just its second quarter of general availability, our Omniverse Enterprise software is being adopted by some of the world's largest companies. Amazon is using Omniverse to create digital twins to better optimize warehouse design and flow and to train more intelligent robots. Kroger is using Omniverse to optimize store efficiency with digital twin store simulation. And PepsiCo is using Omniverse digital twins to improve the efficiency and environmental sustainability of its supply chain. Omniverse is also expanding our GPU sales pipeline, driving higher end and multiple GPU configurations.

The Omniverse ecosystem continues to rapidly expand with third-party developers in the robotics, industrial automation, 3D design, and rendering ecosystems developing connections to Omniverse. Moving to automotive. Q1 revenue of $138 million increased 10% sequentially and declined 10% from the year-ago quarter. Our drive, Oren SoC, is now in production and kicks off a major product cycle with auto customers ramping in Q2 and beyond. Oren has great traction in the marketplace with over 35 customer wins from automakers, truck makers, and robotaxi companies. In Q1, BYD, China's largest EV maker and Lucid, an award-winning EV pioneer, were the latest to announce that they are building their next-generation fleets on Drive Oren.

Our automotive design WIM pipeline now exceeds $11 billion over the next six years, up from $8 billion just a year ago. Moving to data center. Record revenue of $3.8 billion grew 15% sequentially and accelerated to 83% growth year-on-year. Revenue from hyperscale and cloud computing customers more than doubled year-on-year, driven by strong demand for both external and internal workloads.

Customers remain supply constrained in their infrastructure needs and continue to add capacity as they try to keep pace with demand.

Revenue from vertical industries grew a strong double-digit percentage from last year. Top verticals driving growth this quarter include consumer internet companies, financial services, and telecom. Overall, data center growth was driven primarily by strong adoption of our A100 GPU for both training and inference with large volume deployments by hyperscale customers and broadening adoption across the vertical industries.

Top workloads include recommender systems, conversational AI, large language models, and cloud graphics. Networking revenue accelerated on strong, broad-based demand for our next-generation 25, 50, and 100-gig Ethernet adapters. Customers are choosing NVIDIA's networking products for their leading performance and robust software functionality. In addition, networking revenue is benefiting from growing demand for DGX SuperPods and cross-selling opportunities. Customers are increasingly combining our compute and networking products to build what are essentially modern AI factories, with data as the raw material input and intelligence as the output. Our networking products are still supply constrained, though we expect continued improvement throughout the rest of the year.

One of the biggest workloads driving adoption of NVIDIA AI is natural language processing, which has been revolutionized by transformer-based models. Recent industry breakthroughs traced to transformers include large language models like GPT-3, NVIDIA Megamole BART for drug discovery, and DeepMind AlphaFold for protein structure prediction.

Transformers allow self-supervised learning without the need for human-labeled data. They enable unprecedented levels of accuracy for tasks such as text generation, translation, summarization, and answering questions. To do that, transformers use enormous training data sets and very large neural networks, well into the hundreds of billions of parameters. To run these giant models without sacrificing low inference times, customers like Microsoft are increasingly deploying NVIDIA AI, including our NVIDIA Ampere architecture-based GPUs and full software stack.

In addition, we are seeing a rising wave of customer innovation using large language models that is driven by increased demand for NVIDIA AI and GPU instances in the cloud. At GTC, we announced our next-generation data center GPU, the H100, based on the new upper architecture. Packed with 80 billion transistors, H100 is the world's largest, most powerful accelerator, offering an order of magnitude leap in performance over the A100. We believe H100 is hitting the market at the perfect time.

H100 is ideal for advancing large language models and deep recommender systems, the two largest scale AI workloads today. We are working with leading server makers and hyperscale customers to qualify and ramp H100. as well as the new DGX H100 AI computing system will ramp in volume late in the calendar year. Building on the H100 product cycle, we are on track to launch our first ever data center CPU, Grace, in the first half of 2023.

Grace is the ideal CPU for AI factories. This week at Computex, we announced that dozens of server models based on Grace will be brought to market by the first wave of system builders, including ASUS, Foxconn, Gigabyte, QCT, Supermicro, and YWIN. These servers will be powered by the NVIDIA Grace CPU Superchip, which features two CPUs, and the Grace Upper Superchip, which pairs an NVIDIA Upper GPU with an NVIDIA Grace CPU in an integrated model.

