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5,700 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | large as well, and we land that in DGX Cloud. And so DGX Cloud has multiple use cases, multiple drivers, and it's been off to just an enormous success. And our CSPs love it, the developers love it and our own internal engineers are clamoring to have more of it. And it's a great way for us to engage and work closely with all of the AI ecosystem around the world. |
5,701 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: And let's see if I can answer your question regarding our software revenue. In part of our opening remarks that we made as well, remember, software is a part of almost all of our products, whether they're our Data Center products, GPU systems or any of our products within gaming and our future automotive products. You're correct, we're also selling it in a standalone business. And that stand-alone software continues to grow where we are providing both the software services, upgrades across there as well. Now we're seeing, at this point, probably hundreds of millions of dollars annually for our software business, and we are looking at NVIDIA AI enterprise to be included with many of the products that we're selling, such as our DGX, such as our PCIe versions of our H100. And I think we're going to see more availability even with our CSP marketplaces. So we're off to a great start, and I do believe we'll see this continue to grow going forward.
Operator: And that does conclude today's question-and-answer session. I'll turn the call back over to Jensen Huang for any additional or closing remarks. |
5,702 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: A new computing era has begun. The industry is simultaneously going through 2 platform transitions, accelerated computing and generative AI. Data centers are making a platform shift from general purpose to accelerated computing. The $1 trillion of global data centers will transition to accelerated computing to achieve an order of magnitude better performance, energy efficiency and cost. Accelerated computing enabled generative AI, which is now driving a platform shift in software and enabling new, never-before possible applications. Together, accelerated computing and generative AI are driving a broad-based computer industry platform shift. Our demand is tremendous. We are significantly expanding our production capacity. Supply will substantially increase for the rest of this year and next year. NVIDIA has been preparing for this for over two decades and has created a new computing platform that the world’s industry -- world’s industries can build upon. What makes NVIDIA special are: one, architecture. NVIDIA accelerates everything from data processing, training, inference, every AI model, real-time speech to computer vision, and giant recommenders to vector databases. The performance and versatility of our architecture translates to the lowest data center TCO and best energy efficiency. Two, installed base. NVIDIA has hundreds of millions of CUDA-compatible GPUs worldwide. Developers need a large installed base to reach end users and grow their business. NVIDIA is the developer’s preferred platform. More developers create more applications that make NVIDIA more valuable for customers. Three, reach. NVIDIA is in clouds, enterprise data centers, industrial edge, PCs, workstations, instruments and robotics. Each has fundamentally unique computing models and ecosystems. System suppliers like OEMs, computer OEMs can confidently invest in NVIDIA because we offer significant market demand and reach. Scale and velocity. NVIDIA has achieved significant scale and is 100% invested in accelerated computing |
5,703 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | demand and reach. Scale and velocity. NVIDIA has achieved significant scale and is 100% invested in accelerated computing and generative AI. Our ecosystem partners can trust that we have the expertise, focus and scale to deliver a strong road map and reach to help them grow. We are accelerating because of the additive results of these capabilities. We’re upgrading and adding new products about every six months versus every two years to address the expanding universe of generative AI. While we increased the output of H100 for training and inference of large language models, we’re ramping up our new L40S universal GPU for scale, for cloud scale-out and enterprise servers. Spectrum-X, which consists of our Ethernet switch, BlueField-3 Super NIC and software helps customers who want the best possible AI performance on Ethernet infrastructures. Customers are already working on next-generation accelerated computing and generative AI with our Grace Hopper. We’re extending NVIDIA AI to the world’s enterprises that demand generative AI but with the model privacy, security and sovereignty. Together with the world’s leading enterprise IT companies, Accenture, Adobe, Getty, Hugging Face, Snowflake, ServiceNow, VMware and WPP and our enterprise system partners, Dell, HPE, and Lenovo, we are bringing generative AI to the world’s enterprise. We’re building NVIDIA Omniverse to digitalize and enable the world’s multi-trillion dollar heavy industries to use generative AI to automate how they build and operate physical assets and achieve greater productivity. Generative AI starts in the cloud, but the most significant opportunities are in the world’s largest industries, where companies can realize trillions of dollars of productivity gains. It is an exciting time for NVIDIA, our customers, partners and the entire ecosystem to drive this generational shift in computing. We look forward to updating you on our progress next quarter. |
5,704 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: This concludes today's conference call. You may now disconnect. |
5,705 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Good afternoon. My name is David, and I'll be your conference operator today. At this time, I'd like to welcome everyone to NVIDIA's First Quarter Earnings Call. Today's conference is being recorded. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there'll be a question-and-answer session. [Operator Instructions] Thank you. Simona Jankowski, you may begin your conference.
Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2024. 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 2024. The content of today's call is NVIDIA's property. It can't 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 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, May 24, 2023, 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 a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. And with that, let me turn the call over to Colette. |
5,706 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Thanks, Simona. Q1 revenue was $7.19 billion, up 19% sequentially and down 13% year-on-year. Strong sequential growth was driven by record data center revenue, with our gaming and professional visualization platforms emerging from channel inventory corrections. Starting with data center, record revenue of $4.28 billion was up 18% sequentially and up 14% year-on-year, on strong growth by accelerated computing platform worldwide. Generative AI is driving exponential growth in compute requirements and a fast transition to NVIDIA accelerated computing, which is the most versatile, most energy-efficient, and the lowest TCO approach to train and deploy AI. Generative AI drove significant upside in demand for our products, creating opportunities and broad-based global growth across our markets. Let me give you some color across our three major customer categories, cloud service providers or CSPs, consumer Internet companies, and enterprises. First, CSPs around the world are racing to deploy our flagship Hopper and Ampere architecture GPUs to meet the surge in interest from both enterprise and consumer AI applications for training and inference. Multiple CSPs announced the availability of H100 on their platforms, including private previews at Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, upcoming offerings at AWS, and general availability at emerging GPU specialized cloud providers like CoreWeave and Lambda. In addition to enterprise AI adoption, these CSPs are serving strong demand for H100 from Generative AI pioneers. Second, consumer Internet companies are also at the forefront of adopting Generative AI and deep learning-based recommendation systems, driving strong growth. For example, Meta has now deployed it's H100 powered Grand Teton AI supercomputer for its AI production and research teams. Third, enterprise demand for AI and accelerated computing is strong. We are seeing momentum in verticals such as automotive, financial services, healthcare, and telecom, where AI and |
5,707 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | is strong. We are seeing momentum in verticals such as automotive, financial services, healthcare, and telecom, where AI and accelerated computing are quickly becoming integral to customers' innovation roadmaps and competitive positioning. For example, Bloomberg announced it has a 50 billion parameter model, BloombergGPT, to help with financial natural language processing tasks such as sentiment analysis, named entity recognition, news classification, and question-answering. Auto Insurance company, CCC Intelligent Solutions is using AI for estimating repairs. And AT&T is working with us on AI to improve fleet dispatches so their field technicians can better serve customers. Among other enterprise customers using NVIDIA AI are Deloitte for logistics and customer service and Amgen for drug discovery and protein engineering. This quarter, we started shipping DGX H100, our Hopper generation AI system, which customers can deploy on-prem. And with the launch of DGX Cloud through our partnership with Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, we deliver the promise of NVIDIA DGX to customers from the cloud. Whether the customers deploy DGX on-prem or via DGX Cloud, they get access to NVIDIA AI software, including NVIDIA Base Command, and AI frameworks, and pre-trained models. We provide them with the blueprint for building and operating AI, spanning our expertise across systems, algorithms, data processing, and training methods. We also announced NVIDIA AI Foundations, which are model foundry services available on DGX Cloud, that enable businesses to build, refine, and operate custom large language models and generative AI models, trained with our own proprietary data, created for unique domain-specific tasks. They include NVIDIA NeMo for large language models, NVIDIA Picasso for images, video, and 3D, and NVIDIA BioNeMo for life sciences. Each service has six elements, pre-trained models, frameworks for data processing and curation, proprietary knowledge-based sector databases, systems for |
5,708 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | pre-trained models, frameworks for data processing and curation, proprietary knowledge-based sector databases, systems for fine-tuning, aligning, and guardrailing, optimized inference engines, and support from NVIDIA experts to help enterprises fine-tune models for their custom use cases. ServiceNow, a leading enterprise services platform is an early adopter of DGX Cloud and NeMo. They are developing custom large language models trained on data specifically for the ServiceNow platform. Our collaboration will let ServiceNow create new enterprise-grade generative AI offerings, with the 1,000s of enterprises worldwide running on the ServiceNow platform, including for IT departments, customer service teams, employees, and developers. Generative AI is also driving a step-function increase in inference workloads. Because of their size and complexities, these workloads require acceleration. The latest MLPerf industry benchmark released in April showed NVIDIA's inference platform deliver performance that is orders of magnitude ahead of the industry, with unmatched versatility across diverse workloads. To help customers deploy generative AI applications at scale, at GTC, we announced four major new inference platforms that leverage the NVIDIA AI software stack. These include L4 Tensor Core GPU for AI video, L40 for Omniverse, and graphics rendering, H100 NVL for large language models, and the Grace Hopper Superchip for LLMs and also, recommendation systems and vector databases. Google Cloud is the first CSP to adopt our L4 inference platform with the launch of its G2 virtual machines for generative AI inference and other workloads such as Google Cloud Dataproc, Google AlphaFold, and Google Cloud's Immersive Stream, which render 3D and AR experiences. In addition, Google is integrating our Triton inference server with Google Kubernetes engine and its cloud-based Vertex AI platform. In networking, we saw strong demand at both CSPs and enterprise customers for generative AI and accelerated computing, which require |
5,709 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | we saw strong demand at both CSPs and enterprise customers for generative AI and accelerated computing, which require high-performance networking like NVIDIA's Mellanox networking platforms. Demand relating to general purpose CPU infrastructure remain soft. As generative AI applications grow in size and complexity, high performance networks become essential for delivering accelerated computing at data center scale to meet the enormous demand of all training and inferencing. Our 400 gig Quantum-2 InfiniBand platform is the gold standard for AI dedicated infrastructure, with broad adoption across major cloud and consumer Internet platforms such as Microsoft Azure. With the combination of in-network computing technology and the industry's only end-to-end data center scale, optimized software stack, customers routinely enjoy a 20% increase in throughput for their sizable infrastructure investment. For multi-tenant cloud transitioning to support generative AI our high-speed Ethernet platform with BlueField-3 DPUs and Spectrum-4 Ethernet switching, offers the highest available Ethernet network performance. BlueField-3 is in production and has been adopted by multiple hyperscale and CSP customers, including Microsoft Azure, Oracle Cloud, CoreWeave, Baidu, and others. We look forward to sharing more about our 400 gig Spectrum-4 accelerated AI networking platform next week at the COMPUTEX Conference in Taiwan. Lastly, our Grace data center CPU is sampling with customers. At this week's International Supercomputing Conference in Germany, the University of Bristol announced a new supercomputer based on the NVIDIA Grace CPU Superchip, which is 6x more energy-efficient than the previous supercomputer. This adds to the growing momentum for Grace with both CPU only and CPU/GPU opportunities across AI and cloud and supercomputing applications. The coming wave of BlueField-3, Grace and Grace Hopper Superchips will enable a new generation of super energy efficient accelerated data centers. Now, let's move to gaming. Gaming |
5,710 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Superchips will enable a new generation of super energy efficient accelerated data centers. Now, let's move to gaming. Gaming revenue of $2.24 billion was up 22% sequentially, and down 38% year-on-year. Strong sequential growth was driven by sales of the 40 Series GeForce RTX GPUs for both notebooks and desktops. Overall, end demand was solid, and consistent with seasonality, demonstrating resilience against a challenging consumer spending backdrop. The GeForce RTX 40 Series GPU laptops are off to a great start, featuring four NVIDIA inventions, RTX Path Tracing, DLSS 3 AI rendering, Reflex Ultra-Low Latency rendering, and Max-Q, energy efficient technologies. They deliver tremendous gains in industrial design, performance and battery life for gamers and creators. And like our desktop offerings, 40 Series laptops support the NVIDIA Studio platform or software technologies, including acceleration for creative data science and AI workflows, and Omniverse, giving content creators unmatched tools and capabilities. In desktop, we ramped the RTX 4070, which joined the previously launched RTX 4090, 4080, and 4070 Ti GPUs. The RTX 4070 is nearly 3x faster than the RTX 2070 and offers our large installed-base a spectacular upgrade. Last week, we launched the 60 family, RTX 4060, and 4060 Ti, bringing our newest architecture to the world's core gamers starting at just $299. These GPUs for the first time provide 2x the performance of the latest gaming console at mainstream price points. The 4060 Ti is available starting today, while the 4060 will be available in July. Generative AI will be transformative to gaming and content creation from development to run time. At the Microsoft Build Developer Conference earlier this week, we showcased how Windows PCs and workstations with NVIDIA RTX GPUs will be AI-powered at their core. NVIDIA and Microsoft have collaborated on end-to-end software engineering, spanning from the Windows operating system to the NVIDIA graphics drivers, and NeMo's LLM framework to help make Windows on |
5,711 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | spanning from the Windows operating system to the NVIDIA graphics drivers, and NeMo's LLM framework to help make Windows on NVIDIA RTX Tensor Core GPUs, a supercharged platform for generative AI. Last quarter, we announced a partnership with Microsoft to bring Xbox PC games to GeForce NOW. The first game from this partnership, Gears 5 is now available with more set to be released in the coming months. There are now over 1,600 games on GeForce NOW, the richest content available on any cloud gaming service. Moving to Pro Visualization. Revenue of $295 million was up 31% sequentially, and down 53% year-on-year. Sequential growth was driven by stronger workstation demand across both mobile and desktop form factors, with strength in key verticals such as Public Sector, Healthcare, and Automotive. We believe the channel inventory correction is behind us. The ramp of our Ada Lovelace GPU architecture in workstations kicked-off a major product cycle. At GTC, we announced six new RTX GPUs for laptops and desktop workstations, with further rollout planned in the coming quarters. Generative AI is a major new workload for NVIDIA-powered workstation. Our collaboration with Microsoft transformed windows into the ideal platform for creators and designers, harnessing generative AI to elevate their creativity and productivity. At GTC, we announced NVIDIA Omniverse Cloud, an NVIDIA fully managed service running in Microsoft Azure that includes the full suite of Omniverse applications and NVIDIA OVX infrastructure. Using this full stack cloud environment, customers can design, develop, deploy, and manage industrial metaverse applications. NVIDIA Omniverse Cloud will be available starting in the second half of this year. Microsoft NVIDIA will also connect Office 365 applications with Omniverse. Omniverse Cloud is being used by companies to digitalize their workflows from design and engineering to smart factories and 3D content generation for marketing. The automotive industry has been a leading early adopter of Omniverse, including |
5,712 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | and 3D content generation for marketing. The automotive industry has been a leading early adopter of Omniverse, including companies such as BMW Group, Geely Lotus, General Motors, and Jaguar Land Rover. Moving to Automotive. Revenue was $296 million, up 1% sequentially, and up 114% from a year ago. Our strong year-on-year growth was driven by the ramp of the NVIDIA DRIVE Orin across a number of new energy vehicles. As we announced in March, our automotive design win pipeline over the next six years now stands at $14 billion, up from $11 billion a year ago, giving us visibility into continued growth over the coming years. Sequentially, growth moderated as some NEV customers in China are adjusting their production schedules to reflect slower than expected demand growth. We expect this dynamic to linger for the rest of the calendar year. During the quarter, we expanded our partnership with BYD, the world's leading manufacturer of NEVs. Our new design win will extend BYD's use of the DRIVE Orin to its next-generation high-volume Dynasty, and Ocean series of vehicles, set to start production in calendar 2024. Moving to the rest of the P&L. GAAP gross margins was 64.6%, and non-GAAP gross margins were 66.8%. Gross margins have now largely recovered to prior peak level, and we have absorbed higher costs, and offset them by innovating and delivering higher valued products as well as products incorporating more and more software. Sequentially, GAAP operating expenses were down 3%, and non-GAAP operating expenses were down 1%, We have held OpEx at roughly the same level over the last past four quarters. We're working through the inventory corrections in gaming and professional visualization. We now expect to increase investments in the business while also delivering operating leverage. We returned $99 million to shareholders in the form of cash dividends. At the end of the Q1, we have approximately $7 billion remaining under our share repurchase authorization through December 2023. Let me turn to the outlook for the |
5,713 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | $7 billion remaining under our share repurchase authorization through December 2023. Let me turn to the outlook for the second quarter fiscal '24. Total revenue is expected to be $11 billion, plus or minus 2%. We expect this sequential growth to largely be driven by data center, reflecting a steep increase in demand related to generative AI and large language models. This demand has extended our data center visibility out a few quarters and we have procured substantially higher supply for the second half of the year. GAAP and non-GAAP gross margins are expected to be 68.6% and 70% respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $2.71 billion and $1.9 billion, respectively. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $90 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 14%, plus or minus 1%, excluding any discrete items. Capital expenditures are expected to be approximately $300 million to $350 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight some of the upcoming events, Jensen will give the COMPUTEX keynote address in person in Taipei this coming Monday, May 29 local time, which will be Sunday evening in the U.S. In addition, we will be attending the BofA Global Technology Conference in San Francisco on June 6. And Rosenblatt Virtual Technology Summit on The Age of AI on June 7, and the New Street Future of Transportation Virtual Conference on June 12. Our earnings call to discuss the results of our second quarter fiscal '24 is scheduled for Wednesday, August 23. Well, that covers our opening remarks. We're now going to open the call for questions. Operator, would you please poll for questions? |
5,714 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Thank you. [Operator Instructions] We'll take our first question from Toshiya Hari with Goldman Sachs. Your line is open.
