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5,600 | NKE | 1 | 2,025 | 2024-10-01 17:00:00 | NIKE, Inc. | 291,981 | Operator: And ladies and gentlemen, that does conclude today's conference call. Thank you for your participation, and you may now disconnect. |
5,601 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Good afternoon. My name is Rob and I'll be your conference operator today. At this time, I would like to welcome everyone to the NVIDIA's Fourth Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question-and-answer session. [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 fourth quarter and fiscal 2024. With me today from NVIDIA are Jen-Hsun 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 first 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, February 21, 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. |
5,602 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Thanks, Simona. Q4 was another record quarter. Revenue of $22.1 billion was up 22% sequentially and up to 265% year-on-year and well above our outlook of $20 billion. For fiscal 2024, revenue was $60.9 billion and up 126% from the prior year. Starting with data center. Data center revenue for the fiscal 2024 year was $47.5 billion, more than tripling from the prior year. The world has reached the tipping point of new computing era. The $1 trillion installed base of data center infrastructure is rapidly transitioning from general purpose to accelerated computing. As Moore's Law slows while computing demand continues to skyrocket, companies may accelerate every workload possible to drive future improvement in performance, TCO and energy efficiency. At the same time, companies have started to build the next generation of modern data centers, what we refer to as AI factories, purpose built to refine raw data and produce valuable intelligence in the era of generative AI. In the fourth quarter, data center revenue of $18.4 billion was a record, up 27% sequentially and up 409% year-over-year, driven by the NVIDIA Hopper GPU computing platform along with InfiniBand end-to-end networking. Compute revenue grew more than 5x and networking revenue tripled from last year. We are delighted that supply of Hopper architecture products is improving. Demand for Hopper remains very strong. We expect our next-generation products to be supply constrained as demand far exceeds supply. Fourth quarter data center growth was driven by both training and inference of generative AI and large language models across a broad set of industries, use cases and regions. The versatility and leading performance of our data center platform enables a high return on investment for many use cases, including AI training and inference, data processing and a broad range of CUDA accelerated workloads. We estimate in the past year approximately 40% of data center revenue was for AI inference. Building and deploying AI solutions has reached |
5,603 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | in the past year approximately 40% of data center revenue was for AI inference. Building and deploying AI solutions has reached virtually every industry. Many companies across industries are training and operating their AI models and services at scale, enterprises across NVIDIA AI infrastructure through cloud providers, including hyperscales, GPU specialized and private clouds or on-premise. NVIDIA's computing stack extends seamlessly across cloud and on-premise environments, allowing customers to deploy with a multi-cloud or hybrid-cloud strategy. In the fourth quarter, large cloud providers represented more than half of our data center revenue, supporting both internal workloads and external public cloud customers. Microsoft recently noted that more than 50,000 organizations use GitHub Copilot business to supercharge the productivity of their developers, contributing to GitHub revenue growth accelerating to 40% year-over-year. And Copilot for Microsoft 365 adoption grew faster in its first two months than the two previous major Microsoft 365 enterprise suite releases did. Consumer internet companies have been early adopters of AI and represent one of our largest customer categories. Companies from search to e-commerce, social media, news and video services and entertainment are using AI for deep learning-based recommendation systems. These AI investments are generating a strong return by improving customer engagement, ad conversation and click-throughs rates. Meta in its latest quarter cited more accurate predictions and improved advertiser performance as contributing to the significant acceleration in its revenue. In addition, consumer internet companies are investing in generative AI to support content creators, advertisers and customers through automation tools for content and ad creation, online product descriptions and AI shopping assistance. Enterprise software companies are applying generative AI to help customers realize productivity gains. Early customers we've partnered with for both training and |
5,604 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | applying generative AI to help customers realize productivity gains. Early customers we've partnered with for both training and inference of generative AI are already seeing notable commercial success. ServiceNow's generative AI products in their latest quarter drove their largest ever net new annual contract value contribution of any new product family release. We are working with many other leading AI and enterprise software platforms as well, including Adobe, Databricks, Getty Images, SAP and Snowflake. The field of foundation of large-language models is thriving. Anthropic, Google, Inflection, Microsoft, OpenAI and xAI are leading with continued amazing breakthrough in generative AI. Exciting companies like Adept, AI21, Character.ai, Cohere, Mistral, Perplexity and Runway are building platforms to serve enterprises and creators. New startups are creating LLMs to serve the specific languages, cultures and customs of the world many regions. And others are creating foundation models to address entirely different industries like Recursion Pharmaceuticals and Generate:Biomedicines for biology. These companies are driving demand for NVIDIA AI infrastructure through hyperscale or GPU specialized cloud providers. Just this morning, we announced that we've collaborated with Google to optimize its state-of-the art new Gemma language models to accelerate their inference performance on NVIDIA GPUs in the cloud data center and PC. One of the most notable trends over the past year is the significant adoption of AI by enterprises across the industry verticals such as automotive, healthcare and financial services. NVIDIA offers multiple application frameworks to help companies adopt AI in vertical domains such as autonomous driving, drug discovery, low latency machine learning for fraud detection or robotics, leveraging our full stack accelerated computing platform. We estimate the data center revenue contribution of the automotive vertical through the cloud or on-prem exceeded $1 billion last year. NVIDIA DRIVE |
5,605 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | center revenue contribution of the automotive vertical through the cloud or on-prem exceeded $1 billion last year. NVIDIA DRIVE infrastructure solutions includes systems and software for the development of autonomous driving, including data ingestion, creation, labeling and AI training, plus validation through simulation. Almost 80 vehicle manufacturers across global OEMs, new energy vehicles, trucking, robotaxi and Tier 1 suppliers are using NVIDIA's AI infrastructure to train LLMs and other AI models for automated driving and AI cockpit applications. And in fact, nearly every automotive company working on AI is working with NVIDIA. As AV algorithms move to video transformers and more cars are equipped with cameras, we expect NVIDIA's automotive data center processing demand to grow significantly. In healthcare, digital biology and generative AI are helping to reinvent drug discovery, surgery, medical imaging and wearable devices. We have built deep domain expertise in healthcare over the past decade, creating the NVIDIA Clara healthcare platform and NVIDIA BioNeMo, a generative AI service to develop, customize and deploy AI foundation models for computer-aided drug discovery. BioNeMo features a growing collection of pre-trained Biomolecular AI models that can be applied to the end-to-end drug discovery processes. We announced Recursion is making available for their proprietary AI model through BioNeMo for the drug discovery ecosystem. In financial services, customers are using AI for a growing set of use cases from trading and risk management to customer service and fraud detection. For example, American Express improved fraud detection accuracy by 6% using NVIDIA AI. Shifting to our data center revenue by geography. Growth was strong across all regions, except for China where our data center revenue declined significantly following the U.S. government export control regulations imposed in October. Although we have not received licenses from the U.S. government to ship restricted products to China, we have |