We've introduced new reference designs based on Grace for the massive new workloads of next-generation data centers. CGX for cloud graphics and gaming, OVX for digital twins or Omniverse, and HDX for HPC and AI. These server designs are all optimized for NVIDIA's rich, accelerated computing software stacks and can be qualified as part of our NVIDIA certified systems lineup.

The enabler for the Grace Hopper and Grace Superchips is our ultra-energy-efficient, low-latency, high-speed memory coherent interconnect called NVIDIA Link, which scales from die to die, chip to chip, and system to system. With NVIDIA Link, we can configure Grace and Hopper to address a broad range of workloads. Future NVIDIA chips, the CPUs, GPUs, DPUs, NICs, and SOCs will integrate NVLink just like Grayson Hopper based on our world-class CERDES technology.

We are making NVLink open to customers and partners to implement custom chips that connect to NVIDIA's platforms. In networking we kicking off a major product cycle with the introduction of Spectrum 4 the world first 400 gigabit per second end Ethernet networking platform including the Spectrum 4 switch ConnectX 7 SmartNIC, Bluefield 3 DPU, and the DOCA software. Built for AI, NVIDIA Spectrum 4 arrives as data centers are growing exponentially and demanding extreme performance, advanced security, and powerful features to enable high-performance, advanced virtualization, and simulation at scale.

Across our businesses, we are launching multiple new GPU, CPU, DPU, and SOC products over the coming quarters with a ramp in supply to support the customer demand. demand. Moving to the rest of the P&L, gap gross margin for the first quarter was 65.5 percent, and non-gap gross margin was up 67.1 percent, up 90 basis points from a year ago, and up 10 basis points sequentially. We have been able to offset rising costs and supply chain pressures. We expect to maintain gross margins at current levels in Q2. Going forward, as new products ramp and software becomes a larger percent of revenue, we have opportunities to increase gross margins longer term. Gap operating margin was 22.5%, impacted by a $1.35 billion acquisition termination charge related to the ARM transaction.

Non-GAAP operating margin was 47.7%. We are closely managing our operating expenses to balance the current macro environment with our growth opportunities, and we've been very successful in hiring so far this year and are now slowing to integrate these new employees. This also enables us to focus our budget on taking care of our existing employees as inflation persists. We are still on track to grow our non-GAAP operating expenses in the high 20s range this year. We expect sequential increases to level off after Q2, as the first half of the year includes a significant amount of expenses related to the bring-up of multiple new products, which should not reoccur in the second half. During Q1, we repurchased $2 billion of our stock.

Our Board of Directors increased and extended our share repurchase program to repurchase an additional common stock up to a total of $15 billion through December 2023. Let me now turn to the outlook for the second quarter of fiscal 2023. Our outlook assumes an estimated impact of approximately 500 million relating to Russia and China COVID lockdowns. We estimate the impact of lower sell-through in Russia and China to affect our Q2 gaming sell-in by 400 million. Furthermore, we estimate the absence of sales to Russia to have a 100 million impact on Q2 in data center. We expect strong sequential growth in data center and automotive to be more than an offset by the sequential decline in gaming.

Revenue is expected to be $8.1 billion, plus or minus 2%. Gap and non-gap gross margins are expected to be 65.1% and 67.1%, percent, respectively plus or minus 50 basis points. GAP operating expenses are expected to be 2.46 billion. Non-GAP operating expenses are expected to be 1.75 billion. GAP and non-GAP other income and expenses are expected to be an expense of approximately 40 million, excluding gains and losses on non-affiliated investments. GAP and non-GAP tax rates are expected to be 12.5% plus or minus 1% excluding discrete items. And capital expenditures are expected to be approximately $400 million to $450 million. Further financial details are included in the CFO commentary and other information available on our IR website.

In closing, let me highlight the upcoming events for the financial community. We'll be attending the B of A, Securities Technology Conference in person on June 7th, where Jensen will participate in a keynote fireside chat. Our earnings call to discuss the results of our second quarter of fiscal 2023 is scheduled for Wednesday, August 24th. We will now open the call for questions.

Operator, could you please call for questions?

Yes, CJ, thanks for the question. You captured a lot of the dynamics well in your question. The underlying dynamics of the gaming industry is really solid.

Net of the situation with COVID lockdown in China and Russia, the rest of the market is fairly robust. And we expect the gaming dynamics to be intact. You know, the several things that are driving the gaming industry, in the last two years alone, 100 million new gamers came into the PC industry. The format has expanded tremendously. And the ways that people are using their PCs to connect with friends, to be an influencer as a platform for themselves, use it for broadcast.