Toshiya Hari: Hi. Good afternoon. Thank you so much for taking the question and congrats on the strong results, and incredible outlook. Just one question on data center. Colette, you mentioned the vast majority of the sequential increase in revenue this quarter will come from data center. I was curious what the construct is there, if you can speak to, what the key drivers are from April to July and perhaps more importantly, you talked about visibility into the second half of the year. I'm guessing it's more of a supply problem at this point, what kind of sequential growth beyond the July quarter can your supply chain support at this point? Thank you. |
5,715 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Okay. So, a lot of different questions there. So, let me see if I can start and I am sure Jensen will have some following up comments. So when we talk about our sequential growth that we're expecting between Q1 and Q2, our generative AI large language models are driving the surge in demand, and it's broad-based across both our consumer Internet companies, our CSPs, our enterprises, and our AI start-ups. It is also interest in both of our architectures, both of our Hopper latest architecture as well as our Ampere architecture. This is not surprising as we generally often sell both of our architectures at the same time. This is also a key area where deep recommendators are driving growth. And we also expect to see growth both in our computing as well as in our networking business. So, those are some of the key things that we have baked in when we think about the guidance we provided to Q2. We also surfaced in our opening remarks that we are working on both supply today for this quarter, but we have also procured a substantial amount of supply for the second half. We have significant supply chain flow to serve our significant customer demand that we see, and this is demand that we see across a wide range of different customers. They are building platforms for some of the largest enterprises, but also setting things up at the CSPs and the large consumer Internet companies. So, we have visibility right now for our data center demand that has probably extended out a few quarters and that's led us to working on quickly procuring that substantial supply for the second half. I'm going to pause there and see if Jensen wants to add a little bit more.
Jensen Huang: I thought that was great color. Thank you.
Operator: Next we'll go to C.J. Muse with Evercore ISI. Your line is open. |
5,716 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next we'll go to C.J. Muse with Evercore ISI. Your line is open.
C.J. Muse: Yeah. Good afternoon. Thank you for taking the question. I guess with data center, you are essentially doubling quarter-on-quarter, two natural kind of questions that relate to one another come to mind. Number one, where are we in terms of driving acceleration into servers to support AI? And as part of that, as you deal with longer cycle times with TSMC and your other partners, how are you thinking about managing their commitments there with where you want to manage your lead times in the coming years to best match that supply and demand? Thanks so much. |
5,717 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah, C.J. Thanks for the question. I'll start backwards. The -- remember, we were in full production of both Ampere and Hopper when I -- when the ChatGPT moment came. And it helped everybody crystallize how to transition from the technology of large language models to a product and service based on a chatbot. The integration of guardrails and alignment systems were through reinforcement learning human feedback, knowledge vector data bases for proprietary knowledge, connection to search, all of that came together in a really wonderful way and it's the reason why I call it the iPhone moment, all the technology came together and helped everybody realize what an amazing product that can be and what capabilities it can have. And so we were already in full production. NVIDIA's supply chain flow and our supply chain is very significant as you know. And we build supercomputers in volume, and these are giant systems and we build them in volume. It includes, of course, the GPUs, but on our GPUs, the system boards have 35,000 other components. And the networking, and fiberoptics, and the incredible transceivers and the NICs, the Smart NICs, the switches, all of that has to come together in order for us to stand-up a data center. And so we were already in full production when the moment came. We had to really significantly increase our procurement substantially for the second half as Colette said. Now, let me talk about the bigger picture and why the entire world's data centers are moving towards accelerated computing. It's been known for some time and you've heard me talk about it, that accelerated computing is a full stack problem, but it is full stack challenge, but if we could successfully do it in a large number of application domain has taken us 15 years. If - sufficiently that almost the entire data centers' major applications could be accelerated you could reduce the amount of energy consumed and the amount of cost for our data center substantially by an order of magnitude. It takes -- it costs a lot |
5,718 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | energy consumed and the amount of cost for our data center substantially by an order of magnitude. It takes -- it costs a lot of money to do it because you have to do all the software and everything and you have to build all the systems and so on and so forth, but we’ve been at it for 15 years. And what happened is, when generative AI came along, it triggered a killer app for this computing platform that's been in preparation for some time. And so, now we see ourselves in two simultaneous transitions. The world's $1 trillion data center is nearly populated entirely by CPUs today, and $1 trillion, $250 billion a year, it's growing of course. But over the last four years, call it a $1 trillion worth of infrastructure installed. And it's all completely based on CPUs and dumb NICs. It's basically unaccelerated. In the future, it's fairly clear now with this -- with generative AI becoming the primary workload of most of the world's data centers generating information, it is very clear now that -- and the fact that accelerated computing is so energy efficient, that the budget of the data center will shift very dramatically towards accelerated computing and you're seeing that now. We're going through that moment right now as we speak. While the world's data center CapEx budget is limited but at the same time we're seeing incredible orders to retool the world's data centers. And so I think you're starting -- you're seeing the beginning of call it a 10-year transition to basically recycle or reclaim the world's data centers and build it out as accelerated computing. You'll have a pretty dramatic shift in the spend of the data center from traditional computing, and to accelerated computing with smart NICs, smart switches, of course, GPUs, and the workload is going to be predominantly generative AI. |
5,719 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: And we'll move to our next question, Vivek Arya with BofA Securities. Your line is open.
Vivek Arya: Thanks for the question. Could I just wanted to clarify does visibility mean data center sales can continue to grow sequentially in Q3 and Q4 or do they sustain at Q2 levels? I just wanted to clarify that. And then Jensen, my question is that, given this very strong demand environment, what does that do to the competitive landscape? Does it invite more competition in terms of custom ASICs? Does it invite more competition in terms of other GPU solutions or other kinds of solutions? How do you see the competitive landscape change over the next two to three years?
Colette Kress: Yeah, Vivek. Thanks for the question. Let me see if I can add a little bit more color. We believe that the supply that we will have for the second half of the year will be substantially larger than H1. So, we are expecting not only the demand that we just saw in this last quarter, the demand that we have in Q2 for our forecast, but also planning on seeing something in the second half of the year. We just have to be careful here. But we are not here to guide on the second half of that. Yes, we do plan a substantial increase in the second half compared to the first half. |
5,720 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Regarding competition, we have competition from every direction. Start-ups really-really well-funded and innovative startups, countless of them all over the world. We have competitions from existing semiconductor companies. We have competition from CSPs with internal projects. And many of you know about most of these. And so, we're mindful of competition all the time, and we get competition all the time. But NVIDIA's value proposition at the core is, we are the lowest cost solution. We're the lowest TCO solution. And the reason for that is, because accelerated computing is two things that I talk about often, which is it's a full stack problem, it's a full stack challenge, you have to engineer all of the software and all the libraries and all the algorithms, integrated them into and optimize the frameworks and optimize it for the architecture of not just one ship but the architecture of an entire data center, all the way into the frameworks, all the way into the models. And the amount of engineering and distributed computing, fundamental computer science work is really quite extraordinary. It is the hardest computing as we know. And so, number one, it's a full stack challenge and you have to optimize it across the whole thing and across just the mind blowing number of stacks. We have 400 acceleration libraries. As you know, the amount of libraries and frameworks that we accelerate is pretty mind blowing. The second part is that generative AI is a large scale problem, and it's a data center scale problem, it's another way of thinking that the computer is the data center or to data center is the computer, it's not the chip, it's the data center and it's never happened like this before. And in this particular environment, your networking operating system, your distributed computing engines, your understanding of the architecture of the networking gear, the switches and the computing systems, the computing fabric, that entire system is your computer and that's what you're trying to operate. And so in |
5,721 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | systems, the computing fabric, that entire system is your computer and that's what you're trying to operate. And so in order to get the best performance, you have to understand full stack and you have to understand data center scale, and that's what accelerated computing is. The second thing is that -- utilization, which talks about the amount of the types of applications that you can accelerate and diversity of our architecture keeps that utilization high. If you can do one thing and do one thing only and incredibly fast, then your data center is largely underutilized and it's hard to scale that up. And the thing is, universal GPU in fact that we accelerate so many stacks, makes our utilization incredibly high, and so number one is throughput, and that's software -- that's a software-intensive problems and data center architecture problems. The second is utilization versatility problem and the third is just data center expertise. We've built five data centers of our own and we've helped companies all over the world build data centers and we integrate our architecture into all the world's clouds. From the moment of delivery of the product to do standing up in the deployment, the time to operations of the data center is measured not -- if you're not good at it and not – not proficient at it, it could take months. Standing up a supercomputer, let's see, some of the largest supercomputers in the world were installed about a year and a half ago and now they're coming online, and so it's not – it unheard of to see a delivery to operations of about a year. Our delivery to operation is measured in weeks. And we've taken data centers and supercomputers and we've turned it into products, and the expertise of the team in doing that is incredible, and so. So, our value proposition is in the final analysis, all of this technology translates in the infrastructure, the highest throughput in the lowest possible cost. And so I think -- our market is of course very, very competitive, very large. But the challenge is |
5,722 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | in the lowest possible cost. And so I think -- our market is of course very, very competitive, very large. But the challenge is really-really great. |
5,723 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next we go to Aaron Rakers with Wells Fargo. Your line is open.
Aaron Rakers: Yeah. Thank you for taking the question and congrats on the quarter. As we kind of think about unpacking the various different growth drivers of the data center business going forward, I'm curious, Colette, of just how we should think about the monetization effect of software, considering that the expansion of your cloud service agreements continues to grow? I'm curious of what -- where do you think we're at in terms of that approach in terms of the AI enterprise software suite and other drivers of software only revenue going forward?