5,606 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | in October. Although we have not received licenses from the U.S. government to ship restricted products to China, we have started shipping alternatives that don't require a license for the China market. China represented a mid-single digit percentage of our data center revenue in Q4. And we expect it to stay in a similar range in the first-quarter. In regions outside of the U.S. and China, sovereign AI has become an additional demand driver. Countries around the world are investing in AI infrastructure to support the building of large-language models in their own language, on domestic data and in support of their local research and enterprise ecosystems. From a product perspective, the vast majority of revenue was driven by our Hopper architecture along with InfiniBand networking. Together, they have emerged as the de-facto standard for accelerated computing and AI infrastructure. We are on track to ramp H200 with initial shipments in the second quarter. Demand is strong as H200 nearly doubles the inference performance of H100. Networking exceeded a $13 billion annualized revenue run rate. Our end-to-end networking solutions define modern AI data centers. Our Quantum InfiniBand solutions grew more than 5x year on year. NVIDIA Quantum InfiniBand is the standard for the highest performance AI-dedicated infrastructures. We are now entering the ethernet networking space with the launch of our new Spectrum-X end-to-end offering designed for an AI-optimized networking for the data center. Spectrum-X introduces new technologies over ethernet, that are purpose built for AI. Technologies incorporated in our Spectrum switch, BlueField DPU and software stack deliver 1.6x higher networking performance for AI processing compared with traditional ethernet. Leading OEMs, including Dell, HPE, Lenovo and Super Micro, with their global sales channels, are partnering with us to expand our AI solution to enterprises worldwide. We are on track to ship Spectrum-X this quarter. We also made great progress with our software and |
5,607 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | to enterprises worldwide. We are on track to ship Spectrum-X this quarter. We also made great progress with our software and services offerings, which reached an annualized revenue run rate of $1 billion in Q4. We announced that NVIDIA DGX Cloud will expand its list of partners to include Amazon's AWS, joining Microsoft Azure, Google Cloud and Oracle Cloud. DGX Cloud is used for NVIDIA's own AI R&D and custom model development as well as NVIDIA developers. It brings the CUDA ecosystem to NVIDIA CSP partners. Okay, moving to gaming. Gaming revenue was $2.87 billion, was flat sequentially and up 56% year on year, better than our outlook on solid consumer demand for NVIDIA GeForce RTX GPUs during the holidays. Fiscal year revenue of $10.45 billion was up 15%. At CES, we announced our GeForce RTX 40 Super Series family of GPUs. Starting at $599, they deliver incredible gaming performance and generative AI capabilities. Sales are off to a great start. NVIDIA AI Tensor cores and the GPUs deliver up to 836 AI tops, perfect for powering AI for gaming, creating an everyday productivity. The rich software stack we offer with our RTX GPUs further accelerates AI. With our DLSS technologies, seven out of eight pixels can be AI generated, resulting up to 4x faster ray tracing and better image quality. And with the Tensor RT LLM for Windows, our open-source library that accelerates inference performance for the latest large-language models generative AI can run up to 5X faster on RTX AI PCs. At CES, we also announced a wave of new RTX 40 Series AI laptops from every major OEMs. These bring high-performance gaming and AI capabilities to a wide range of form factors, including 14 inch and thin and light laptops. With up to 686 tops of AI performance, these next-generation AI PCs increase generative AI performance by up to 60x, making them the best-performing AI PC platforms. At CES, we announced NVIDIA Avatar Cloud Engine microservices, which allowed developers to integrate state-of-the-art generative AI models into digital |
5,608 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Avatar Cloud Engine microservices, which allowed developers to integrate state-of-the-art generative AI models into digital avatars. ACE won several Best of CES 2024 awards. NVIDIA has an end-to-end platform for building and deploying generative AI applications for RTX PCs and workstations. This includes libraries, SDKs, tools and services developers can incorporate into their generative AI workloads. NVIDIA is fueling the next wave of generative AI applications coming to the PC. With over 100 million RTX PCs in the installed-base and over 500 AI-enabled PC applications and games, we are on our way. Moving to Pro Visualization. Revenue of $463 million was up 11% sequentially and up 105% year on year. Fiscal year revenue of $1.55 billion was up 1%. Sequential growth in the quarter was driven by a rich mix of RTX Ada architecture GPUs continuing to ramp. Enterprises are refreshing their workstations to support generative AI-related workloads, such as data preparation, LLM fine-tuning and retrieval augmented generation. These key industrial verticals driving demand include manufacturing, automotive and robotics. The automotive industry has also been an early adopter of NVIDIA Omniverse as it seeks to digitize work flows from design to build, simulate, operate and experience their factories and cars. At CES, we announced that creative partners and developers including Brickland, WPP and ZeroLight are building Omniverse-powered car configurators. Leading automakers like LOTUS are adopting the technology to bring new levels of personalization, realism and interactivity to the car buying experience. Moving to Automotive. Revenue was $281 million, up 8% sequentially and down 4% year on year. Fiscal year revenue of $1.09 billion was up 21%, crossing the $1 billion mark for the first time on continued adoption of the NVIDIA DRIVE platform by automakers. NVIDIA DRIVE Orin is the AI car computer of choice for software-defined AV fleets. Its successor, NVIDIA DRIVE Thor, designed for vision transformers often -- offers more |
5,609 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | choice for software-defined AV fleets. Its successor, NVIDIA DRIVE Thor, designed for vision transformers often -- offers more AI performance and integrates a wide range of intelligent capabilities into a single AI compute platform, including autonomous driving and parking, driver and passenger monitoring and AI cockpit functionality and will be available next year. There were several automotive customer announcements this quarter, Li Auto, Great Wall Motor, ZEEKR, the premium EV subsidiary of Geely and Jeremy Xiaomi EV all announced new vehicles built on NVIDIA. Moving to the rest of the P&L. GAAP gross margins expanded sequentially to 76% and non-GAAP gross margins to 76.7% on strong data center growth and mix. Our gross margins in Q4 benefited from favorable component costs. Sequentially, GAAP operating expenses were up 6% and non-GAAP operating expenses were up 9%, primarily reflecting higher compute and infrastructure investments and employee growth. In Q4, we returned $2.8 billion to shareholders in the form of share repurchases and cash dividends. During fiscal year '24, we utilized cash of $9.9 billion towards shareholder returns, including $9.5 billion in share repurchases. Let me turn to the outlook for the first quarter. Total revenue is expected to be $24 billion, plus or minus 2%. We expect sequential growth in data center and proviz, partially offset by seasonal decline in gaming. GAAP and non-GAAP gross margins are expected to be 76.3% and 77% respectively, plus or minus 50 basis-points. Similar to Q4, Q1 gross margins are benefiting from favorable component costs. Beyond Q1, for the remainder of the year, we expect gross margins to return to the mid-70s percent range. GAAP and non-GAAP operating expenses are expected to be approximately $3.5 billion and $2.5 billion respectively. Fiscal year 2025 GAAP and non-GAAP operating expenses are expected to grow in the mid-30% range as we continue to invest in the large opportunities ahead of us. GAAP and non-GAAP other income and expenses are expected to |