So many people are now using their home PCs as their second workstation, if you will, second studio, because they're also working from home. It is our primary way of communicating these days. The need for GeForce PCs have never been greater. And so I think the fundamental dynamics are really good. And so as we look into the second half of the year, it's hard to predict exactly when COVID and the war in Russia is going to be behind us.

But nonetheless, the governing dynamics of the gaming industry is great.

Yeah, I appreciate the question. Let's see. The first point that you made is a very big point. The next generation of servers that are being teed up right now are all Gen 5. The I. O. performance is substantially higher than what was available before. And so you're going to see a pretty large refresh as a result of that. brand new networking cards from our company and others. Gen5, of course, drives new platform refresh. And so we're perfectly timed to ramp into the Gen5 generation with Hopper. There are a lot of different system configurations you want to make. If you take a step back and look at the type of systems that are necessary for data processing, scientific computing, machine learning and training, inference done in the cloud for hyperscale nature, done on prim for enterprise computing, done at the edge.

Each one of these workloads and deployment locations, the way that you manage would dictate a different system architecture. So there isn't one size that fits all, which is one of the reasons why it's so terrific that we support PCI Express, that we innovated chip-to-chip interconnect before anybody else did. This is now some seven years ago. We're in our fourth generation of MVLink. That allows us to connect two chips next to each other, two dyes two chips two modules uh two uh two sxm modules to two systems to multiple systems and so so our um our coherent uh chip to chip link mv link has made it possible for us to mix and match uh chips dyes packages systems in all of these different types of configurations and and i think that over time you're going to see even more types of configurations and the reason for that has to do with a couple of very important new type of data centers that are emerging.

And you're starting to see that now with fairly large installations infrastructures with NVIDIA HPC and NVIDIA AI. These are really AI factories where you're processing the data, refining the data, and turning that data into intelligence. These AI factories are essentially running one major workload and they're running at 24 7. deep recommender systems is a good example of that in the future you're going to see large language models essentially becoming a platform themselves that would be running 24 7 hosting a whole bunch of applications and then on the other end you're seeing data centers at the edge that are going to be robotics or autonomous data centers that are running 24-7. They were going to be running in factories and retail stores and warehouses, logistic warehouses, all over the world.

So, these two new type of data centers are just emerging, and they also have different architectures. So, I think that the net of it all is that our ability to support every single workload, because we have a universal accelerator running every single workload from data processing to data analytics to high-performance computing to training to inference, that we can support ARM and x86, that we support PCI Express to multi-system NVLink, to multi-chip NVLink, to multi-DIE NVLink. That capability for us makes it possible for us to really be able to serve all of these different segments.

With respect to vertical integration, I think that system integration, the better way of saying that is that system integration is going to come in all kinds of different ways. We're going to do semi-custom chips, as we've done with many companies in the past, including Nintendo. We'll do semi-custom chiplets, as we do with NBLink. EnvyLink is open to our partners, and they could bring it to any fab and connect it coherently into our chip. We could do multi-module packages. We could do multi-package systems. So there's a lot of different ways to do system integration.

Sure. Let me start first with what does that mean to gaming? What does that mean to gaming for Q2? We do expect gaming to decline into Q2. We still believe our end demand remains very strong. Ampere has just been a great architecture, and there's many areas where we continue to see strength and growth in both our sell-through and probably what we will see added into that channel as well. But in total, Q2 gaming will decline from last quarter from Q1, that it will probably decline in the teens as we try and work through some of these lockdowns in China, which are holding us up.

So overall, the demand for gaming is still strong. We still expect end demand to grow year over year in Q2.

Yeah, thanks, Mark. We had a record data center business this last quarter. We expect to have another record quarter. this quarter and we're fairly enthusiastic about the second half.

AI and data-driven machine learning techniques for writing software and extracting insight from the vast amount of data that companies have is incredibly strategic to all the companies that we know because in the final analysis, AI is about automation of intelligence and most companies are about domain-specific intelligence. We want to produce intelligence and there are several techniques now that have been created to make it possible for most companies to apply their data to extract insight and to automate a lot of the predictive things that they have to do and do it quickly. And so I think the trend that you hear other people experiencing about machine learning, data analytics, data-driven insights, artificial intelligence, however it's described, is all exactly the same thing.