Colette Kress: Thanks for the question. Software is really important to our accelerated platforms. Not only do we have a substantial amount of software that we are including in our nearest architecture and essentially, all products that we have. We are now with many different models to help customers start their work in generative AI and accelerated computing. So, anything that we have here from a DGX Cloud and providing those services, helping them build models or as we've discussed the importance of NVIDIA AI enterprise, essentially that operating system for AI. So, all things should continue to grow as we go forward, both the architecture and the infrastructure, as well as both availability of this offering, our ability to monetize [indiscernible] as well. I'll turn it over to Jensen, if he needs to add. |
5,724 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. We can see in real-time the growth of generative AI and CSPs, both for training the models, refining the models, as well as deploying the models. As Colette said earlier, inference is now a major driver of accelerated computing because generative AI is used so capably in so many applications already. There are two segments that requires a new stack of software and the two segments are enterprise and industrials. Enterprise requires a new stack of software, because many enterprises need to have all the capabilities that we've talked about, whether it's large language models, the ability to adapt, and for your proprietary use-case and your proprietary data, align it to your own principles, and your own operating domains. You want to have the ability to be able to do that in a high performance computing sandbox, and we that DGX Cloud, and create a model. Then you want to deploy your chatbot or your AI in any Cloud, because you have services and you have agreements with multiple Cloud vendors and depending on the applications, you might deploy it on various clouds. And for the enterprise, we have NVIDIA AI Foundation for helping you create custom models and we have NVIDIA AI Enterprise. NVIDIA AI Enterprise is the only accelerated stack, GPU accelerated stack in the world that is Enterprise safe, and Enterprise supported. There are a constant patching that you have to do, there are 4,000 different packages that buildup NVIDIA AI Enterprise and represents the operating engine, end-to-end operating engine of the entire AI workflow. It's the only one of its kind from data ingestion, data processing, obviously, in order to train an AI model, you have a lot of data, you have to process and package up and curate, and align and there's just a whole bunch of stuff that you have to do to the data to prepare it for training. That amount of data, that could consume some 40%, 50%, 60% of your computing time and so, data processing is very big deal. And then the second aspect of it is training the model, |
5,725 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | 60% of your computing time and so, data processing is very big deal. And then the second aspect of it is training the model, refining the model and the third is deploying model for inferencing. NVIDIA AI Enterprise supports and patches and security patches continuously all of those 4,000 packages of software. And for an Enterprise that wants to deploy their engines, just like they want to deploy Red Hat Linux, this is incredibly complicated software in order to deploy that in every cloud and as well as on-prem, it has to be secure, it has to be supported. And so, NVIDIA AI Enterprise is the second point. The third is Omniverse. Just as people are starting to realize that you need to align an AI to ethics, the same for robotics, you need to align the AI for physics. And aligning an AI for ethics includes a technology called reinforcement learning human feedback. In the case of industrial applications and robotics, it's reinforcement learning Omniverse feedback. And Omniverse is a vital engine for software defined in robotic applications and industries. And so, Omniverse also needs to be a cloud service platform. And so our software stack, the three software stacks, AI Foundation, AI Enterprise and Omniverse runs in all of the world's clouds that we have partnerships, DGX Cloud partnerships with. Azure, we have partnerships on both AI as well as Omniverse. With GTP and Oracle, we have great partnerships in DGX Cloud for AI and AI Enterprise is integrated into all three of them and so I think the -- in order to for us to extend the reach of AI beyond the cloud, and into the world's Enterprise and into the world's industries, you need two new types of -- you need new software stacks in order to make that happen and by putting it in the cloud, integrate it into the world's CSP clouds, it's a great way for us to partner with the sales and the marketing team and the leadership team of all the cloud vendors. |
5,726 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next we'll go to Timothy Arcuri with UBS. Your line is Open.
Tim Arcuri: Thanks a lot. I had a question and then I had a clarification as well. So, the question first is, Jensen, on the InfiniBand versus Ethernet argument, can you sort of speak to that debate and maybe how you see it playing out? I know you need the low late -- the low latency of InfiniBand for AI, but can you sort of talk about the attach rate of your InfiniBand solutions to what you're shipping on the core compute side and maybe whether that's similarly crowding out Ethernet like you are with on the compute side? And then the clarification, Colette, is that there wasn't a share buyback despite you still having about $7 billion on the share repo authorization. Was that just timing? Thanks.
Jensen Huang: Colette, how about you go first? You should take the question.
Colette Kress: That is correct. We have $7 billion available in recurrent authorization for repurchases. We did not repurchase anything in this last quarter, but we do repurchase opportunistically and we'll consider that as we go forward as well. Thankyou |
5,727 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: InfiniBand and Ethernet are Target different applications in a data center. All right. They both have their place. InfiniBand had a record quarter. We're going to have a giant record year. And InfiniBand has a really -- NVIDIA's Quantum InfiniBand has an exceptional roadmap. It's going to be really incredible. But the two networks are very different. InfiniBand is designed for an AI factory, if you will. If that data center is running a few applications for a few people for a specific use case and it's doing it continuously and that infrastructure costs you, pick a number, $500 million. The difference between InfiniBand and Ethernet could be 15%, 20% in overall throughput. And if you spent $500 million in an infrastructure and the difference is 10% to 20% and it's a $100 million, InfiniBand is basically free. That's the reason why people use it. InfiniBand is effectively free. The difference in data center throughput is just -- it's too great to ignore, and you're using it for that one application and so, however, if your data center is a cloud datacenter and its multi-tenant. It's a bunch of little jobs, a bunch of little jobs and is shared by millions of people. Then Ethernet is really do I answer? There's a new segment in the middle where the Cloud is becoming a generative AI cloud. It's not only AI factory per se. But it's still a multi-tenant Cloud but it wants to run generative AI workloads. This new segment is a wonderful opportunity and at COMPUTEX, I referred to it at the last GTC. At COMPUTEX, we're going to announce a major product line for this segment, which is Ethernet focused generative AI application type of clouds. But InfiniBand is doing fantastically and we're doing record numbers quarter-on-quarter year-on-year.
Operator: Next we'll go to Stacy Rasgon with Bernstein Research. Your line is open. |
5,728 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next we'll go to Stacy Rasgon with Bernstein Research. Your line is open.
Stacy Rasgon: Hi, guys. Thanks for taking my question. I had a question on inference versus training for generative AI. So, you're talking about inference as being a very large opportunity. I guess, two sub parts to that. Is that, besides inference basically scales with like the usage versus like training is more of a one-and-done. And can you give us some sort of even if it's just like qualitatively, like if do you think are influence is bigger than training or vice-versa, like if it's bigger, how much bigger? Is it like the opportunity, is it 5x, is it 10x, is there anything you can give us on those two workloads within generative AI, it would be helpful. |
5,729 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. I'll work backwards. You're never done with training. You're always -- every time you deploy, you're collecting new data. When you collect new data, you train with the new data. And so, you're never done training. You're never done producing and processing a Vector database that augments the large language model. You're never done with vectorizing all of the collected structured -- unstructured data that you have. And so, whether you're building a recommender system, a large language model, a vector database, these are probably the three major applications of the three core engines, if you will, of the future of computing. It's all a bunch of other stuff, but obviously these are very three very important ones. They are always running. You're going to see that more-and-more companies realize they have a factory for intelligence, an intelligence factory and in that particular case, it's largely dedicated to training and processing data and vectorizing data and learning representation of the data, so on and so forth. The inference part of it, are APIs that are either open APIs that can be connected to all kinds of applications, APIs that is integrated into workflows. But APIs of all kinds, there'll be 100s of APIs in the company, some of them they built themselves, some of them part that could -- many of them could come from companies like ServiceNow and Adobe that we're partnering with in AI Foundations. And they'll create a whole bunch of generative AI APIs that companies can then connect into their workflows or use as an application. And of course, there will be a whole bunch of Internet Service Companies. So, I think you're seeing for the very first time simultaneously a very significant growth in the segment of AI Factories, as well as a market that -- a segment that really didn't exist before, but now it's growing exponentially, practically by the weak for AI inference with APIs. The simple way to think about it in the end, is that, the world has a $1 trillion of data center installed and |
5,730 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | with APIs. The simple way to think about it in the end, is that, the world has a $1 trillion of data center installed and they used to be 100% CPUs. In the future, we know we've heard it in enough places and I think this year there is a ISC keynote was actually about the end of Moore's Law. We've seen it in a lot of places now that you can't reasonably scale-out data centers with general-purpose computing and that accelerated computing is the path forward and now it's got a killer app and it's got generative AI, and so the easiest way to think about that is your $1 trillion infrastructure. Every quarters capital CapEx budget would lean very heavily into generative AI into accelerated computing infrastructure everywhere from the number of GPUs that would be used in the CapEx budget to the accelerated switches and accelerated net -- networking chips that connect them all. That the easiest way to think about that is over the next four or five, 10 years, most of that $1 trillion and then compensating adjusting for all the growth in data center still, it will be largely generative AI and so that's probably the easiest way to think about that and that's training as well as inference. |
5,731 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next, we'll go to Joseph Moore with Morgan Stanley. Your line is open.
Joseph Moore: Great. Thank you. I want to follow-up on that, in terms of the focus on inference. It's pretty clear that this is a really big opportunity around large language models, but the cloud customers are also talking about trying to reduce cost per query by very significant amounts. You can talk about the ramifications of that for you guys, is that where some of the specialty insurance products that you launched at GTC come in and just how are you going to help your customers get the cost per query down? |
5,732 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. That's a great question. Whether your -- you start by building a large language model and you use that large language model very large version and you could distill them into medium, small and tiny size. And the tiny sized ones, you can put in your phone and your PC and so on and so forth and they all have good -- they all have -- it seems surprising, but they all can do the same thing. But obviously, the zero shot or the generalizeability of the large language model, the biggest one is much more versatile and it can do a lot more amazing things. And the large one would teach the smaller ones, how to be good AIs and so, you use the large one to generate prompts to align the smaller ones and so on and so forth. And so you start by building very large ones. And then you also have to train a whole bunch of smaller ones. Now, that's exactly the reason why we have so many different sizes of our inference. You saw that I announced L4, L40, H100 NBL -- which also have H100 HGX and then we have H100 multi-node with NVLink and so there is -- you could have model sizes of any kind that you like. The other thing that's important is, these are models, but they are connected ultimately to applications. And the applications could have image in, video out, video in, text out, image in, proteins out, text in, 3D out, video in, in the future, 3D graphics out. So, the input and the output requires a lot of pre and post-processing. The pre and post-processing can't be ignored. And this is one of the things that most of the specialized chip arguments fall apart and it's because the length -- the model itself is only call it 25% of the data -- of the overall processing of inference. The rest of it is about preprocessing and post-processing, security, decoding, all kinds of things like that. And so, I think the multimodality aspect of inference, the multi diversity of inference, that it's going to be done in the Cloud on-prem. It's going to be done in multi-cloud, that's the reason why we have the AI Enterprise in |
5,733 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | going to be done in the Cloud on-prem. It's going to be done in multi-cloud, that's the reason why we have the AI Enterprise in all the clouds. It's going to be done on-prem, it's the reason why we have a great partnership with Dell we just announced the other day, called project Helix, that's going to be integrated into third-party services. That's the reason why we have a great partnership with ServiceNow, and Adobe, because they're going to be creating a whole bunch of generative AI capabilities. And so, there's all the diversity, and the reach of generative AI is so broad, you need to have some very fundamental capabilities like what I just described, in order to really address the whole space of it. |
5,734 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next we'll go to Harlan Sur with JP Morgan. Your line is open.
Harlan Sur: Hi. Good afternoon, and congratulations on the strong results and execution. I really appreciate more of the focus or some of the focus today in your networking products. I mean, it's really an integral part to sort of maximize the full performance of your compute platforms. I think so data center networking business is driving a part of $1 billion of revenues per quarter plus or minus, that's 2.5x growth from three years ago, right, when you guys acquired Mellanox. So very strong growth, but given the very high attach of your InfiniBand, Ethernet solutions, your accelerated compute platforms, is the networking run-rate stepping up in line with your compute shipment? And then, what is the team doing to further unlock more networking bandwidth going forward just to keep pace with the significant increase in compute complexity, datasets, requirements for lower latency, better traffic predictability, and so on? |
5,735 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah, Harlan. I really appreciate that. So, nearly everybody who thinks about AI, they think about that chip, the accelerator chip and in fact, it misses the whole point nearly completely. And I've mentioned before that accelerated computing is about the stack, about the software and networking, remember, we announced a very early-on this networking stack called DOCA and we have the acceleration library call Magnum IO. These two pieces of software are some of the crown jewels of our company. Nobody ever talks about it, because it's hard to understand, but it makes it possible for us to connect 10s of 1,000s of GPUs. How do you connect 10s of 1000s of GPUs, if the operating system of the data center, which is the infrastructure, is not insanely great, and so that's the reason why we're so obsessed about networking in the company. And one of the great things that we have -- we have Mellanox as you know quite well, was the world's highest performance and the unambiguous leader in high performance networking, that's the reason why our two companies are together. You also see that our network expands starting from NVLink, which is a computing fabric with a really super low latency and it communicates using memory references, not network package. And then we take NVLink, we connect it inside multiple GPUs, and I described, going beyond the GPU. And I'll talk a lot more about that at COMPUTEX in a few days. And then, that gets connected to InfiniBand, which includes the NIC, and the SmartNIC BlueField-3 that we're in full production with and the switches, all of the fiber optics that are optimized end-to-end. These things are running at an incredible line rates. And then beyond that, if you want to connect the smart AI factory -- the smart fact -- this AI factory into your computing fabric, we have a brand new type of Ethernet that we'll be announcing at COMPUTEX, and so -- this whole area of the computing fabric extending connecting all of these GPUs and computing units together, all the way through the |
5,736 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | whole area of the computing fabric extending connecting all of these GPUs and computing units together, all the way through the networking, through the switches, the software stack is insanely complicated. And so, we're -- I'm delighted you understand it, and but this -- we don't break it out, particularly, because we think of the whole thing as a computing platform as it should be. We sell it to all of the world's data centers as components, so that they can integrate it into whatever style or architecture that they would like and we can still run our software stack. That's the reason why we break it up, it's way more complicated the way that we do it, but it makes it possible for NVIDIA's computing architecture to be integrated into anybody's data center in the world from Cloud of all different kinds to on-prem of all different kinds, all the way out to the edge to 5G and so this way of doing it is really complicated, but it gives us incredible reach. |
5,737 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: And our last question will come from Matt Ramsay with TD Cowen. Your line is open.