5,610 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | as we continue to invest in the large opportunities ahead of us. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $250 million, excluding gains and losses from non-affiliated investments. 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. In closing, let me highlight some upcoming events for the financial community. We will attend the Morgan Stanley Technology and Media and Telecom Conference in San Francisco on March 4 and the TD Cowen's 44th Annual Healthcare Conference in Boston on March 5. And of course, please join us for our Annual DTC conference starting Monday March 18 in San Jose, California, to be held in-person for the first time in five years. DTC will kick off with Jen-Hsun's keynote and we will host a Q&A session for financial analysts the next day, March 19. At this time, we will now open the call for questions. Operator, would you please poll for questions? |
5,611 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: [Operator Instructions] Your first question comes from the line of Toshiya Hari from Goldman Sachs. Your line is open.
Toshiya Hari: Hi. Thank you so much for taking the question and congratulations on the really strong results. My question is for Jen-Hsun on the data center business. Clearly, you're doing extremely well in the business. I'm curious how your expectations for calendar '24 and '25 have evolved over the past 90 days. And as you answer the question, I was hoping you can touch on some of the newer buckets within data center, things like software. Sovereign AI, I think you've been pretty vocal about how to think about that medium-to-long term. And recently, there was an article about NVIDIA potentially participating in the ASIC market. Is there any credence to that, and if so, how should we think about you guys playing in that market over the next several years? Thank you.
Jensen Huang: Thanks, Toshiya. Let's see. There were three questions, one more time. First question was -- can you -- well?
Toshiya Hari: I guess your expectations for data center, how they've evolved. Thank you. |
5,612 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Okay. Yeah. Well, we guide one quarter at a time. But fundamentally, the conditions are excellent for continued growth calendar '24, to calendar '25 and beyond. And let me tell you why? We're at the beginning of two industry-wide transitions and both of them are industry wide. The first one is a transition from general to accelerated computing. General-purpose computing, as you know, is starting to run out of steam. And you can tell by the CSPs extending and many data centers, including our own for general-purpose computing, extending the depreciation from four to six years. There's just no reason to update with more CPUs when you can't fundamentally and dramatically enhance its throughput like you used to. And so you have to accelerate everything. This is what NVIDIA has been pioneering for some time. And with accelerated computing, you can dramatically improve your energy efficiency. You can dramatically improve your cost in data processing by 20 to 1. Huge numbers. And of course, the speed. That speed is so incredible that we enabled a second industry-wide transition called generative AI. Generative AI, I'm sure we're going to talk plenty -- plenty about it during the call. But remember, generative AI is a new application. It is enabling a new way of doing software, new types of software are being created. It is a new way of computing. You can't do generative AI on traditional general-purpose computing. You have to accelerate it. And the third is it is enabling a whole new industry, and this is something worthwhile to take a step back and look at and it connects to your last question about sovereign AI. A whole new industry in the sense that for the very first time a data center is not just about computing data and storing data and serving the employees of a company. We now have a new type of data center that is about AI generation, an AI generation factory. And you've heard me describe it as AI factories. But basically, it takes raw material, which is data, it transforms it with these AI |
5,613 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | heard me describe it as AI factories. But basically, it takes raw material, which is data, it transforms it with these AI supercomputers that NVIDIA builds, and it turns them into incredibly valuable tokens. These tokens are what people experience on the amazing ChatGPT or Midjourney or, search these days are augmented by that. All of your recommender systems are now augmented by that, the hyper-personalization that goes along with it. All of these incredible startups in digital biology, generating proteins and generating chemicals and the list goes on. And so all of these tokens are generated in a very specialized type of data center. And this data center we call AI supercomputers and AI generation factories. But we're seeing diversity -- one of the other reasons -- so at the foundation is that. The way it manifests into new markets is in all of the diversity that you're seeing us in. One, the amount of inference that we do is just off the charts now. Almost every single time you interact with ChatGPT, that we're inferencing. Every time you use Midjourney, we're inferencing. Every time you see amazing -- these Sora videos that are being generated or Runway, the videos that they're editing, Firefly, NVIDIA is doing inferencing. The inference part of our business has grown tremendously. We estimate about 40%. The amount of training is continuing, because these models are getting larger and larger, the amount of inference is increasing. But we're also diversifying into new industries. The large CSPs are still continuing to build out. You can see from their CapEx and their discussions, but there's a whole new category called GPU specialized CSPs. They specialize in NVIDIA AI infrastructure, GPU specialized CSPs. You're seeing enterprise software platforms deploying AI. ServiceNow is just a really, really great example. You see Adobe. There's the others, SAP and others. You see consumer Internet services that are now augmenting all of their services of the past with generative AI. So they can have even more |
5,614 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Internet services that are now augmenting all of their services of the past with generative AI. So they can have even more hyper-personalized content to be created. You see us talking about industrial generative AI. Now our industries represent multi-billion dollar businesses, auto, health, financial services. In total, our vertical industries are multi-billion dollar businesses now. And of course sovereign AI. The reason for sovereign AI has to do with the fact that the language, the knowledge, the history, the culture of each region are different and they own their own data. They would like to use their data, train it with to create their own digital intelligence and provision it to harness that raw material themselves. It belongs to them, each one of the regions around the world. The data belongs to them. The data is most useful to their society. And so they want to protect the data. They want to transform it themselves, value-added transformation, into AI and provision those services themselves. So we're seeing sovereign AI infrastructure is being built in Japan, in Canada, in France, so many other regions. And so my expectation is that what is being experienced here in the United States, in the West, will surely be replicated around the world, and these AI generation factories are going to be in every industry, every company, every region. And so I think the last -- this last year, we've seen a generative AI really becoming a whole new application space, a whole new way of doing computing, a whole new industry is being formed and that's driving our growth. |
5,615 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Joe Moore from Morgan Stanley. Your line is open.