And it's sweeping just about every industry and every company. Our networking business is also highly supply constrained. Our demand is really, really high and it requires a lot of components aside from just our chips components and transceivers and connectors and cables and just it's a really it's a complicated system the network and there are many physical components and so so the supply chain has been problematic we're doing our best and our our supply has been increasing from q4 to q1 we're expecting it to increase in q2 and increase in q3 and q4 And so we're really, really grateful for the support from the component industry around us, and we'll be able to increase that. With respect to software, there are two – well, first of all, there are all kinds of machine learning models, computer vision, speech AI, natural language understanding, all kinds of robotics applications.

The most, probably the largest, the most visible one is self-driving cars, which is essentially a robotic AI. And then recently, this incredible breakthrough from an AI model called Transformers that has led to really, really significant advances in natural language understanding. And so there are all these different types of models. There are thousands and thousands of species of AI models in all these different industries.

One of my favorites, I'll just say very quickly, and I'll answer that question about the software. One of my favorites is using transformers to understand the language of chemistry, or using transformers and using AI models to understand the language of proteins, amino acids, which is genomics. To apply AI to understand, to recognize the patterns, to understand the sequence, and essentially understand the language of chemistry and biology is a really, really important breakthrough.

And all of this excitement around synthetic biology, much of it stems back to some of these inventions. But anyhow, all of these different models need an engine to run on. And that engine is called NVIDIA AI. In the case of hyperscalers, they can cobble together a lot of open source, and we provide a lot of our source to them and a lot of our engines to them for them to operate their AI. But for enterprises, they need someone to package it together and be able to support it and refresh it, update it for new architectures, support old architectures and their install base, etc. , and all the different use cases that they have. And so that engine is called NVIDIA AI.

It's almost like a SQL engine, if you will, except this is an engine for artificial intelligence. There's another engine that we provide, and that engine is called Omniverse, and it's designed for the next wave of AI, where artificial intelligence has to not just manipulate information like recommender systems and conversational systems and such, but it has to interact with physical systems. whether it's interacting with physics directly, meaning robotics, or being able to automate physical systems like heat recovery steam generators, which is really important today.

And so Omniverse is designed to be able to sit at that interface, that intersection between simulation and artificial intelligence, and that's what Omniverse is about. Omniverse has now, let's see, we're still early in the deployment of Omniverse for commercial license. It's been a couple of quarters now since we've released Omniverse Enterprise. And I think at this point we have 10% of the world's top 100 companies that are already customers, licensing customers. substantially more who are evaluating. I think it's been downloaded nearly 200,000 times.

It is being tried in some 700 companies. And Collette highlighted some of the companies. You might see some of the companies that are using it in all kinds of interesting applications at GTC. And so I fully expect that the NVIDIA AI engine, the Omniverse engine, are going to be very successful for us in the future and contribute greatly to our earnings.

Yeah, Vek, let me first answer the question that you asked regarding any new products. as we look at Q2. As we discuss about it, most of the ramp that we have of our new architectures, we're going to see in the back half of the year. We're going to start to see, for example, Hopper will probably be here in Q3, but starting to ramp closer to the end of the calendar year. So you should think about most of our product launches to be ramping in the second half of the year on that part. I'll turn it over for Jensen for the rest.

Thanks. Our data center demand is strong and remains strong. Hyperscale and cloud computing revenues, as you mentioned, has grown significantly, has doubled year over year. And we're seeing really strong adoption of A100. A100 is really quite special and unique in the world of accelerators. And this is one of the really, really great innovations as we extended our GPU from graphics to CUDA to TensorCore GPUs. It's now a universal accelerator, and so you could use it for data processing, for ETL, for example, extract, transform, and load. You could use it for database acceleration. Many SQL functions are accelerated on NVIDIA GPUs. We accelerate RAPIDS. We accelerate, which is the Python version, data center scale version of Pandas.

We accelerate Spark 3.0. And so from database queries to data processing to extraction and transform and loading of data before you do training and inference and whatever image processing or other algorithmic processing you need to do can be fully accelerated on A100. And so we're seeing great success there. At the core and closer to what is happening today, you're seeing several different, very important new AI models that are being invested in at very, very large scale and with great urgency.

You probably have heard about deep recommender systems. This is the economic engine, the information filtering engine of the Internet. If not for the recommender system, it would be practically impossible for us to enjoy our Internet experience, shopping experience, with trillions of things that are changing in the world every day, constantly, and to be able to use your three-inch phone to even engage the Internet. And so all of that magic is made possible by this incredible thing called a recommender system. The second thing is conversational AI. You're seeing chatbots and website customer service, even live customer service being now supported by AI, conversational AI, has an opportunity to enhance the customer service on the one hand.