Matt Ramsay: Thank you very much. Congratulations, Jensen, and to the whole team. One of the things I wanted to dig into a little bit is the DGX Cloud offering. You guys have been working on this for some time behind the scenes, where you sell in the hardware to your hyperscale partners and then lease it back for your own business, and the rest of us kind of found out about it publicly a few months ago. And as we look forward over the next number of quarters that Colette discussed to high visibility in the data center business. Maybe you could talk a little bit about the mix you're seeing of hyperscale customers buying for their own first-party internal workloads versus their own sort of third-party, their own customers versus what of that big upside in data center going forward is systems that you're selling in, with potential to support your DGX Cloud offerings and what you've learned since you've launched it about the potential of that business. Thanks. |
5,738 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. Thanks Matt. It's -- without being too specific about numbers, but the ideal scenario, the ideal mix is something like 10% NVIDIA DGX Cloud and 90% the CSPs clouds, and the reason -- and our DGX Cloud is -- the NVIDIA stack is the pure NVIDIA stack. It is architected the way we like and achieves the best possible performance. It gives us the ability to partner very deeply with the CSPs to create the highest-performing infrastructure, number one. Number two, it allows us to partner with the CSPs to create markets like, for example, we're partnering with Azure to bring Omniverse cloud to the world's industries. And the world's never had a system like that, the computing stack. Now with all the generative AI stuff and all the 3D stuff and the physics stuff, incredibly large database and really high-speed networks and low-latency networks, that kind of a virtual industrial virtual world has never existed before. And so, we partnered with Microsoft to create Omniverse cloud inside Azure cloud. So, it allows us number two, to create new applications together and develop new markets together. And we go-to-market as one team and we benefit by getting our customers on our computing platform and they benefit by having us in their cloud, number one; but number two, the amount of data and services and security services and all of the amazing things that Azure and GCP and OCI have, they can instantly have access to that through Omniverse cloud. And so it's a huge win-win. And for the customers, the way that NVIDIA's cloud works for these early applications, they can do it anywhere. So one standard stack runs in all the clouds and if they would like to take their software and run it on the CSPs cloud themselves and manage it themselves, we're delighted by that, because NVIDIA AI Enterprise, NVIDIA AI Foundations. And long-term, this is going to take a little longer, but NVIDIA Omniverse will run in the CSPs clouds. Okay. So, our goal really is to drive architecture to partner deeply in creating new markets |
5,739 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | will run in the CSPs clouds. Okay. So, our goal really is to drive architecture to partner deeply in creating new markets and the new applications that we're doing and provide our customers with the flexibilities to run in their -- in their everywhere, including on-prem and so, that -- those were the primary reasons for it and it's worked out incredibly. Our partnership with the three CSPs and that we currently have DGX Cloud in and their sales force and marketing teams, their leadership team is really quite spectacular. It works great. |
5,740 | NVDA | 1 | 2,024 | 2023-05-24 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Thank you. I'll now turn it back over to Jensen Huang for closing remarks.
Jensen Huang: The computer industry is going through two simultaneous transitions, accelerated computing and generative AI. CPU scaling has slowed, yet computing demand is strong and now with generative AI supercharged. Accelerated computing, a full stack and data center scale approach that NVIDIA pioneered is the best path forward. There is $1 trillion installed in the global data center infrastructure, based on the general-purpose computing method of the last era. Companies are now racing to deploy accelerated computing for the generative AI era. Over the next decade, most of the world's data centers will be accelerated. We are significantly increasing our supply to meet the surging demand. Large language models can learn information encoded in many forms. Guided by large language models, generative AI models can generate amazing content and with models to fine-tune, guardrail, align to guiding principles and ground the facts, generative AI is emerging from labs and is on its way to industrial applications. As we scale with cloud and Internet service providers, we are also building platforms for the world's largest enterprises. Whether within one of our CSP partners or on-prem with Dell Helix, whether on a leading enterprise platform like ServiceNow and Adobe or a bespoke with NVIDIA AI Foundations, we can help enterprises leverage their domain expertise and data to harness generative AI securely and safely. We are ramping a wave of products in the coming quarters, including H100, our Grace and Grace Hopper super chips and our BlueField-3 and Spectrum 4 networking platform. They are all in production. They will help deliver data center scale computing that is also energy-efficient and sustainable computing. Join us next week at COMPUTEX and we'll show you what's next. Thank you.
Operator: This concludes today's conference call. You may now disconnect. |
5,741 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Good afternoon. My name is Christa, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA Corporation's Fourth Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers' 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 the number one on your telephone keypad. And if you would like to withdraw your question, Thank you. Stewart Stecker. You may begin your conference. Thank you. |
5,742 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Stewart Stecker: Good afternoon, everyone, and welcome to NVIDIA Corporation's conference call for the fourth quarter of fiscal 2025. With me today from NVIDIA Corporation 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 Corporation's Investor website. Webcast will be available for replay until the conference call discuss our financial results, the first quarter of fiscal 2026. The content of today's call is NVIDIA Corporation's property. It can't be reproduced or transcribed without 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. 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 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, February 26, 2025, 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. Confined and reconciliation of these non-GAAP financial measures GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. |
5,743 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Thanks, Stewart. Q4 was another record quarter. Revenue of $39.3 billion was up 12% sequentially and up 78% year on year. And above our outlook, of $37.5 billion. For fiscal 2025, revenue was $130.5 billion. Up 114% in the prior year. Let's start with data center. Data center revenue for fiscal 2025 was $115.2 billion. More than doubling from the prior year. In the fourth quarter, it is in a revenue of $35.6 billion was a record, up 16% sequentially and 93% year on year. As the Blackwell ramp commenced, and Hopper 200 continued to contribute growth, In Q4, Blackwell sales exceeded our expectations. We delivered $11 billion of Blackwell revenue to meet strong demand. This is the fastest product ramp in our company's history. Unprecedented in its speed and scale. Blackwell production is in full gear across multiple configurations, and we are increasing supply quickly. Expanding customer adoption. Our Q4 data center compute revenue jumped 18% sequentially and over 2x year on year. Customers are racing to scale infrastructure to train the next generation of cutting-edge models and unlock the next level of AI capabilities. With Blackwell, it will be common for these clusters to start with 100,000 GPUs or more. Shipments have already started for multiple infrastructures of this size. Post-training and model customization are fueling demand for NVIDIA Corporation infrastructure and software as developers and enterprises leverage techniques such as fine-tuning, reinforcement learning, and distillation to tailor models for domain-specific use cases. Hugging Face alone hosts over 90,000 derivatives traded from the Llama Foundation model. The scale of post-training and model customization is massive and can collectively demand orders of magnitude more compute than pretraining. Our inference demand is accelerating. Driven by test time scaling and new reasoning models. Like OpenAI's O3, DeepSeq R1, and Grok 3. Long-thinking reasoning AI can require 100x more compute per task compared to one-shot inferences. |
5,744 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | O3, DeepSeq R1, and Grok 3. Long-thinking reasoning AI can require 100x more compute per task compared to one-shot inferences. Blackwell was architected for reasoning AI inference. Blackwell supercharges reasoning AI models with up to 25x higher token throughput and 20x lower cost versus Hopper 100. It is revolutionary. Transformer engine is built for LLM. And mixer of experts inference. And its NVLink domain delivers 14x the throughput of PCIe Gen 5. Ensuring the response time, throughput, and cost efficiency needed to tackle the growing complexity of inferences scale. Companies across industries are tapping into NVIDIA Corporation's full-stack inference platform to boost performance and slash cost. Now tripled inference throughput and cut cost by 66% using NVIDIA Corporation TensorRT for its screenshot feature. Perplexity sees 435 million monthly queries and reduced its inference costs 3x with NVIDIA Corporation Triton inference server and TensorRT LLM. Microsoft Bing achieved a 5x speedup at major TCO savings for visual search across billions of images with NVIDIA Corporation, TensorRT, and acceleration libraries. Blackwell has great demand for inference. Many of the early GV200 deployments are earmarked for inference. A first for a new architecture. Blackwell addresses the entire AI market from pretraining, post-training, to inference across clouds, to on-premise, to enterprise. Its programmable architecture accelerates every AI model and over 4,400 applications ensuring large infrastructure investments against obsolescence in rapidly evolving markets. Our performance and pace of innovation are unmatched. We're driven to a 200% reduction in inference cost in just the last two years. We delivered the lowest TCO and the highest ROI. And full-stack optimizations for NVIDIA Corporation and our large ecosystem including 5.9 million developers continuously improve our customers' economics. In Q4, large CSPs represented about half of our data center revenue. And these sales increased nearly 2x year on year. Large |
5,745 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | In Q4, large CSPs represented about half of our data center revenue. And these sales increased nearly 2x year on year. Large CSPs were some of the first to stand up Blackwell, with Azure, GCP, AWS, and OCI, bringing GV200 systems to cloud regions around the world to meet surging customer demand for AI. Regional cloud hosting NVIDIA Corporation GPUs increased as a percentage of data center revenue. Reflecting continued AI factory build-outs globally and rapidly rising demand for AI reasoning models and agents. Coreweave launched a 100,000 GV200 cluster-based instance with NVLink switch and Quantum-2 InfiniBand. Consumer Internet revenue grew 3x year on year. Driven by an expanding set of generative AI and deep learning use cases. These include recommender systems, vision language understanding, synthetic data generation search, and agentic AI. For example, XAI is adopting the GV200 to train and inference its next generation of Grok AI models. Meta's cutting-edge Andromeda, advertising engine runs on NVIDIA Corporation's Grace Hopper Superchip. Serving vast quantities of ads across Instagram, Facebook applications. Andromeda harnesses Grace Hopper's fast interconnect and large memory to boost inference, throughput by 3x. Enhanced ad personalization, and deliver meaningful jumps in monetization and ROI. Enterprise revenue increased nearly 2x year on accelerating demand model fine-tuning. Agentic AI workflows. And GPU-accelerated data processing. We introduced NVIDIA Corporation Llama Numitron model family nodes to help developers create and deploy AI agents across a range of applications, including customer support, fraud detection, and product supply chain and inventory management. Leading AI agent platform providers, including SAP and ServiceNow, are among the first to use new models. Health care leaders, IQVIA, and Lumenon. And Mayo Clinic as well as ARC and Institute are using NVIDIA Corporation AI to speed drug discovery enhance genomic research, and pioneer advanced health care services with generative and |
5,746 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Corporation AI to speed drug discovery enhance genomic research, and pioneer advanced health care services with generative and agentic AI. As AI expands beyond the digital world, NVIDIA Corporation infrastructure and software platforms are increasingly being adopted to power robotics and physical AI development. One of the early and largest robotics applications and autonomous vehicles were virtually every AV company is developing on NVIDIA Corporation, in the data center. NVIDIA Corporation's automotive vertical revenue is expected to grow to approximately $5 billion this fiscal year. At CES, Hyundai Motor Group announced it is adopting NVIDIA Corporation Technologies to accelerate AV and robotics development and smart factory initiatives. Vision transformers, self-supervised learning, multimodal sensor fusion, and high-fidelity simulation are driving breakthroughs in AV development and will require 10x more compute. At TDX, we announced the NVIDIA Corporation Cosmos World foundation model platform. Just as language foundation models have revolutionized language AI, Cosmos is a physical AI to revolutionize robotics. The robotics and automotive companies, including ride-sharing giant Uber, are among the first to adopt the platform. From a geographic perspective, sequential growth in our data center revenue was strongest in the US, driven by the initial ramp of Blackwell. Countries across the globe are building their AI ecosystems and demand for compute infrastructure is surging. France's €200 billion AI investment and the EU's €200 billion Invest AI initiative offer a glimpse into the build-out that will redefine global AI infrastructure in the coming years. Now as a percentage of total data center revenue, data center sales in China remained well below levels seen onset of export controls. China shipments absent any change in regulations, we believe that will remain roughly at the current percentage. The market in China for data center solutions remained very competitive. We will continue to comply with export |
5,747 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | percentage. The market in China for data center solutions remained very competitive. We will continue to comply with export controls while serving our customers. Networking revenue declined 3% sequentially. Our networking attached to GPU compute systems is robust at over 75%. We are transitioning from small NVLink 8 with InfiniBand to large NVLink 72. The Spectrum X. Spectrum X and NVLink switch revenue increased and represents a major new growth sector. We expect networking a return to growth in Q1. AI requires a new class of networking. NVIDIA Corporation offers NVLink switch systems for scale of compute. For scale out, we offer Quantum InfiniBand for HPC supercomputers, and SpectrumX for Ethernet environments. Spectrum X enhances the Ethernet for AI computing and has been a huge success. Microsoft Azure OCI, Fortease, and others are building large AI factories with SpectrumX. The first Stargate data centers will use Spectrum X. Yesterday, Cisco announced integrating Spectrum X into their networking portfolio to help enterprises build AI infrastructure. With its large enterprise footprint and global reach, Cisco will bring NVIDIA Corporation Ethernet to every industry. Now moving to gaming and AR PCs. Gaming revenue of $2.5 billion decreased 22% sequentially and 11% year on year. Full year revenue of $11.4 billion increased 9% year on year. And demand remains strong throughout the holiday. However, Q4 shipments were impacted by supply constraints. We expect strong sequential growth in Q1 as supply increases. The new GeForce RTX 50 series desktop and laptop GPUs are here. Built for gamers, creators, and developers, they fuse AI and graphics, redefining visual computing. Powered by the Blackwell architecture, fifth-generation tensor cores, and fourth-generation RT cores and featuring up to 3,400 AI TOPS. These GPUs deliver a 2x performance leap and new AI-driven rendering, including neural shaders, digital human technologies, geometry, and lighting. The new DLSS 4 boosts frame rates up to 8x with AI-driven frame |
5,748 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | shaders, digital human technologies, geometry, and lighting. The new DLSS 4 boosts frame rates up to 8x with AI-driven frame generation turning one rendered frame into three. It also features the industry's first real-time application of transformer models packing 2x more parameters and 4x to compute unprecedented visual fidelity. We also announced a wave of GeForce Blackwell laptop GPUs with new NVIDIA Corporation Max-Q technology that extends battery life, by up to an incredible 40%. These laptops will be available starting in March from the world's top manufacturers. Moving to our professional visualization business. Revenue of $511 million was up 5% sequentially and 10% year on year. Full year revenue of $1.9 billion increased 21% year on year. Key industry verticals driving demand include automotive and health care. NVIDIA Corporation Technologies and generative AI are reshaping design engineering, and simulation workloads. Increasingly, these technologies are being leveraged in leading software platforms. From ANSYS, Cadence, and Siemens fueling demand for NVIDIA Corporation RTX workstations. Now moving to automotive. Revenue was a record $570 million, up 27% sequentially and up 103% year on year. Full year revenue of $1.7 billion increased 55% year on year. Strong growth was driven by the continued ramp in autonomous vehicles, including cars and robotaxis. At CES, we announced Toyota the world's largest automaker will build its next generation vehicles on NVIDIA Corporation Oren, running the safety-certified NVIDIA Corporation Drive OS. We announced Aurora and Continental. Will deploy driverless trucks at scale powered by NVIDIA Corporation Drive 4. Finally, our end-to-end autonomous vehicle platform, NVIDIA Corporation DRIVE Hyperion, has passed industry safety assessments by Ryland, two of the industry's foremost authorities, automotive-grade safety and cybersecurity, NVIDIA Corporation is the first AV platform to receive a comprehensive set of third-party assessments. Moving to the rest of the P&L. |
5,749 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Corporation is the first AV platform to receive a comprehensive set of third-party assessments. Moving to the rest of the P&L. GAAP gross margins, was 73%. And non-GAAP gross margins were 73.5%. Down sequentially as expected with our first deliveries of the Blackwell architecture. As discussed last quarter, Blackwell is a customizable AI infrastructure with several different types of NVIDIA Corporation build chips. Multiple networking options, and for air and liquid-cooled data center. We exceeded our expectations in Q4, in ramping Blackwell, increasing system availability, providing several configurations to our customers. As Blackwell ramps, we expect gross margins to be in the low seventies. We initially, we are focused on expediting the manufacture as they race to build out Blackwell infrastructure. When fully ramped, we have many opportunities to improve the cost and gross margin. Will improve and return to the mid-seventies. Late this fiscal year. Sequentially, GAAP operating expenses were up 9% and non-GAAP operating expenses were 11%, reflecting higher engineering development costs and higher compute and infrastructure costs for new product introductions. In Q4, we returned $8.1 billion to shareholders, the form of share repurchases cash dividends. Let me turn to the outlook in the first quarter. Total revenue is expected to be $43 billion. Plus or minus 2%. Continuing with its strong demand, we expect a significant ramp of Blackwell in Q1. We expect sequential growth. In both data center and gaming. Within data center, we expect sequential growth from both. Compute and networking. GAAP and non-GAAP gross margins are expected to be 70.6%. And 71% respectively. Plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $5.2 billion and $3.6 billion. We expect full year fiscal year 2026 operating expenses grow to grow to be in the mid-thirties. GAAP and non-GAAP other incoming expenses are expected to be an income of approximately $400 million. Excluding gains and |
5,750 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | GAAP and non-GAAP other incoming expenses are expected to be an income of approximately $400 million. Excluding gains and losses, from non-marketable and publicly held equity securities. GAAP and non-GAAP tax rates are expected to be 17% plus or minus 1% excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website. Including a new financial information AI agent. In closing, let me highlight upcoming events for the financial community. We will be at the TD Cowen Healthcare Conference in Boston on March 3rd. And at the Morgan Stanley Technology, Media, and Telecom Conference in San Francisco. On March 5th. Please join us for our annual GTC conference starting Monday, March 17th, in San Jose, California. Jensen will deliver a news-packed keynote on March 18th, and we will host a Q&A session for our financial analysts. Next day, March 19th. We look forward to seeing you at these events. Our earnings call to discuss the results for our first quarter of fiscal 2026 is scheduled for May 28th, 2025. We are going to open up the call, operator. To questions. If you could start that, that would be great. |
5,751 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Thank you. At this time, I would like I also ask that you please limit yourself to one question. For any additional questions, please requeue. And your first question comes from C.J. Muse with Cantor Fitzgerald. Please go ahead.