Joe Moore: Great. Thank you. I wanted to follow up on the 40% of revenues coming from inference. That's a bigger number than I expected. Can you give us some sense of where that number was maybe a year before, how much you're seeing growth around LLMs from inference? And how are you measuring that? Is that -- I assume it's in some cases the same GPUs you use for training and inference. How solid is that measurement? Thank you. |
5,616 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: I'll go backwards. The estimate is probably understated. And -- but we estimated it. And let me tell you why. Whenever -- a year ago, the recommender systems that people are -- when you run the internet, the news, the videos, the music, the products that are being recommended to you because as you know, the internet has trillions -- I don't know how many trillions, but trillions of things out there and your phone is 3-inches square. And so the ability for them to fit all of that information down to something, such a small real estate, is through a system, an amazing system called recommender systems. These recommender systems used to be all based on CPU approaches. But the recent migration to deep learning and now generative AI has really put these recommender systems now directly into the path of GPU acceleration. It needs GPU acceleration for the embeddings. It needs GPU acceleration for the nearest neighbor search. It needs GPU acceleration for the re-ranking and it needs GPU acceleration to generate the augmented information for you. So GPUs are in every single step of a recommender system now. And as you know, recommender system is the single largest software engine on the planet. Almost every major company in the world has to run these large recommender systems. Whenever you use ChatGPT, it's being inferenced. Whenever you hear about Midjourney and just the number of things that they're generating for consumers, when you when you see Getty, the work that we do with Getty and Firefly from Adobe. These are all generative models. The list goes on. And none of these, as I mentioned, existed a year ago, 100% new.
Operator: Your next question comes from the line of Stacy Rasgon from Bernstein Research. Your line is open. |
5,617 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Stacy Rasgon from Bernstein Research. Your line is open.
Stacy Rasgon: Hi, guys. Thanks for taking my question. I wanted Colette -- I wanted to touch on your comment that you expected the next generation of products -- I assume that meant Blackwell, to be supply constrained. Could you dig into that a little bit, what is the driver of that? Why does that get constrained as Hopper is easing up? And how long do you expect that to be constrained, like do you expect the next generation to be constrained like all the way through calendar '25, like when do those start to ease? |
5,618 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. The first thing is overall, our supply is improving, overall. Our supply chain is just doing an incredible job for us, everything from of course the wafers, the packaging, the memories, all of the power regulators, to transceivers and networking and cables and you name it. The list of components that we ship -- as you know, people think that NVIDIA GPUs is like a chip. But the NVIDIA Hopper GPU has 35,000 parts. It weighs 70 pounds. These things are really complicated things we've built. People call it an AI supercomputer for good reason. If you ever look in the back of the data center, the systems, the cabling system is mind boggling. It is the most dense complex cabling system for networking the world's ever seen. Our InfiniBand business grew 5x year over year. The supply chain is really doing fantastic supporting us. And so overall, the supply is improving. We expect the demand will continue to be stronger than our supply provides and -- through the year and we'll do our best. The cycle times are improving and we're going to continue to do our best. However, whenever we have new products, as you know, it ramps from zero to a very large number. And you can't do that overnight. Everything is ramped up. It doesn't step up. And so whenever we have a new generation of products -- and right now, we are ramping H200's. There is no way we can reasonably keep up on demand in the short term as we ramp. We're ramping Spectrum-X. We're doing incredibly well with Spectrum-X. It's our brand-new product into the world of ethernet. InfiniBand is the standard for AI-dedicated systems. Ethernet with Spectrum-X --ethernet is just not a very good scale-out system. But with Spectrum-X, we've augmented, layered on top of ethernet, fundamental new capabilities like adaptive routing, congestion control, noise isolation or traffic isolation, so that we could optimize ethernet for AI. And so InfiniBand will be our AI-dedicated infrastructure. Spectrum-X will be our AI-optimized networking and that is ramping, and |
5,619 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | so InfiniBand will be our AI-dedicated infrastructure. Spectrum-X will be our AI-optimized networking and that is ramping, and so we'll -- with all of the new products, demand is greater than supply. And that's just kind of the nature of new products and so we work as fast as we can to capture the demand. But overall, overall net-net, overall, our supply is increasing very nicely. |
5,620 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Matt Ramsay from TD Cowen. Your line is open.