On the other hand, supplement for a lot of labor shortage. And then the third is this groundbreaking piece of work that's related to transformers that led to natural language understanding breakthrough. But within it is this incredible thing called large language models. which embeds human knowledge because it's been trained in so much data. And we recently announced Megatron 530B, and it was a collaboration we did with Microsoft, the foundation of, I think they call it Turing.

And this language model and others like it, like OpenAI, GPT-3, are really transformative, and they take an enormous amount of computation. However, the net result is a pre-trained model that is really quite remarkable. We're working with thousands of startups, large companies that are building, who are using the public cloud. And so it's driving a lot of demand for us in the public cloud.

I think we have now 10,000 AI inception startups that are working with us and using NVIDIA AI, whether it's on-prem or in the cloud. It saves money because the computation time is significantly reduced. The quality of service is a lot better, and they could do greater things. And so that's driving AI in the cloud. And so all of these different factors, whether it's just the industrial recognition of the importance of AI, the transformative nature of these new AI models, recommender systems, large language models, conversational AI, The thousands of companies around the world that are using NVIDIA AI in the cloud, driving public cloud demand, all of these things are driving our data center growth. And so we expect to see data center demand remain strong.

Thanks, Tim, for the question. Let me try a bet here on the China and Russia. Two very different things. The current China lockdowns that we are seeing, interestingly, has implications to both supply and demand. We have seen challenges in terms of the logistics throughout the country, things going in, out of the country. It puts a lot of pressure on just logistics that were already under pressure. From a demand perspective, it has also been hit from the gaming side. You have very large cities that are in full lockdown, focusing really on other important things for the citizens there. So it's impacting our demand. We do believe that they will come out of COVID and the demand for our products will come back.

We do believe that will occur. The supply will sort it out. It's very difficult to determine how. Now, in the case of Russia, we're not selling to Russia. That's something that we had announced earlier last quarter. But there were plans and Russia has been a part of our overall company revenue. probably about 2% of our company revenue historically, and a little larger percentage when you look at our gaming business. Hope that helps.

Yeah, Ambrish, thanks for the question. On first principles, it should be the case that our visibility of data centers is vastly better, vastly better than a couple of years ago. And the reason for that is several. One, if you recall a couple, two, three years ago, deep learning and AI was starting to accelerate in the most computer science deep companies in the world with CSPs and hyperscalers. And and but just about everywhere else, it was still quite nascent. And there was a couple of reasons for that. Obviously, the the the understanding of the technology is not as not as not as pervasive at the time.

The type of industrial use cases for artificial intelligence requires labeling of data. that's really quite difficult. And then now with Transformers, you have unsupervised learning and other techniques, zero-shot learning that allows us to do all kinds of interesting things without having to have human-labeled data. We even have synthetic generated data with Omniverse that helps customers do data generation without having to label data, which is either too cost-effect, too costly, or quite frankly, oftentimes impossible. And so now the knowledge and the technology has evolved to a place that most of the industries could use artificial intelligence at a fairly effective way and in many industries rather transformative. And so I think, number one, we went from clouds as hyperscalers to all of industry.

Second, we went from training-focused to inference. Most people thought that inference was going to be easy. It turns out inference is by far the harder. And the reason for that is because there's so many different models, and there's so many different use cases, and so many quality of service requirements, and you want to run these inference models in as small of a footprint as you can. And so when you scale out, the number of users that use the service is really quite high. So using Acceleration and using NVIDIA's platform, we could inference any model from computer vision to speech to, you know, chemistry to biology, you name it.

And we do it so quickly and so fast that the cost is very low. And so the more Acceleration you do, the more money you will save. And I think that wisdom is absolutely true. And so the second dimension is training to inference. The third dimension is that we now have so many different types of configurations of systems that we can go from high-performance computing systems all the way to cloud, to on-prem, to edge. And then the final concept is really this industrial deployment now of AI that's causing us to be able to, in just about every industry, find growth. And so, as you know, our cloud and hyperscalers are growing very, very quickly. However, the vertical industries, which is the financial services and retail and telco and all of those vertical industries have also grown very, very nicely.

And so in all of those different dimensions, our visibility should be a lot better. And then starting a couple years ago, adding the Mellanox portfolio to our company, we're able to provide a lot more solution-oriented end-to-end platform solutions for companies that don't have the skills and don't have the technical depth to be able to stand up these sophisticated systems.

And so our networking business is going very, very nicely as well.

Either one of us can answer.