C.J. Muse: Yeah. Good afternoon. Thank you for taking the question. I guess, for me, Jensen, as test time compute and reinforcement learning shows such promise, we're clearly seeing increasing blurring in the lines between training and inference. What does this mean for the potential future of potentially inference-dedicated clusters? And how do you think about the overall impact to NVIDIA Corporation and your customers? Thank you. |
5,752 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. I appreciate that, C.J. There are now multiple scaling laws. There's the pretrained scaling laws. And that's gonna continue to scale because we have multimodality. We have data that came from reasoning that are now used to pretraining. And then the second is post-training scaling law. Using reinforcement learning human feedback, reinforcement learning AI feedback, reinforcement learning verifiable rewards, the amount of computation you use for post-training is actually higher than pretraining. And it's kinda sensible in the sense that you could while you're using reinforcement learning, generate an enormous amount of synthetic data or synthetically generated tokens. AI models are basically generating tokens to train AI models. That's post-train. And the third part, this is the part that you mentioned, is test time compute or reasoning. Long thinking, inference scaling, basically the same ideas. And there's you have chain of thought, you have search. The amount of tokens generated, the amount of inference compute needed, is already a hundred times more than the one-shot examples and the one-shot capabilities of large language models in the beginning and that's just the beginning. This is just the beginning. The idea that the next generation could have thousands of times and even hopefully extremely thoughtful and simulation-based and search-based models that could be hundreds of thousands, millions of times more compute than today, is in our future. And so the question is how do you design such an architecture? Some of the models are autoregressive. Some of the models are diffusion-based. Some of the times you want your data center to have disaggregated inference. Sometimes it's compacted. And so it's hard to figure out what is the best configuration of a data center, which is the reason why NVIDIA Corporation's architecture is so popular. We run every model. We are great at training. The vast majority of our compute today is actually inference, and Blackwell takes all of that to a new level. |
5,753 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | at training. The vast majority of our compute today is actually inference, and Blackwell takes all of that to a new level. We designed Blackwell with the idea of reasoning models in mind. And you look at training, it's many times more performant. But what's really amazing is for long-thinking, test time scaling reasoning AI models, we're tens of times faster, 25 times higher throughput. And so Blackwell is gonna be incredible across the board. And when you have a data center, that allows you to configure and use your data center based on are you doing more pretraining now, post-training now? Or scaling out your inference our architecture is fungible, and easy to use. In all of those different ways. And so we're seeing, in fact, much, much more concentration of a unified architecture than ever before. |
5,754 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Your next question comes from the line of Joseph Moore with JPMorgan. Please go ahead.
Joseph Moore: I wonder if you could talk about GV200 at CES. You sort of talked about the complexity of the rack-level systems and the challenges you have. And then as you said in the prepared remarks, we've seen a lot of general availability. You know, where are you in terms of that ramp? Are there still bottlenecks to consider at a systems level above and beyond the chip level? And just you know, have you maintained your enthusiasm for the NVLink 72 platforms?
Jensen Huang: Well, I'm more enthusiastic today than I was at CES. And the reason for that is because we shipped a lot more to CES. We have some 350 plants manufacturing the one and a half million components that go into each one of the Blackwell racks. Base Blackwell racks. Yes. It's extremely complicated. And we successfully and incredibly ramped up Grace Blackwell. Delivering some $11 billion of revenues last quarter. We're gonna have to continue to scale as demand is quite high and customers are anxious and impatient to get their Blackwell systems. You'd probably seen on the web a fair number of celebrations about Grace Blackwell Systems coming online and we have them, of course. We have a fairly large installation of Grace Blackwell for our own engineering and our own design teams and software teams. Coreweave has now gone public about the successful bring-up of theirs. Microsoft has. Of course, OpenAI has. And you're starting to see many come online. So I think the answer to your question is nothing is easy about what we're doing. But we're doing great, and all of our partners are doing great.
Christa: Your next question comes from the line of Vivek Arya with Bank of America Securities. Please go ahead. |
5,755 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Your next question comes from the line of Vivek Arya with Bank of America Securities. Please go ahead.
Vivek Arya: Thank you for taking my question. Could I just you wouldn't mind confirming if Q1 is the bottom for gross margins? And then, Jensen, my question is for you. What is on your dashboard to give you the confidence that the strong demand can sustain into next year and has DeepSeq and whatever innovations they came up with, has that changed that view in any way? Thank you.
Colette Kress: Let me first take the first part of the question. Regarding the gross margin. During our Blackwell ramp, our gross margins will be in the low seventies. At this point, we are focusing on expediting our manufacturing. Expediting our manufacturing is to make sure that we can provide customers as soon as possible. Our Blackwell is fully ramped. And once it does, I'm sorry. Blackwell fully ramps, we can improve our cost and our gross margin. So we expect to probably be in the mid-seventies later this year. You know, walking through what you heard, Jensen speak about the systems and their complexity. They are customizable in some cases. They've got multiple networking options. Have liquid cool and water-cooled. So we know there is an opportunity for us to improve these gross margins going forward. But right now, we are gonna focus on getting the manufacturing plate into our customers as soon as possible. |
5,756 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: We know several things, Vivek. We have a fairly good line of sight of the amount of capital investment that data centers are building out towards. We know that going forward, the vast majority of software is gonna be based on machine learning. And so accelerated computing and generative AI, reasoning AI, are going to be the type of architecture you want in your data center. We have, of course, forecast and plans from our top partners. And we also know that there are many innovative really exciting start-ups that are still coming online. As new opportunities for developing the next breakthroughs in AI, whether it's agentic AIs, reasoning AIs, or physical AIs. The number of start-ups are still quite vibrant and each one of them needs a fair amount of computing infrastructure. So I think the whether it's the near-term signals or the mid-term signals. Near-term signals, of course, are, you know, POs and forecasts and things like that. Mid-term signals, would be the level of infrastructure and CapEx scale out compared to previous years. And then the long-term signals it has to do with the fact that we know fundamentally software has changed. From hand coding that runs on CPUs through machine learning and AI-based software that runs on GPUs and accelerated computing systems. So we have a fairly good sense that this is the future of software. And then maybe as you roll it out, another way to think about that is we've really only touched consumer AI and search and some amount of consumer generative AI. Advertising, recommenders, kind of the early days of software. The next wave's coming. Agentic AI for enterprise, physical AI for robotics. And Sovereign AI has different regions build out their AI for their own ecosystems. And so each one of these are barely off the ground, and we can see them. We can see them because, you know, obviously, we're in the center of much of this development. And we can see great activity happening in all these different places. And these will happen. So near-term, mid-term, |
5,757 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | And we can see great activity happening in all these different places. And these will happen. So near-term, mid-term, long-term. |
5,758 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Your next question comes from the line of Matt Ramsay with Cowen. Please go ahead.
Matt Ramsay: Yeah. Good afternoon. Thanks for taking my question. Your next generation Blackwell Ultra is set to launch in the second half of this year. In line with the team's annual product cadence. Jensen, can you help us understand the demand dynamics for Ultra given that you'll still be ramping the current generation Blackwell solutions? How do your customers and the supply chain also manage the simultaneous ramps of these two products and is the team still on track to execute Blackwell Ultra in the second half of this year? |
5,759 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yes. Blackwell Ultra is second half. As you know, the first Blackwell was have we had a hiccup? That probably cost us a couple of months. We're fully recovered, of course. The team did an amazing job recovery. And all of our supply chain partners and just so many people helped us recover at the speed of light. And so now we've successfully ramped production of Blackwell. But that doesn't stop the next train. The next train is you know, it's on an annual rhythm. And, Blackwell Ultra with, new networking, new memories, and, of course, new processors and all of that is coming online. We've been working with all of our partners and customers laying this out. They have all of the necessary information. And we'll work with everybody to do the proper transition. This time between Blackwell, Blackwell Ultra, the system architecture is exactly the same. It's a lot harder going from Hopper to Blackwell because we went from an NVLink 8 system to a NVLink 72 base system. So the chassis, the architecture of the system, the hardware, the power delivery, all of that had to change. This was quite a challenging transition. But the next transition will slot right in. Grace Blackwell Ultra will slot right in. We've also already revealed and been working very closely with all of our partners on the click after that. And the click after that is called Vera Rubin. And, all of our partners are getting up to speed on the transition of that. And so preparing for that transition and, again, we're gonna provide a big, big, huge step up. And so come to GTC, and I'll hold on to you about Blackwell Ultra, Vera Rubin, and then show you what's the one click after that. Really, really exciting new product, so come to GTC, please.
Christa: Your next question comes from the line of Timothy Arcuri with UBS. Please go ahead. |
5,760 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Your next question comes from the line of Timothy Arcuri with UBS. Please go ahead.
Timothy Arcuri: Thanks a lot. Jensen, we hear a lot about custom ASICs. Can you kinda speak to the balance between custom ASIC and merchant GPU? We hear about some of these heterogeneous super clusters to use both GPU and ASIC. Is that something customers are planning on building or will these infrastructures remain fairly distinct? Thanks. |
5,761 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Well, we build very different things than ASICs. In some ways, completely different in some areas we intercept. We're different in several ways. One, NVIDIA Corporation's architecture is general. You know, whether you've optimized for autoregressive models or diffusion-based models or vision-based models or multimodal models or text models. We're great in all of it. We're great in all of it because our software stack is so our architecture is responsible. Our software stack is ecosystem is so rich that we're the initial target of, you know, most exciting innovations and algorithms. And so by definition, we're much, much more general than narrow. We're also really good from the end to end. From data processing, the curation of the training data, to the training of the data, of course, to reinforcement learning used in post-training. All the way to inference with test time scaling. So, you know, we're general. We're end to end. And we're everywhere. And because we're not in just one cloud, we're in every cloud, we could be on-prem. We could be in, you know, in a robot. Our architecture is much more accessible. And a great target initial target for anybody who's starting up a new company. And so we're everywhere. And then the third thing I would say is that our performance and our rhythm is so incredibly fast. Remember that these data centers are always fixed in size. They're fixed in size or they're fixed in power. And if our performance per watt is anywhere from 2x to 4x to 8x, which is not unusual. It translates directly to revenues. And so if you have a 100-megawatt data center, if the performance or the throughput that 100-megawatt or that gigawatt data center is four times or eight times higher your revenues for that gigawatt data center is eight times higher. And the reason that is so different than data centers of the past is because AI factories are directly monetizable through its tokens generated. And so the token throughput of our architecture being so incredibly fast is just incredibly |
5,762 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | through its tokens generated. And so the token throughput of our architecture being so incredibly fast is just incredibly valuable to all of the companies that are building these things for revenue generation reasons. And capturing the fast ROIs. So I think the third reason is performance. And then the last thing that I would say is the software stack is incredibly hard. Building an ASIC is no different than what we do. We have to build a new architecture. And the ecosystem that sits on top of our architecture is ten times more complex today than it was two years ago. And that's fairly obvious because the amount of software this world building on top of architecture is growing exponentially and AI is advancing very quickly. So bringing that whole ecosystem on top of multiple chips is hard. And so I would say that those four reasons and then finally, I will say this. Just because the chip is designed doesn't mean it gets deployed. And you've seen this over and over again. There are a lot of chips that get built. But when the time comes a business decision has to be made. And that business decision is about deploying a new engine, a new processor into a limited AI factory in size and power and find. And our technology is, you know, not only more advanced, more performant, it has much, much better software capability, and very importantly, our ability to deploy is lightning fast. And so these things are enough for the faint of heart as everybody knows now. And so there's a lot of different reasons why we do well. Why we win. |
5,763 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Your next question comes from the line of Ben Reitzes with Melius Research. Please go ahead.