Matt Ramsay: Good afternoon, Jensen, Colette. Congrats on the results. I wanted to ask I guess a two-part question, and it comes at what Stacy was just getting out on your demand being significantly more than your supply, even though supply is improving. And I guess the two sides of the question are, I guess, first for Colette, like how are you guys thinking about allocation of product in terms of customer readiness to deploy and sort of monitoring if there's any kind of build-up of product that might not yet be turned on? And then I guess Jen-Hsun, for you, I'd be really interested to hear you speak a bit about the thought that you and your company are putting into the allocation of your product across customers, many of which compete with each other, across industries to smaller startup companies, to things in the healthcare arena to government. It's a very, very unique technology that you're enabling and I'd be really interested to hear you speak a bit about how you think about quote/unquote fairly allocating sort of for the good of your company, but also for the good of the industry. Thanks. |
5,621 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Let me first start with your question, thanks, about how we are working with our customers as they look into how they are building out their GPU instances and our allocation process. The folks that we work with, our customers that we work with, have been partners with us for many years as we have been assisting them both in what they set up in the cloud, as well as what they are setting up internally. Many of these providers have multiple products going at one time to serve so many different needs across their end customers but also what they need internally. So they are working in advance, of course, thinking about those new clusters that they will need. And our discussions with them continue not only on our Hopper architecture, but helping them understand the next wave and getting their interest and getting their outlook for the demand that they want. So it's always a moving process in terms of what they will purchase, what is still being built and what is in use for our end customers. But the relationships that we've built and their understanding of the sophistication of the build has really helped us with that allocation and both helped us with our communications with them. |
5,622 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: First, our CSPs have a very clear view of our product road map and transitions. And that transparency with our CSPs gives them the confidence of which products to place and where and when. And so they know their -- they know the timing to the best of our ability. And they know quantities and of course allocation. We allocate fairly. We allocate fairly. We do the best of our -- do the best we can to allocate fairly and to avoid allocating unnecessarily. As you mentioned earlier, why allocate something when the data center's not ready. Nothing is more difficult then to have anything sit around. And so, allocate fairly, and to avoid allocating unnecessarily. And where we do -- the question that you asked about the end markets, that we have an excellent ecosystem with OEMs, ODMs, CSPs and, very importantly, end markets. What NVIDIA is really unique about is that we bring our customers, we bring our partners, CSPs and OEMs, we bring them customers. The biology companies, the healthcare companies, financial services companies, AI developers, large-language model developers, autonomous vehicle companies, robotics companies. There's just a giant suite of robotics companies that are emerging. There are warehouse robotics to surgical robotics to humanoid robotics, all kinds of really interesting robotics companies, agriculture robotics companies. All of these startups, large companies, healthcare, financial services and auto and such are working on NVIDIA's platform. We support them directly. And oftentimes, we can have a twofer by allocating to a CSP and bringing the customer to the CSP at the same time. And so this ecosystem, you're absolutely right that it's vibrant. But at the core of it, we want to allocate fairly with avoiding waste and looking for opportunities to connect partners and end users. We're looking for those opportunities all the time.
Operator: Your next question comes from the line of Timothy Arcuri from UBS. Your line is open. |
5,623 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Timothy Arcuri from UBS. Your line is open.
Timothy Arcuri: Thanks a lot. I wanted to ask about how you're converting backlog into revenue. Obviously, lead times for your products have come down quite a bit. Colette, you didn't talk about the inventory purchase commitments. But if I sort of add up your inventory plus the purchase commits and your prepaid supply, sort of the aggregate of your supply, it was actually down a touch. How should we read that? Is that just you saying that you don't need to make as much of a financial commitment to your suppliers because the lead times are lower or is that maybe you're reaching some sort of steady state where you're closer to filling your order book and your backlog? Thanks.
Colette Kress: Yeah. So let me, highlight on those three different areas of how we look at our suppliers. You're correct. Our inventory on hand given our allocation that we're on, we're trying to, as things come into inventory, immediately work to ship them to our customers. I think our customer appreciates our ability to meet the schedules that we've looked for. The second piece of it is our purchase commitments. Our purchase commitments have many different components into it, components that we need for manufacturing. But also, often we are procuring capacity that we may need. The length of that need for capacity or the length for the components are all different. Some of them may be for the next two quarters, but some of them may be for multiple years. I can say the same regarding our prepaids. Our prepaids are pre-designed to make sure that we have the reserve capacity that we need at several of our manufacturing suppliers as we look forward. So wouldn't read into anything regarding approximately about the same numbers as we are increasing our supply. All of them just have different lengths as we have sometimes had to buy things in long-lead times or things that needed capacity to be built for us. |
5,624 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Ben Reitzes from Melius Research. Your line is open.
Ben Reitzes: Yeah. Thanks. Congratulations on the results. Colette, I wanted to talk about your comment regarding gross margins and that they should go back to the mid-70s. If you don't mind unpacking that. And also, is that due to the HBM content in the new products and what do you think are the drivers of that comment? Thanks so much.
Colette Kress: Yeah. Thanks for the question. We highlighted in our opening remarks really about our Q4 results and our outlook for Q1. Both of those quarters are unique. Those two quarters are unique in their gross margin as they include some benefit from favorable component cost in the supply chain kind of across both our compute and networking and also in several different stages of our manufacturing process. So looking forward, we have visibility into a mid-70s gross margin for the rest of the fiscal year, taking us back to where we were before this Q4 and Q1 peak that we've had here. So we're really looking at just a balance of our mix. Mix is always going to be our largest driver of what we will be shipping for the rest of the year. And those are really just the drivers.
Operator: Your next question comes from the line of C.J. Muse from Cantor Fitzgerald. Your line is open.
C.J. Muse: Yeah. Good afternoon, and thank you for taking the question. Bigger picture question for you, Jen-Hsun. When you think about the million-x improvement in GPU compute over the last decade and expectations for similar improvements in the next, how do your customers think about the long-term usability of their NVIDIA investments that they're making today? Do today's training clusters become tomorrow's inference clusters? How do you see this playing out? Thank you. |
5,625 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Hey, CJ. Thanks for the question. Yeah, that's the really cool part. If you look at the reason why we're able to improve performance so much, it's because we have two characteristics about our platform. One, is that it's accelerated. And two, it's programmable. It's not brittle. NVIDIA is the only architecture that has gone from the very, very beginning, literally the very beginning when CNN's and Alex Krizhevsky and Ilya Sutskever and Geoff Hinton first revealed AlexNet, all the way through to RNNs to LSTMs to every -- RLs to deep learning RLs to transformers to every single version. Every single version and every species that have come along, vision transformers, multi-modality transformers, every single -- and now time sequence stuff, and every single variation, every single species of AI that has come along, we've been able to support it, optimize our stack for it and deploy it into our installed base. This is really the great amazing part. On the one hand, we can invent new architectures and new technologies like our Tensor cores, like our transformer engine for Tensor cores, improved new numerical formats and structures of processing like we've done with the different generations of Tensor cores, meanwhile, supporting the installed base at the same time. And so, as a result, we take all of our new software algorithm invest -- inventions, all of the inventions, new inventions of models of the industry, and it runs on our installed base on the one hand. On the other hand, whenever we see something revolutionary we can -- like transformers, we can create something brand new like the Hopper transformer engine and implement it into future. And so we simultaneously have this ability to bring software to the installed base and keep making it better and better and better, so our customers installed base is enriched over time with our new software. On the other hand, for new technologies, create revolutionary capabilities. Don't be surprised if in our future generation, all of a sudden amazing |
5,626 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | new technologies, create revolutionary capabilities. Don't be surprised if in our future generation, all of a sudden amazing breakthroughs in large-language models were made possible And those breakthroughs, some of which will be in software because they run CUDA, will be made available to the installed base. And so we carry everybody with us on the one hand. We make giant breakthroughs on the other hand. |
5,627 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Aaron Rakers from Wells Fargo. Your line is open.