The answer is yes. The answer is yes. We see a strong demand in data centers, hyperscale to cloud computing, the vertical industries. Ampere is going to continue to scale out. It's been qualified in every single company in the world. And so after two years, it remains the best universal accelerator on the planet. And it's going to continue to scale out in all these different domains and different markets. We're going to layer on top of that a brand-new architecture, Hopper. We're going to layer on top of that brand-new networking architectures, Quantum 3, CX7, Bluefield 3, and we have increasing supply. And so we're looking forward to an excellent quarter next quarter again for data centers and going into the second half.

Yeah, so let me start here and I'll see if Jensen wants to add more of that. Our purchase obligations as well as our prepays have two major things to keep in mind. One, for the first time ever, we are prepaying to make sure that we have that supply and those commitments long term. And additionally, on our purchase obligations, many of them are for long lead time items that are a must for us to procure to make sure that we have for the products coming to market.

A good percentage of our purchase commitments is for our data center business, which you can imagine are much larger systems, much more complex systems, and those things that we are procuring to make sure we can feed the demand, both in the upcoming quarters and further. Areas in terms of where we are still a little bit supply constrained. Our networking, our demand is quite strong. We've been improving it each time, but yes, we still have demand, excuse me, supply concerns with networking still. So is there others that you want to add on, Jensen?

No, I thought you were perfect. That was perfect.

So several data points. We are just starting. We have just started shipping Oren in the first quarter of shipping production, Oren. Oren is a robotics processor. It's designed for a software-defined robotic car or robotic, you know, pick-and-placer or robotic mover, logistics mover. And we've been designed into 35 car and trucks and robo-taxi companies and more others. If you include logistics movers and last mile delivery systems and farming equipment, the number of design wins for Oren is really quite fantastic. Ornn is a revolutionary processor, and it's designed as a, if you will, a data center on a chip.

And it is the first data center on a chip that is robotic, processes sensor information, it's safe, it has the ability to be rather resilient, it has confidential computing, it is designed to be secure, you know, designed to be all those things, because these data centers are going to be everywhere. And so Oren is really a technological marvels in production.

We experience very likely the lowest auto quarter for some time. And the reason for that is because over the next six years or so, we have $11 billion in counting of business that we've secured, estimated. And so I think it's a fairly safe thing to say now that Oren and our autonomous vehicle and robotics business is going to be our next multibillion-dollar business. It's on its way surely there. The robotics and autonomous systems and autonomous machines, whether they move or not move, but AI systems that are at the physical edge, is surely going to be the next major computing segment. It is surely going to be the next major data center segment.

We've been working in this area, as you know, for a decade. We have a fair amount of expertise in this area, and Oren is just one example of our work here. We have four pillars to our strategy for autonomous systems, starting from the data processing and the AI training part of it to train robotics AIs. second to simulate robotics AIs which is omniverse third to the memory of the robotics AI otherwise known as mapping and and then finally the actual robotics application and the robotics processor in the system and that's where Oren goes but Oren is just one of our four pillars of our robotics strategy and the next wave of AI. And so I am really optimistic and really enthusiastic about the next phase of the computer industry's growth.

And I think a lot of it's going to be at the edge. A lot of it's going to be about robotics. Thank you.

Thanks, everyone. The full impact and duration of the war in Ukraine and COVID lockdowns in China is difficult to predict. However, the impact of our technology and our market opportunities remain unchanged. The effectiveness of deep learning AI continues to astound. The transformer model, which led to the natural language understanding breakthroughs, is being advanced to learn patterns with great spatial, sequential, and temporal complexity.

Researchers are creating transformer models that are revolutionizing applications from robotics to drug discovery. The effectiveness of deep learning AI is driving companies across industries to adopt NVIDIA for AI computing. We're focused on four major initiatives.

First, ramping our next generation of AI infrastructure chips and platforms, Hopper GPU, Bluefield DPU, NVLink, Quantum InfiniBand Spectrum Ethernet networking. And all this to help customers build their AI factories and take advantage of new AI breakthroughs like transformers.

Second, ramping our system and software industry partners to launch Grace, our first CPU. Third, ramping Orin, our new robotics processor, and nearly 40 customers building cars, robo-taxis, trucks, delivery robots, logistics robots, farming robots, to medical instruments. And fourth, with our software platforms, adding new value to our ecosystem with NVIDIA AI and NVIDIA Omniverse, and expanding into new markets with new CUDA acceleration libraries. These initiatives will greatly advance AI, and while continuing to extend this most impactful technology of our time to scientists in every field and companies in every industry.

We look forward to updating you on our progress next quarter. Thank you.