Ben Reitzes: Yeah. Hi. Ben Reitzes here. Hey. Thanks a lot for the question. Hey, Jensen. It's a geography-related question. You know, you did a great job explaining some of the demand underlying, you know, factors here on the strength. But the US was up about $5 billion or so sequentially. And I think, you know, there is a concern about whether the US can pick up the slack if there's regulations towards other geographies. And I was just wondering as we go throughout the year, you know, if this kind of surge in the US continues and it's gonna be whether that's okay. And if that underlies your growth rate, how can you keep growing so fast with this mix shift towards the US? Your guidance looks like China is probably up sequentially. So just wondering if you could go through that dynamic and maybe Colette can weigh in. Thanks a lot. |
5,764 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: China is approximately the same percentage as Q4. And as in as previous quarters. It's about half of what it was before the export control. But it's approximately the same in percentage. With respect to geographies, the takeaway is that AI is software. It's modern software. It's incredible modern software. But it's modern software. And AI has gone mainstream. AI is used in delivery services everywhere, shopping services everywhere. You know? You were to buy a quart of milk is delivered to you. AI was involved. And so almost everything that a consumer service provides AI's at the core of it. Every student will use AI as a tutor. Health care services use AI. Financial services use AI. No fintech company will not use AI. Every fintech company will. Climate tech company uses AI. Mineral Discovery now uses AI. The number of every higher education, every university, uses AI. So I think it is fairly safe to say that AI has gone mainstream. And that it's being integrated into every application. And our hope is that, of course, the technology continues to advance safely and advance in a helpful way to our society. And with that, you know, we're I do believe that we're at the beginning of this new transition. And what I mean by that in the beginning, is remember behind us has been decades of data centers and decades of computers that have been built. And they've been built for a world of hand coding and general-purpose computing. And CPUs and so on and so forth. And going forward, I think it's fairly safe to say that that world is going to be almost all software will be infused with AI. All software and all services will be based on ultimately based on machine learning, and the data flywheel is gonna part of improving software and services. And that the future computers will be accelerated. The future computers will be based on AI. And we're really three years into that journey. And in modernizing computers that have taken decades to build out. And so I'm fairly sure that we're in the beginning of this new |
5,765 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | in modernizing computers that have taken decades to build out. And so I'm fairly sure that we're in the beginning of this new era. And then lastly, no technology has ever had the opportunity to address a larger part of the world's GDP than AI. No software tool ever has. And so this is now a software tool that can address a much larger part of the world's GDP, more than any time in history. And so the way we think about growth and the way we think about whether something is big or small. Has to be in the context of that. And when you take a step back and look at it from that perspective, we're really just in the beginnings. |
5,766 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Your next question comes from the line of Aaron Rakers with Wells Fargo. Please go ahead. Erin, your line is open. Your next question comes from Mark Lipacis with Evercore ISI. Please go ahead.
Marshall Pappas: Hi. This is Marshall Pappas. Thanks for taking the question. Question. I had a clarification and a question. Colette, for the clarification. Did you say that enterprise within the data center grew 2x year on year for the January quarter? And if so, does that would that make it the faster growing than the hyperscalers? And then, Jensen, for you, the question, hyperscalers are the biggest purchasers of your solutions, but they buy equipment for both internal and external workloads, external workloads being cloud services that enterprises use. So the question is, can you give us a sense of how that hyperscale expense splits between that external workload and internal and as these new AI workflows and applications come up, would you expect enterprises to become a larger part of that consumption mix? And does that impact how you develop your service your ecosystem? Thank you.
Colette Kress: Sure. Thanks for the question regarding our enterprise business. Yes. It grew 2x. Very similar to what we were seeing with our large CSPs. Keep in mind, these are both important areas to understand. Working with the CSPs can be working on large language models. Can be working on inference on their own work? But keep in mind, that is also where the enterprises are surfacing. Your enterprises are both with your CSPs, as well as in terms of building on their own. They're both growing quite well. |
5,767 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: The CSPs are about half of our business. And the CSPs have internal consumption, and external consumption, as you say. And we're using of course, used for internal consumption. We work very closely with all of them to optimize workloads that are internal to them because they have a large infrastructure of NVIDIA Corporation gear that they could take advantage of. And the fact that we could be used for AI on the one hand, video processing on the other hand, data processing like Spark. We're fungible. And so the useful life. Our infrastructure is much better. If the useful life is much longer, then the TCO is also lower. And so the second part is how do we see the growth of enterprise or not CSPs, if you will, going forward? And the answer is I believe, long term. It is by far larger. And the reason for that is because if you look at the computer industry today, and what is not served by the computer industry is largely industrial. Let me give you an example. When we say enterprise, and let's say let's use a car company as an example because they make both soft things and hard things. And so in the case of a car company, the employees would be what we call enterprise. And agentic AI and software planning systems and tools, and we have some really exciting things to with you guys at GTC. Those agentic systems are for employees to make employees more productive. To design, to market, plan, to operate their company. That's agentic AIs. On the other hand, the cars that they manufacture also need AI. They need an AI system that trains the cars treats this entire giant fleet of cars, and you know, today, there's some billion cars on the road. Someday, there'd be a billion cars on the road, and every single one of those cars will be, you know, robotic cars. And they'll all be collecting data, and we'll be improving them using an AI factory where they whereas they have a car factory today, in the future, they'll have a car factory and an AI factory. And then inside the car itself is a robotic system. And so |
5,768 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | today, in the future, they'll have a car factory and an AI factory. And then inside the car itself is a robotic system. And so as you can see, there are three computers involved. And there's the computer that helps the people. There's the computer that builds the AI for it. The machineries. It could be, of course. Could be a tractor. It could be a lawnmower. It could be a human or a robot that's being developed today. It could be a building. It could be a warehouse. These physical systems require a new type of AI we call physical AI. They can't just understand the meaning of words and languages but they have to understand the meaning of the world. Friction and inertia, object permanence, and cause and effect, and all of those types of things that are common sense to you and I. But you know, AI has to go learn those physical effects. So we call that physical AI. That whole part of using agentic AI to revolutionize the way we work inside companies. That's just starting. This is now the beginning of the agentic AI era. And you hear a lot of people talking about it and got some really great things going on. And then there's the physical AI after that, and then there's robotic systems after that. And so these three computers are all brand new. And my sense is that long term, this will be by far a larger of a mold which kinda makes sense. You know, the world the world's GDP is represented by either heavy industries industrials. And companies that are providing for those. |
5,769 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: Your next question comes from the line of Aaron Rakers with Wells Fargo. Please go ahead.
Aaron Rakers: Yeah. Thanks for letting me back in. Jensen, I'm curious as we now approach the two-year anniversary of really the Hopper inflection that you saw in 2023 in Gen AI in general. We think about the roadmap you have in front of us, how do you think about the infrastructure that's been deployed from a replacement cycle perspective and whether, you know, if it's GV300 or if it's the Rubin cycle where we start to see maybe some refresh opportunity. I'm just curious to how you look at that. |
5,770 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. I appreciate it. First of all, people are still using Voltas. And Pascals, and Amperes. And the reason for that is because they're always things that because CUDA is so programmable, you could use it right well, one of the major use cases right now is data processing and data curation. You find a circumstance that an AI model is not very good at? You present that circumstance to a vision language model, let's say. Let's say it's a car? You present that circumstance to a vision language model, the vision language model actually looks at the circumstances. It's a this isn't this is what happened, and I wasn't very good at it. You then take that response, this the prompt, and you go and prompt an AI model to go find in your whole link of data of other circumstances like that. Whatever that circumstance was. And then you use an AI to do domain randomization and generate a whole bunch of other examples. And then from that, you can go train the model. And so you could use the Amperes to go and do data processing and data curation and machine learning-based search. And then you create the training dataset, which you then present to your Hopper systems for training. And so each one of these architectures are completely are you know, they're all CUDA compatible, and so everything runs on everything. But if you have infrastructure in place, and you can put the less intensive workloads onto the installed base of the past. All of our CPUs are very well employed.
Christa: We have time for one more question, and that question comes from Atif Malik with Citi. Please go ahead. |
5,771 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: We have time for one more question, and that question comes from Atif Malik with Citi. Please go ahead.
Atif Malik: Hi. Thank you for taking my question. I have a follow-up question on gross margins, Colette. I understand there are many moving parts that will yield and NVLink 72 and Ethernet mix. And you kind of tiptoed the earlier question if April quarter is the bottom. But second half would have to ramp, like, 200 basis point per quarter to get to the mid-seventies range that you're giving, for the end of the fiscal year. And we still don't know much about tariffs impact to broader semiconductor. So what kind of gives you the confidence in that trajectory in the back half of this year?
Colette Kress: Yeah. Thanks for the question. Our gross margins, they're quite complex. In terms of the material. And everything that we put together in a Blackwell system. Tremendous amount of opportunity to look at a lot of different pieces of that. On how we can better improve our gross margins over time. Remember, we have many different configurations as well. On Blackwell. That will be able to help us do that. So, together, working after we get some of these really strong ramping completed for our customers we can begin a lot of that work. If not, we're gonna probably start as soon as possible. If we can improve it in the short term, we will also do that. Tariffs, at this point, it's a little bit of an unknown. It's an unknown until we understand further what the US government's plan is, its timing, it's where, and how much. So at this time, we are awaiting but again, we would, of course, always follow export control and or tariffs in that manner.
Christa: Ladies and gentlemen, that does conclude our question and answer session. I'm sorry. Thank you. |
5,772 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: No. No. I'm gonna just wanna thank you. Up to, Jensen? And, like, the medium, a couple things. I just wanna thank you. Thank you, Colette. Demand for Blackwell is extraordinary. AI is evolving beyond perception. And generative AI into reasoning. With reasoning AI, we're observing another scaling law. Inference time or test time scaling. The more computation the more the model thinks the smarter the answer. Models like OpenAI's Grok 3, DeepSeq R1, are reasoning models that apply inference time scale. Reasoning models can consume a hundred times more compute. Future reasoning models can consume much more compute. DeepSeq R1 has ignited global enthusiasm. It's an excellent innovation. But even more importantly, it has open-sourced a world-class reasoning AI model. Nearly every AI developer is applying R1. Or chain of thought and reinforcement learning techniques like R1. To scale their model's performance. We now have three scaling laws, as I mentioned earlier. Driving the demand for AI computing. The traditional scaling laws of AI remain intact. Foundation models are being enhanced with multimodality. And pretraining is still growing. But it's no longer enough. We have two additional scaling dimensions. Post-training scaling, where reinforcement learning fine-tuning, model distillation, require orders of magnitude more compute than pretraining alone. Inference time scaling and reasoning where a single query can demand a hundred times more compute. We designed Blackwell for this moment a single platform that can easily transition from pretraining, post-training, and test time scaling. Blackwell's MP4 transformer engine, and NVLink 72 scale-up fabric. And new software technologies let Blackwell process reasoning AI models 25 times faster than Hopper. Blackwell, in all of these configurations, is in full production. Each Grace Blackwell NVLink 72 rack is an engineering marvel. One and a half million components produced across 350 manufacturing sites by nearly a hundred thousand factory operators. AI is |
5,773 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | One and a half million components produced across 350 manufacturing sites by nearly a hundred thousand factory operators. AI is advancing at light speed. We're at the beginning of reasoning AI and inference time scaling. But we're just at the start of the age of AI. Multimodal AIs. Enterprise AI, Sovereign AI. And physical AI are right around the corner. We will grow strongly in 2025. Going forward, data centers will dedicate most of CapEx to accelerated computing and AI. Data centers will increasingly become AI factories. And every company will have a either rented or self-operated. I wanna thank all of you for joining us today. Come join us at GTC in a couple of weeks gonna be talking about Blackwell Ultra, Rubin, and other new computing networking, reasoning AI, physical AI products. And a whole bunch more. Thank you. |
5,774 | NVDA | 4 | 2,025 | 2025-02-26 17:00:00 | NVIDIA Corporation | 32,307 | Christa: This concludes today's conference call. You may now disconnect. |
5,775 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Good afternoon. My name is Joel, and I'll be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Third Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. [Operator Instructions] Thank you. Stewart Stecker, you may begin your conference.