Aaron Rakers: Yeah. Thanks for taking the question. I wanted to ask about the China business. I know that in your prepared comments you said that you started shipping some alternative solutions into China. You also put it out that you expect that contribution to continue to be about a mid-single digit percent of your total data center business. So I guess the question is what is the extent of products that you're shipping today into the China market and why should we not expect that maybe other alternative solutions come to the market and expand your breadth to participate in that in that opportunity again? Thank you.
Jensen Huang: Think of, at the core, remember the US government wants to limit the latest capabilities of NVIDIA's accelerated computing and AI to the Chinese market. And the U.S. government would like to see us be as successful in China as possible. Within those two constraints, within those two pillars if you will, are the restrictions, and so we had to pause when the new restrictions came out. We immediately paused. So that we understood what the restrictions are, reconfigured our products in a way that is not software hackable in any way. And that took some time. And so we reset -- we reset our product offering to China and now we're sampling to customers in China. And we're going to do our best to compete in that marketplace and succeed in that marketplace within the -- within the specifications of the restriction. And so that's it. We -- this last quarter, we -- our business significantly declined as we -- as we paused in the marketplace. We stopped shipping in the marketplace. We expect this quarter to be about the same. But after, that hopefully we can go compete for our business and do our best, and we'll see how it turns out.
Operator: Your next question comes from the line of Harsh Kumar from Piper Sandler. Your line is open. |
5,628 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Harsh Kumar from Piper Sandler. Your line is open.
Harsh Kumar: Yeah. Hey, Jen-Hsun, Colette and NVIDIA team. First of all, congratulations on a stunning quarter and guide. I wanted to talk about, a little bit about your software business and it's pleasing to hear that it's over a $1 billion but I was hoping Jen-Hsun or Colette if you could just help us understand what the different parts and pieces are for the software business? In other words, just help us unpack it a little bit, so we can get a better understanding of where that growth is coming from. |
5,629 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Let me take a step back and explain the fundamental reason why NVIDIA will be very successful in software. So first, as you know, accelerated computing really grew in the cloud. In the cloud, the cloud service providers have really large engineering teams and we work with them in a way that allows them to operate and manage their own business. And whenever there are any issues, we have large teams assigned to them. And their engineering teams are working directly with our engineering teams and we enhance, we fix, we maintain, we patch the complicated stack of software that's involved in accelerated computing. As you know, accelerated computing is very different than general-purpose computing. You're not starting from a program like C++. You compile it and things run on all your CPUs. The stacks of software necessary for every domain from data processing SQL versus -- SQL structure data versus all the images and text and PDF, which is unstructured, to classical machine-learning to computer vision to speech to large-language models, all --recommender systems. All of these things require different software stacks. That's the reason why NVIDIA has hundreds of libraries. If you don't have software, you can't open new markets. If you don't have software, you can't open and enable new applications. Software is fundamentally necessary for accelerated computing. This is the fundamental difference between accelerated computing and general-purpose computing that most people took a long time to understand. And now, people understand that the software is really key. And the way that we work with CSPs, that's really easy. We have large teams that are working with their large teams. However, now that generative AI is enabling every enterprise and every enterprise software company to embrace accelerated computing -- and when -- it is now essential to embrace accelerated computing because it is no longer possible, no longer likely anyhow to sustain improved throughput through just general-purpose computing. All of |
5,630 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | it is no longer possible, no longer likely anyhow to sustain improved throughput through just general-purpose computing. All of these enterprise software companies and enterprise companies don't have large engineering teams to be able to maintain and optimize their software stack to run across all of the world's clouds and private clouds and on-prem. So we are going to do the management, the optimization, the patching, the tuning, the installed-base optimization for all of their software stacks. And we containerize them into our stack. We call it NVIDIA AI Enterprise. And the way we go to market with it is that think of that NVIDIA AI Enterprise now as a run time like an operating system, it's an operating system for artificial intelligence. And we charge $4,500 per GPU per year. And my guess is that every enterprise in the world, every software enterprise company that are deploying software in all the clouds and private clouds and on-prem, will run on NVIDIA AI Enterprise, especially obviously for our GPUs. And so this is going to likely be a very significant business over time. We're off to a great start. And Colette mentioned that it's already at $1 billion run rate and we're really just getting started. |
5,631 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Thank you. I will now turn the call back over to Jen-Hsun Huang, CEO, for closing remarks. |
5,632 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: The computer industry is making two simultaneous platform shifts at the same time. The trillion-dollar installed base of data centers is transitioning from general purpose to accelerated computing. Every data center will be accelerated so the world can keep up with the computing demand, with increasing throughput, while managing costs and energy. The incredible speed up of NVIDIA enabled -- that NVIDIA enabled, a whole new computing paradigm, generative AI, where software can learn, understand and generate any information from human language to the structure of biology and the 3D world. We are now at the beginning of a new industry where AI-dedicated data centers process massive raw data to refine it into digital intelligence. Like AC power generation plants of the last industrial revolution, NVIDIA AI supercomputers are essentially AI generation factories of this Industrial Revolution. Every company in every industry is fundamentally built on their proprietary business intelligence, and in the future, their proprietary generative AI. Generative AI has kicked off a whole new investment cycle to build the next trillion dollars of infrastructure of AI generation factories. We believe these two trends will drive a doubling of the world's data center infrastructure installed base in the next five years and will represent an annual market opportunity in the hundreds of billions. This new AI infrastructure will open up a whole new world of applications not possible today. We started the AI journey with the hyperscale cloud providers and consumer internet companies. And now, every industry is on board, from automotive to healthcare to financial services, to industrial to telecom, media and entertainment. NVIDIA's full stack computing platform with industry-specific applications frameworks and a huge developer and partner ecosystem, gives us the speed, scale and reach to help every company -- to help companies in every industry become an AI company. We have so much to share with you at next month's GTC in |
5,633 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | company -- to help companies in every industry become an AI company. We have so much to share with you at next month's GTC in San Jose. So be sure to join us. We look forward to updating you on our progress next quarter. |
5,634 | NVDA | 4 | 2,024 | 2024-02-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: This concludes today's conference call. You may now disconnect. |
5,635 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Good afternoon. My name is JL, and I will 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] Simona Jankowski, you may now begin your conference.
Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third 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 fourth quarter and 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 statements are made as of today, November 21, 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. With that, let me turn the call over to Colette. |
5,636 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Thanks, Simona. Q3 was another record quarter. Revenue of $18.1 billion was up 34% sequentially and up more than 200% year-on-year and well above our outlook for $16 billion. Starting with Data Center. The continued ramp of the NVIDIA HGX platform based on our Hopper Tensor Core GPU architecture, along with InfiniBand end-to-end networking, drove record revenue of $14.5 billion, up 41% sequentially and up 279% year-on-year. NVIDIA HGX with InfiniBand together are essentially the reference architecture for AI supercomputers and data center infrastructures. Some of the most exciting generative AI applications are built and run on NVIDIA, including Adobe Firefly, ChatGPT, Microsoft 365 Copilot, CoAssist, now assist with ServiceNow and Zoom AI Companion. Our Data Center compute revenue quadrupled from last year and networking revenue nearly tripled. Investments in infrastructure for training and inferencing large language models, deep learning, recommender systems and generative AI applications is fueling strong broad-based demand for NVIDIA accelerated computing. Inferencing is now a major workload for NVIDIA AI computing. Consumer Internet companies and enterprises drove exceptional sequential growth in Q3, comprising approximately half of our Data Center revenue and outpacing total growth. Companies like Meta are in full production with deep learning, recommender systems and also investing in generative AI to help advertisers optimize images and text. Most major consumer Internet companies are racing to ramp up generative AI deployment. The enterprise wave of AI adoption is now beginning. Enterprise software companies such as Adobe, Databricks, Snowflake and ServiceNow are adding AI copilots and the systems to their platforms. And broader enterprises are developing custom AI for vertical industry applications such as Tesla in autonomous driving. Cloud service providers drove roughly the other half of our Data Center revenue in the quarter. Demand was strong from all hyperscale CSPs, as well as from |
5,637 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | roughly the other half of our Data Center revenue in the quarter. Demand was strong from all hyperscale CSPs, as well as from a broadening set of GPU-specialized CSPs globally that are rapidly growing to address the new market opportunities in AI. NVIDIA H100 Tensor Core GPU instances are now generally available in virtually every cloud with instances in high demand. We have significantly increased supply every quarter this year to meet strong demand and expect to continue to do so next year. We will also have a broader and faster product launch cadence to meet the growing and diverse set of AI opportunities. Towards the end of the quarter, the U.S. government announced a new set of export control regulations for China and other markets, including Vietnam and certain countries in the Middle East. These regulations require licenses for the export of a number of our products, including our Hopper and Ampere 100 and 800 series and several others. Our sales to China and other affected destinations derived from products that are now subject to licensing requirements have consistently contributed approximately 20% to 25% of Data Center revenue over the past few quarters. We expect that our sales to these destinations will decline significantly in the fourth quarter. So we believe will be more than offset by strong growth in other regions. The U.S. government designed the regulation to allow the U.S. industry to provide data center compute products to markets worldwide, including China. Continuing to compete worldwide as the regulations encourage, promotes U.S. technology leadership, spurs economic growth and supports U.S. jobs. For the highest performance levels, the government requires licenses. For lower performance levels, the government requires a streamlined prior notification process. And for products even lower performance levels, the government does not require any notice at all. Following the government's clear guidelines, we are working to expand our Data Center product portfolio to offer compliance |
5,638 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Following the government's clear guidelines, we are working to expand our Data Center product portfolio to offer compliance solutions for each regulatory category, including products for which the U.S. government does not wish to have advance notice before each shipment. We are working with some customers in China and the Middle East to pursue licenses from the U.S. government. It is too early to know whether these will be granted for any significant amount of revenue. Many countries are awakening to the need to invest in sovereign AI infrastructure to support economic growth and industrial innovation. With investments in domestic compute capacity, nations can use their own data to train LLMs and support their local generative AI ecosystems. For example, we are working with India's government and largest tech companies including Infosys, Reliance and Tata to boost their sovereign AI infrastructure. And French private cloud provider, Scaleway, is building a regional AI cloud based on NVIDIA H100 InfiniBand and NVIDIA's AI Enterprise software to fuel advancement across France and Europe. National investment in compute capacity is a new economic imperative and serving the sovereign AI infrastructure market represents a multi-billion dollar opportunity over the next few years. From a product perspective, the vast majority of revenue in Q3 was driven by the NVIDIA HGX platform based on our Hopper GPU architecture with lower contribution from the prior generation Ampere GPU architecture. The new L40S GPU built for industry standard servers began to ship, supporting training and inference workloads across a variety of consumers. This was also the first revenue quarter of our GH200 Grace Hopper Superchip, which combines our ARM-based Grace CPU with a Hopper GPU. Grace and Grace Hopper are ramping into a new multi-billion dollar product line. Grace Hopper instances are now available at GPU specialized cloud providers, and coming soon to Oracle Cloud. Grace Hopper is also getting significant traction with supercomputing |
5,639 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | cloud providers, and coming soon to Oracle Cloud. Grace Hopper is also getting significant traction with supercomputing customers. Initial shipments to Los Alamos National Lab and the Swiss National Supercomputing Center took place in the third quarter. The UK government announced it will build one of the world's fastest AI supercomputers called Isambard-AI with almost 5,500 Grace Hopper Superchips. German supercomputing center, Julich, also announced that it will build its next-generation AI supercomputer with close to 24,000 Grace Hopper Superchips and Quantum-2 InfiniBand, making it the world's most powerful AI supercomputer with over 90 exaflops of AI performance. All-in, we estimate that the combined AI compute capacity of all the supercomputers built on Grace Hopper across the U.S., Europe and Japan next year will exceed 200 exaflops with more wins to come. Inference is contributing significantly to our data center demand, as AI is now in full production for deep learning, recommenders, chatbots, copilots and text to image generation and this is just the beginning. NVIDIA AI offers the best inference performance and versatility, and thus the lower power and cost of ownership. We are also driving a fast cost reduction curve. With the release of TensorRT-LLM, we now achieved more than 2x the inference performance for half the cost of inferencing LLMs on NVIDIA GPUs. We also announced the latest member of the Hopper family, the H200, which will be the first GPU to offer HBM3e, faster, larger memory to further accelerate generative AI and LLMs. It moves inference speed up to another 2x compared to H100 GPUs for running LLMs like Norma2 (ph). Combined, TensorRT-LLM and H200, increased performance or reduced cost by 4x in just one year. With our customers changing their stack, this is a benefit of CUDA and our architecture compatibility. Compared to the A100, H200 delivers an 18x performance increase for inferencing models like GPT-3, allowing customers to move to larger models and with no increase in latency. |