Stewart Stecker: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2025. 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 fourth quarter of fiscal 2025. The content of today's call is NVIDIA's property. It can't 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 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, November 20, 2024, 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 a 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. |
5,776 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Thank you, Stewart. Q3 was another record quarter. We continued to deliver incredible growth. Revenue of $35.1 billion was up 17% sequentially and up 94% year-on-year and, well above our outlook of $32.5 billion. All market platforms posted strong sequential and year-over-year growth, fueled by the adoption of NVIDIA accelerated computing and AI. Starting with Data Center, another record was achieved in Data Center. Revenue of $30.8 billion, up 17% sequential and up 112% year-on-year. NVIDIA Hopper demand is exceptional and sequentially, NVIDIA H200 sales increased significantly to double-digit billions, the fastest product ramp in our company's history. The H200 delivers up to 2 times faster inference performance and up to 50% improved TCO. Cloud service providers were approximately half of our data center sales with revenue increasing more than 2 times year-on-year. CSPs deployed NVIDIA H200 infrastructure and high-speed networking with installations scaling to tens of thousands of GPUs to grow their business and serve rapidly rising demand for AI training and inference workloads. NVIDIA H200-powered cloud instances are now available from AWS, CoreWeave, and Microsoft Azure with Google Cloud and OCI coming soon. Alongside significant growth from our large CSPs, NVIDIA GPU regional cloud revenue jumped 2 times year-on-year as North America, EMEA, and Asia Pacific regions ramped NVIDIA cloud instances and sovereign cloud buildout. Consumer Internet revenue more than doubled year-on-year as companies scaled their NVIDIA Hopper infrastructure to support next-generation AI models, training, multimodal and agentic AI, deep learning recommender engines, and generative AI inference and content creation workloads. NVIDIA's Ampere and Hopper infrastructures are fueling inference revenue growth for customers. NVIDIA is the largest inference platform in the world. Our large installed base and rich software ecosystem encourage developers to optimize for NVIDIA and deliver continued performance and TCL |
5,777 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | base and rich software ecosystem encourage developers to optimize for NVIDIA and deliver continued performance and TCL improvements. Rapid advancements in NVIDIA's software algorithms boosted Hopper inference throughput by an incredible 5 times in one year and cut time to first token by 5 times. Our upcoming release of NVIDIA NIM will boost Hopper Inference performance by an additional 2.4 times. Continuous performance optimizations are a hallmark of NVIDIA and drive increasingly economic returns for the entire NVIDIA installed base. Blackwell is in full production after a successfully executed mass change. We shipped 13,000 GPU samples to customers in the third quarter, including one of the first Blackwell DGX engineering samples to OpenAI. Blackwell is a full stack, full infrastructure, AI data center scale system with customizable configurations needed to address a diverse and growing AI market from x86 to ARM, training to inferencing GPUs, InfiniBand to Ethernet switches, and NVLINK and from liquid-cooled to air-cooled. Every customer is racing to be the first to market. Blackwell is now in the hands of all of our major partners and they are working to bring up their Data Centers. We are integrating Blackwell systems into the diverse Data Center configurations of our customers. Blackwell demand is staggering and we are racing to scale supply to meet the incredible demand customers are placing on us. Customers are gearing up to deploy Blackwell at scale. Oracle announced the world's first Zettascale AI Cloud computing clusters that can scale to over 131,000 Blackwell GPUs to help enterprises train and deploy some of the most demanding next-generation AI models. Yesterday, Microsoft announced they will be the first CSP to offer in private preview Blackwell-based cloud instances powered by NVIDIA GB200, and Quantum InfiniBand. Last week, Blackwell made its debut on the most recent round of MLPerf Training results, sweeping the per GPU benchmarks and delivering a 2.2 times leap in performance over Hopper. The |
5,778 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | of MLPerf Training results, sweeping the per GPU benchmarks and delivering a 2.2 times leap in performance over Hopper. The results also demonstrate our relentless pursuit to drive down the cost of compute. Just 64 Blackwell GPUs are required to run the GPT-3 benchmark compared to 256 H100s or a 4 times reduction in cost. NVIDIA Blackwell architecture with NVLINK Switch enables up to 30 times faster inference performance and a new level of inference scaling throughput and response time that is excellent for running new reasoning inference applications like OpenAI's o1 model. With every new platform shift, a wave of start-ups is created. Hundreds of AI native companies are already delivering AI services with great success. Through Google, Meta, Microsoft, and OpenAI are the headliners and Anthropic, Perplexity, Mistral, Adobe Firefly, Runway, Midjourney, Lightricks, Harvey, Codeium, Cursor, and Bridge are seeing great success, while thousands of AI-native startups are building new services. The next wave of AI are Enterprise AI and Industrial AI. Enterprise AI is in full throttle. NVIDIA AI Enterprise, which includes NVIDIA NeMo and NIM microservices is an operating platform of agentic AI. Industry leaders are using NVIDIA AI to build Co-Pilots and agents. Working with NVIDIA, Cadence, Cloudera, Cohesity, NetApp, Nutanix, Salesforce, SAP and ServiceNow are racing to accelerate development of these applications with the potential for billions of agents to be deployed in the coming years. Consulting leaders like Accenture and Deloitte are taking NVIDIA AI to the world's enterprises. Accenture launched a new business group with 30,000 professionals trained on NVIDIA AI technology to help facilitate this global build-out. Additionally, Accenture with over 770,000 employees is leveraging NVIDIA-powered Agentic AI applications internally, including in one case that cuts manual steps in marketing campaigns by 25% to 35%. Nearly 1,000 companies are using NVIDIA NIM and the speed of its uptake is evident in NVIDIA AI |
5,779 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | campaigns by 25% to 35%. Nearly 1,000 companies are using NVIDIA NIM and the speed of its uptake is evident in NVIDIA AI Enterprise monetization. We expect NVIDIA AI Enterprise full year revenue to increase over 2 times from last year and our pipeline continues to build. Overall, our software, service, and support revenue is annualizing at $1.5 billion, and we expect to exit this year annualizing at over $2 billion. Industrial AI and robotics are accelerating. This is triggered by breakthroughs in physical AI, foundation models that understand the physical world. Like NVIDIA NeMo for enterprise AI agents, we built NVIDIA Omniverse for developers to build, train, and operate industrial AI and robotics. Some of the largest industrial manufacturers in the world are adopting NVIDIA Omniverse to accelerate their businesses, automate their workflows, and to achieve new levels of operating efficiency. Foxconn, the world's largest electronics manufacturer is using digital twins and industrial AI built on NVIDIA Omniverse to speed the bring up of its Blackwells factories and drive new levels of efficiency. In its Mexico facility alone, Foxconn expects to reduce -- a reduction of over 30% in annual kilowatt-hour usage. From a geographic perspective, our Data Center revenue in China grew sequentially due to shipments of export-compliant copper products to industries. As a percentage of total Data Center revenue, it remains well below levels prior to the onset of export controls. We expect the market in China to remain very competitive going forward. We will continue to comply with export controls while serving our customers. Our sovereign AI initiatives continue to gather momentum as countries embrace NVIDIA accelerated computing for a new industrial revolution powered by AI. India's leading CSPs include Tata Communications and Yotta Data Services are building AI factories for tens of thousands of NVIDIA GPUs. By year-end, they will have boosted NVIDIA GPU deployments in the country by nearly 10 times. Infosys, TSE, Wipro |
5,780 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | NVIDIA GPUs. By year-end, they will have boosted NVIDIA GPU deployments in the country by nearly 10 times. Infosys, TSE, Wipro are adopting NVIDIA AI Enterprise and upskilling nearly 0.5 million developers and consultants to help clients build and run AI agents on our platform. In Japan, SoftBank is building the nation's most powerful AI supercomputer with NVIDIA DGX Blackwell and Quantum InfiniBand. SoftBank is also partnering with NVIDIA to transform the telecommunications network into a distributed AI network with NVIDIA AI Aerial and AI-RAN platform that can process both 5G RAN on AI on CUDA. We are launching the same in the US with T-Mobile. Leaders across Japan, including Fujitsu, NEC and NTT are adopting NVIDIA AI Enterprise and major consulting companies, including EY, Strategy, and Consulting will help bring NVIDIA AI technology to Japan's industries. Networking revenue increased 20% year-on-year. Areas of sequential revenue growth include InfiniBand and Ethernet switches, SmartNICs, and BlueField DPUs. The networking revenue was sequentially down, networking demand is strong and growing and we intend -- anticipate sequential growth in Q4. CSPs and supercomputing centers are using and adopting the NVIDIA InfiniBand platform to power new H200 clusters. NVIDIA Spectrum-X Ethernet for AI revenue increased over 3 times year-on-year and our pipeline continues to build with multiple CSPs and consumer Internet companies planning large cluster deployments. Traditional Ethernet was not designed for AI. NVIDIA Spectrum-X uniquely leverages technology previously exclusive to InfiniBand to enable customers to achieve massive scale of their GPU compute. Utilizing Spectrum-X, xAI's Colossus, 100,000 Hopper Supercomputer will experience zero application latency degradation and maintained 95% data throughput versus 60% for traditional Ethernet. Now moving to gaming and AI PCs. Gaming revenue of $3.3 billion increased 14% sequentially and 15% year-on-year. Q3 was a great quarter for gaming with notebook, console, and |
5,781 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | of $3.3 billion increased 14% sequentially and 15% year-on-year. Q3 was a great quarter for gaming with notebook, console, and desktop revenue, all growing sequentially and year-on-year. RTX end-demand was fueled by strong back-to-school sales as consumers continue to choose GeForce RTX GPUs and devices to power gaming, creative, and AI applications. Channel inventory remains healthy and we are gearing up for the holiday season. We began shipping new GeForce RTX AI PCs with up to 321 AI tops from ASUS and MSI with Microsoft's Copilot+ capabilities anticipated in Q4. These machines harness the power of RTX ray tracing and AI technologies to supercharge gaming, photo and video editing, image generation, and coding. This past quarter, we celebrated the 25th anniversary of the GeForce 256, the world's first GPU. The transforming computing graphics to igniting the AI revolution, NVIDIA's GPUs have been the driving force behind some of the most consequential technologies of our time. Moving to ProViz. Revenue of $486 million was up 7% sequentially and 17% year-on-year. NVIDIA RTX workstations continue to be the preferred choice to power professional graphics, design, and engineering-related workloads. Additionally, AI is emerging as a powerful demand driver, including autonomous vehicle simulation, generative AI model prototyping for productivity-related use cases, and generative AI, content creation in media and entertainment. Moving to Automotive. Revenue was a record $449 million, up 30% sequentially and up 72% year-on-year. Strong growth was driven by self-driving ramps of NVIDIA Orin and robust end-market demand for NAVs. Volvo Cars has rolling out its fully electric SUV built on NVIDIA Orin and DriveOS. Okay. Moving to the rest of the P&L. GAAP gross margin was 74.6% and non-GAAP gross margin was 75%, down sequentially, primarily driven by a mix-shift of the H100 systems to more complex and higher cost systems within Data Center. Sequentially, GAAP operating expenses and non-GAAP operating expenses were up 9% |
5,782 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | and higher cost systems within Data Center. Sequentially, GAAP operating expenses and non-GAAP operating expenses were up 9% due to higher compute, infrastructure, and engineering development costs for new product introductions. In Q3, we returned $11.2 billion to shareholders in the form of share repurchases and cash dividends. Well, let me turn to the outlook for the fourth quarter. Total revenue is expected to be $37.5 billion, plus or minus 2%, which incorporates continued demand for Hopper architecture and the initial ramp of our Blackwell products, while demand is greatly exceed supply, we are on track to exceed our previous Blackwell revenue estimate of several billion dollars as our visibility into supply continues to increase. On gaming, although sell-through was strong in Q3, we expect fourth quarter revenue to decline sequentially due to supply constraints. GAAP and non-GAAP gross margins are expected to be 73% and 73.5%, respectively, plus or minus 50 basis points. Blackwell is a customizable AI infrastructure with several different types of NVIDIA-build chips, multiple networking options, and for air and liquid-cooled Data Centers. Our current focus is on ramping to strong demand, increasing system availability, and providing the optimal mix of configurations to our customer. As Blackwell ramps, we expect gross margins to moderate to the low-70s. When fully ramped, we expect Blackwell margins to be in the mid-70s. GAAP and non-GAAP operating expenses are expected to be approximately $4.8 billion and $3.4 billion, respectively. We are a data center scale AI infrastructure company. Our investments include building data centers for development of our hardware and software stacks and to support new introductions. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $400 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 16.5% plus or minus 1%, excluding any discrete items. Further financial details are |
5,783 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | and non-GAAP tax rates are expected to be 16.5% plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR websites. In closing, let me highlight upcoming events for the financial community. We will be attending the UBS Global Technology and AI Conference on December 3rd, in Scottsdale. Please join us at CES in Las Vegas, where Jensen will deliver a keynote on January 6th, and we will host a Q&A session for financial analysts the next day on January 7th. Our earnings call to discuss results for the fourth quarter of fiscal 2025 is scheduled for February 26th, 2025. We will now open the call for questions. Operator, can you poll for questions, please? |
5,784 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Operator: [Operator Instructions] Your first question comes from the line of C.J. Muse of Cantor Fitzgerald. Your line is open.
C.J. Muse: Yes, good afternoon. Thank you for taking the question. I guess just a question for you on the debate around whether scaling for large language models have stalled. Obviously, we're very early here but would love to hear your thoughts on this front. How are you helping your customers as they work through these issues? And then obviously, part of the context here as we're discussing clusters that have yet to benefit from Blackwell. So is this driving even greater demand for Blackwell? Thank you. |
5,785 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: A foundation model pre-training scaling is intact and it's continuing. As you know, this is an empirical law, not a fundamental physical law, but the evidence is that it continues to scale. What we're learning, however, is that it's not enough that we've now discovered two other ways to scale. One is post-training scaling. Of course, the first generation of post-training was reinforcement learning human feedback, but now we have reinforcement learning AI feedback and all forms of synthetic data generated data that assists in post-training scaling. And one of the biggest events and one of the most exciting developments is Strawberry, ChatGPT o1, OpenAI's o1, which does inference time scaling, what's called test time scaling. The longer it thinks, the better and higher-quality answer it produces and it considers approaches like chain of thought and multi-path planning and all kinds of techniques necessary to reflect and so on and so forth and it's intuitively, it's a little bit like us doing thinking in our head before we answer a question. And so we now have three ways of scaling and we're seeing all three ways of scaling. And as a result of that, the demand for our infrastructure is really great. You see now that at the tail-end of the last generation of foundation models were at about 100,000 Hoppers. The next generation starts at 100,000 Blackwells. And so that kind of gives you a sense of where the industry is moving with respect to pre-training scaling, post-training scaling, and then now very importantly inference time scaling. And so the demand is really great for all of those reasons. But remember, simultaneously, we're seeing inference really starting to scale out for our company. We are the largest inference platform in the world today because our installed base is so large and everything that was trained on Amperes and Hoppers inference incredibly on Amperes and Hoppers. And as we move to Blackwells for training foundation models, it leads behind it a large installed base of extraordinary |
5,786 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | And as we move to Blackwells for training foundation models, it leads behind it a large installed base of extraordinary infrastructure for inference. And so we're seeing inference demand go up. We're seeing inference time scaling go up. We see the number of AI-native companies continue to grow. And of course, we're starting to see enterprise adoption of agentic AI really is the latest rage. And so we're seeing a lot of demand coming from a lot of different places. |
5,787 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Toshiya Hari of Goldman Sachs. Your line is open.
Toshiya Hari: Hi, good afternoon. Thank you so much for taking the question. Jensen, you executed the mass change earlier this year. There were some reports over the weekend about some heating issues. On the back of this, we've had investors ask about your ability to execute to the roadmap you presented at GTC this year with Ultra coming out next year and the transition to [Ruben] (ph) in 2026. Can you sort of speak to that? And some investors are questioning that. So if you can sort of speak to your ability to execute on time, that would be super helpful. And then a quick part B, on supply constraints, is it a multitude of componentry that's causing this? Or is it specifically [HBM] (ph)? Is it supply constraints? Are the supply constraints getting better? Are they worsening? Any sort of color on that would be super helpful as well. Thank you. |
5,788 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yes, thanks. Thanks. So let's see, back to the first question. Blackwell production is in full steam. In fact, as Colette mentioned earlier, we will deliver this quarter more Blackwells than we had previously estimated. And so the supply chain team is doing an incredible job working with our supply partners to increase Blackwell, and we're going to continue to work hard to increase Blackwell through next year. It is the case that demand exceeds our supply and that's expected as we're in the beginnings of this generative AI revolution as we all know. And we're at the beginning of a new generation of foundation models that are able to do reasoning and able to do long thinking and of course, one of the really exciting areas is physical AI, AI that now understands the structure of the physical world. And so Blackwell demand is very strong. Our execution is on -- is going well. And there's obviously a lot of engineering that we're doing across the world. You see now systems that are being stood up by Dell and CoreWeave, I think you saw systems from Oracle stood up. You have systems from Microsoft and they're about to preview their Grace Blackwell systems. You have systems that are at Google. And so all of these CSPs are racing to be first. The engineering that we do with them is, as you know, rather complicated. And the reason for that is because although we build full stack and full infrastructure, we disaggregate all of the -- this AI supercomputer and we integrate it into all of the custom data centers in architectures around the world. That integration process is something we've done several generations now. We're very good at it, but still, there's still a lot of engineering that happens at this point. But as you see from all of the systems that are being stood up, Blackwell is in great shape. And as we mentioned earlier, the supply and what we're planning to ship this quarter is greater than our previous estimates. With respect to the supply chain, all right, there are seven different chips, seven |
5,789 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | is greater than our previous estimates. With respect to the supply chain, all right, there are seven different chips, seven custom chips that we built in order for us to deliver the Blackwell systems. The Blackwell systems go in air-cooled or liquid-cooled, NVLink 8 or NVLink 72 or NVLink 8, NVLink 36, NVLink 72 we have x86 or Grace and the integration of all of those systems into the world's data centers is nothing short of a miracle. And so the component supply chain necessary to ramp at the scale you have to go back and take a look at how much Blackwell we shipped last quarter, which was zero. And in terms of how much Blackwell total systems will ship this quarter, which is measured in billions, the ramp is incredible. And so almost every company in the world seems to be involved in our supply chain. And we've got great partners, everybody from, of course, TSMC and Amphenol, the connector company, incredible company, Vertiv and SK Hynix and Micron Spill, Amkor and KYEC and there's Foxconn and the many the factories that they've built and Quanta and Wiwynn and gosh, Dell and HP and Super Micro, Lenovo and the number of companies is just really quite incredible, Quanta. And I'm sure I've missed partners that are involved in the ramping up of Blackwell, which I really appreciate. And so anyways, I think we're in great shape with respect to the Blackwell ramp at this point. And then lastly, your question about our execution of our roadmap. We're on an annual roadmap and we're expecting to continue to execute on our annual roadmap. And by doing so, we increase the performance, of course, of our platform, but it's also really important to realize that when we're able to increase performance and do so at X factors at a time, we're reducing the cost of training, we're reducing the cost of inferencing, we're reducing the cost of AI so that it could be much more accessible. But the other factor that's very important to note is that when there's a data center of some fixed size and a data center always is of some fixed |
5,790 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | that's very important to note is that when there's a data center of some fixed size and a data center always is of some fixed size. It could be, of course, 10s of megawatts in the past and now it's most data centers are now 100 megawatts to several 100 megawatts and we're planning on gigawatt data centers, it doesn't really matter how large the data centers are, the power is limited. And when you're in the power-limited data center, the best -- the highest performance per watt translates directly into the highest revenues for our partners. And so on the one hand, our annual roadmap reduces cost, but on the other hand, because our perf per watt is so good compared to anything out there, we generate for our customers the greatest possible revenues. And so that annual rhythm is really important to us and we have every intentions of continuing to do that. And everything is on track as far as I know. |
5,791 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Timothy Arcuri of UBS. Your line is open.
Timothy Arcuri: Thanks a lot. I'm wondering if you can talk about the trajectory of how Blackwell is going to ramp this year. I know, Jensen, you did just talk about Blackwell being better than I think you had said several billions of dollars in January. It sounds like you're going to do more than that. But I think in recent months also, you said that Blackwell crosses over Hopper in the April quarter. So I guess I had two questions. First of all, is that still the right way to think about it that Blackwell will crossover Hopper in April? And then Colette, you kind of talked about Blackwell bringing down gross margin to the low-70s as it ramps. So I guess if April is the crossover, is that the worst of the pressure on gross margin? So you're going to be kind of in the low-70s as soon as April. I'm just wondering if you can sort of shape that for us. Thanks.
Jensen Huang: Colette, why don't you start?
Colette Kress: Sure. Let me first start with your question, Tim. Thank you regarding our gross margins, and we discussed our gross margins as we are ramping Blackwell in the very beginning and the many different configurations, the many different chips that we are bringing to market, we are going to focus on making sure we have the best experience for our customers as they stand that up. We will start growing into our gross margins, but we do believe those will be in the low 70s in that first part of the ramp. So you're correct, as you look at the quarters following after that, we will start increasing our gross margins and we hope to get to the mid-70s quite quickly as part of that ramp. |
5,792 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Hopper demand will continue through next year, surely the first several quarters of the next year. And meanwhile, we will ship more Blackwells next quarter than this. And we'll ship more Blackwells the quarter after that than our first quarter. And so that kind of puts it in perspective. We are really at the beginnings of two fundamental shifts in computing that is really quite significant. The first is moving from coding that runs on CPUs to machine learning that creates neural networks that runs on GPUs. And that fundamental shift from coding to machine learning is widespread at this point. There are no companies who are not going to do machine learning. And so machine learning is also what enables generative AI. And so on the one hand, the first thing that's happening is a trillion dollars’ worth of computing systems and data centers around the world is now being modernized for machine learning. On the other hand, secondarily, I guess, is that, that on top of these systems are going to be -- we're going to be creating a new type of capability called AI. And when we say generative AI, we're essentially saying that these data centers are really AI factories. They're generating something. Just like we generate electricity, we're now going to be generating AI. And if the number of customers is large, just as the number of consumers of electricity is large, these generators are going to be running 24/7. And today, many AI services are running 24/7, just like an AI factory. And so we're going to see this new type of system come online, and I call it an AI factory because that's really as close to what it is. It's unlike a data center of the past. And so these two fundamental trends are really just beginning. And so we expect this to happen this growth -- this modernization and the creation of a new industry to go on for several years.
Operator: Your next question comes from the line of Vivek Arya of Bank of America Securities. Your line is open. |
5,793 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Vivek Arya of Bank of America Securities. Your line is open.
Vivek Arya: Thanks for taking my question. Colette, just to clarify, do you think it's a fair assumption to think NVIDIA could recover to kind of mid-70s gross margin in the back half of calendar 2025? Just wanted to clarify that. And then, Jensen my main question historically, when we have seen hardware deployment cycles, they have inevitably included some digestion along the way. When do you think we get to that phase, or is it just too premature to discuss that because you're just the start of Blackwell? So how many quarters of shipments do you think is required to kind of satisfy this first wave? Can you continue to grow this into calendar 2026? Just how should we be prepared to see what we have seen historically, right, the periods of digestion along the way of a long-term kind of secular hardware deployment?
Colette Kress: Okay. Vivek, thank you for the question. Let me clarify your question regarding gross margins. Could we reach the mid-70s in the second half of next year? And yes, I think it is reasonable assumption or a goal for us to do, but we'll just have to see how that mix of ramp goes. But yes, it is definitely possible. |
5,794 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: The way to think through that, Vivek, is I believe that there will be no digestion until we modernize a trillion dollars with the data centers. Those -- if you look at the world's data centers, the vast majority of it is built for a time when we wrote applications by hand and we ran them on CPUs. It's just not a sensible thing to do anymore. If you have -- if every company's CapEx, if they're ready to build a data center tomorrow, they ought to build it for a future of machine-learning and generative AI. Because they have plenty of old data centers. And so what's going to happen over the course of next X number of years, and let's assume that over the course of four years, the world's data centers could be modernized as we grow into IT. As you know, IT continues to grow about 20%, 30% a year, let's say. And let's say by 2030, the world's data centers for computing is, call it a couple of trillion dollars. And we have to grow into that. We have to modernize the data center from coding to machine learning. That's number one. The second part of it is generative AI, and we're now producing a new type of capability that world has never known, a new market segment that the world has never had. If you look at OpenAI, it didn't replace anything. It's something that's completely brand new. It's in a lot of ways as when the iPhone came, it was completely brand new. It wasn't really replacing anything. And so we're going to see more and more companies like that. And they're going to create and generate out of their services, essentially intelligence. Some of it would be digital artist intelligence like Runway. Some of it would be basic intelligence, like OpenAI. Some of it would be legal intelligence like Harvey. Digital marketing intelligence like [Reuters] (ph), so on and so forth. And the number of these companies, these -- what are they call AI-native companies are just in hundreds and almost every platform shift there was -- there were Internet companies as you recall, there were cloud-first companies. |
5,795 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | and almost every platform shift there was -- there were Internet companies as you recall, there were cloud-first companies. They were mobile-first companies and now they're AI natives. And so these companies are being created because people see that there's a platform shift and there's a brand new opportunity to do something completely new. And so my sense is that we're going to continue to build out to modernize IT, modernize computing, number one. And then number two, create these AI factories that are going to be for a new industry for the production of artificial intelligence. |
5,796 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Stacy Rasgon of Bernstein Research. Your line is open.
Stacy Rasgon: Hi, guys. Thanks for taking my questions. Colette, I had a clarification and a question for you. The clarification, just when you say low-70s gross margins, is 73.5 count as low-70s, or do you have something else in mind? And for my question, you're guiding total revenues and so I mean, total Data Center revenues in the next quarter must be up quote-unquote several billion dollars, but it sounds like Blackwell now should be up more than that. But you also said Hopper was still strong. So like is Hopper down sequentially next quarter? And if it is like why? Is it because of the supply constraints? Is China has been pretty strong is China is kind of rolling off a bit into Q4. So any color you can give us on sort of the Blackwell ramp and the Blackwell versus Hopper behavior into Q4 would be really helpful. Thank you. |
5,797 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: So first starting on your first question there, Stacy, regarding our gross margin and defined low. Low, of course, is below the mid, and let's say we might be at 71%, maybe about 72%, 72.5%, we're going to be in that range. We could be higher than that as well. We're just going to have to see how it comes through. We do want to make sure that we are ramping and continuing that improvement, the improvement in terms of our yields, the improvement in terms of the product as we go through the rest of the year. So we'll get up to the mid-70s by that point. The second statement was a question regarding our Hopper and what is our Hopper doing. We have seen substantial growth for our H200, not only in terms of orders but the quickness in terms of those that are standing that up. It is an amazing product and it's the fastest-growing and ramping that we've seen. We will continue to be selling Hopper in this quarter, in Q4 for sure, that is across-the-board in terms of all of our different configurations and our configurations include what we may do in terms of China. But keep that in mind that folks are also at the same time looking to build out their Blackwell. So we've got a little bit of both happening in Q4. But yes, is it possible for Hopper to grow between Q3 and Q4, it's possible, but we'll just have to see.
Operator: Your next question comes from the line of Joseph Moore of Morgan Stanley. Your line is open.
Joseph Moore: Great. Thank you. I wonder if you could talk a little bit about what you're seeing in the inference market. You've talked about Strawberry and some of the ramifications of longer scaling inference projects. But you've also talked about the possibility that as some of these Hopper clusters age that you could use some of the Hopper latent chips for inference. So I guess, do you expect inference to outgrow training in the next kind of 12-month time frame, and just generally your thoughts there? |
5,798 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Our hopes and dreams is that someday, the world does a ton of inference. And that's when AI has really succeeded, right. It's when every single company is doing inference inside their companies for the marketing department and forecasting department and supply chain group and their legal department and engineering, of course and coding, of course. And so we hope that every company is doing inference 24/7. And that there will be a whole bunch of AI native startups, thousands of AI native startups that are generating tokens and generating AI and every aspect of your computer experience from using Outlook to PowerPointing or when you're sitting there with Excel, you're constantly generating tokens. And every time you read a PDF, open a PDF, it generated a whole bunch of tokens. One of my favorite applications is NotebookLM, this Google application that came out. I use the living daylights out of it just because it's fun. And I put every PDF, every archive paper into it just to listen to it as well as scanning through it. And so I think -- that's the goal is to train these models so that people use it. And there's now a whole new era of AI if you will, a whole new genre of AI called physical AI, just those large language models understand the human language and how we the thinking process, if you will. Physical AI understands the physical world and it understands the meaning of the structure and understands what's sensible and what's not and what could happen and what won't and not only does it understand but it can predict and roll out a short future. That capability is incredibly valuable for industrial AI and robotics. And so that's fired up so many AI-native companies and robotics companies and physical AI companies that you're probably hearing about. And it's really the reason why we built Omniverse. Omniverse is so that we can enable these AIs to be created and learn in Omniverse and learn from synthetic data generation and reinforcement learning physics feedback instead of human feedback is now |
5,799 | NVDA | 3 | 2,025 | 2024-11-20 17:00:00 | NVIDIA Corporation | 32,307 | Omniverse and learn from synthetic data generation and reinforcement learning physics feedback instead of human feedback is now physics feedback. To have these capabilities, Omniverse was created so that we can enable physical AI. And so that the goal is to generate tokens. The goal is to inference and we're starting to see that growth happening. So I'm super excited about that. Now let me just say one more thing. Inference is super hard. And the reason why inference is super hard is because you need the accuracy to be high on the one hand. You need the throughput to be high so that the cost could be as low as possible, but you also need the latency to be low. And computers that are high throughput as well as low latency is incredibly hard to build. And these applications have long context lengths because they want to understand, they want to be able to inference within understanding the context of what's -- what they're being asked to do. And so the context length is growing larger and larger. On the other hand, the models are getting larger, they're multimodality. Just the number of dimensions that inference is innovating is incredible. And this innovation rate is what makes NVIDIA's architecture so great because our ecosystem is fantastic. Everybody knows that if they innovate on top of CUDA on top of NVIDIA's architecture, they can innovate more quickly and they know that everything should work. And if something were to happen, it's probably likely their code and not ours. And so that ability to innovate in every single direction at the same time, having a large installed base so that whatever you create could land on a NVIDIA computer and be deployed broadly all around the world in every single data center all the way out to the edge into robotic systems, that capability is really quite phenomenal. |
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