5,640 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | increase for inferencing models like GPT-3, allowing customers to move to larger models and with no increase in latency. Amazon Web Services, Google Cloud, Microsoft Azure and Oracle Cloud will be among the first CSPs to offer H200-based instances starting next year. At last week's Microsoft Ignite, we deepened and expanded our collaboration with Microsoft across the entire stock. We introduced an AI foundry service for the development and tuning of custom generative AI enterprise applications running on Azure. Customers can bring their domain knowledge and proprietary data and we help them build their AI models using our AI expertise and software stock in our DGX cloud, all with enterprise grade security and support. SAP and Amdocs are the first customers of the NVIDIA AI foundry service on Microsoft Azure. In addition, Microsoft will launch new confidential computing instances based on the H100. The H100 remains the top performing and most versatile platform for AI training and by a wide margin, as shown in the latest MLPerf industry benchmark results. Our training cluster included more than 10,000 H100 GPUs or 3x more than in June, reflecting very efficient scaling. Efficient scaling is a key requirement in generative AI, because LLMs are growing by an order of magnitude every year. Microsoft Azure achieved similar results on a nearly identical cluster, demonstrating the efficiency of NVIDIA AI in public cloud deployments. Networking now exceeds a $10 billion annualized revenue run rate. Strong growth was driven by exceptional demand for InfiniBand, which grew fivefold year-on-year. InfiniBand is critical to gaining the scale and performance needed for training LLMs. Microsoft made this very point last week, highlighting that Azure uses over 29,000 miles of InfiniBand cabling, enough to circle the globe. We are expanding NVIDIA networking into the Ethernet space. Our new Spectrum-X end-to-end Ethernet offering with technologies, purpose built for AI, will be available in Q1 next year. With support from |
5,641 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | end-to-end Ethernet offering with technologies, purpose built for AI, will be available in Q1 next year. With support from leading OEMs, including Dell, HPE and Lenovo. Spectrum-X can achieve 1.6x higher networking performance for AI communication compared to traditional Ethernet offerings. Let me also provide an update on our software and services offerings, where we are starting to see excellent adoption. We are on track to exit the year at an annualized revenue run rate of $1 billion for our recurring software, support and services offerings. We see two primary opportunities for growth over the intermediate term with our DGX cloud service and with our NVIDIA AI Enterprise software, each reflects the growth of enterprise AI training and enterprise AI inference, respectively. Our latest DGX cloud customer announcement was this morning as part of an AI research collaboration with Gentech, the biotechnology pioneer also plans to use our BioNeMo LLM framework to help accelerate and optimize their AI drug discovery platform. We now have enterprise AI partnership with Adobe, Dropbox, Getty, SAP, ServiceNow, Snowflake and others to come. Okay. Moving to Gaming. Gaming revenue of $2.86 billion was up 15% sequentially and up more than 80% year-on-year with strong demand in the important back-to-school shopping season with NVIDIA RTX ray tracing and AI technology now available at price points as low as $299. We entered the holidays with the best-ever line-up for gamers and creators. Gaming has doubled relative to pre-COVID levels even against the backdrop of lackluster PC market performance. This reflects the significant value we've brought to the gaming ecosystem with innovations like RTX and DLSS. The number of games and applications supporting these technologies has exploded in that period, driving upgrades and attracting new buyers. The RTX ecosystem continues to grow. There are now over 475 RTX-enabled games and applications. Generative AI is quickly emerging as the new pillar app for high performance PCs. NVIDIA |
5,642 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | RTX-enabled games and applications. Generative AI is quickly emerging as the new pillar app for high performance PCs. NVIDIA RTX GPUs to find the most performance AI PCs and workstations. We just released TensorRT-LLM for Windows, which speeds on-device LLM inference up by 4x. With an installed base of over 100 million, NVIDIA RTX is the natural platform for AI application developers. Finally, our GeForce NOW cloud gaming service continues to build momentum. Its library of PC games surpassed 1,700 titles, including the launches of Alan Wake 2, Baldur's Gate 3, Cyberpunk 2077: Phantom Liberty and Starfield. Moving to the Pro Vis. Revenue of $416 million was up 10% sequentially and up 108% year-on year. NVIDIA RTX is the workstation platform of choice for professional design, engineering and simulation use cases and AI is emerging as a powerful demand driver. Early applications include inference for AI imaging in healthcare and edge AI in smart spaces and the public sector. We launched a new line of desktop workstations based on NVIDIA RTX Ada Lovelace generation GPUs and ConnectX, SmartNICs offering up to 2x the AI processing ray tracing and graphics performance of the previous generations. These powerful new workstations are optimized for AI workloads such as fine tune AI models, training smaller models and running inference locally. We continue to make progress on Omniverse, our software platform for designing, building and operating 3D virtual worlds. Mercedes-Benz is using Omniverse powered digital twins to plan, design, build and operate its manufacturing and assembly facilities, helping it increase efficiency and reduce defects. Oxxon (ph) is also incorporating Omniverse into its manufacturing process, including end-to-end simulation for the entire robotics and automation pipeline, saving time and cost. We announced two new Omniverse Cloud services for automotive digitalization available on Microsoft Azure, a virtual factory simulation engine and autonomous vehicle simulation engine. Moving to Automotive. |
5,643 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | on Microsoft Azure, a virtual factory simulation engine and autonomous vehicle simulation engine. Moving to Automotive. Revenue was $261 million, up 3% sequentially and up 4% year-on year, primarily driven by continued growth in self-driving platforms based on NVIDIA DRIVE Orin SOC and the ramp of AI cockpit solutions with global OEM customers. We extended our automotive partnership of Foxconn to include NVIDIA DRIVE for our next-generation automotive SOC. Foxconn has become the ODM for EVs. Our partnership provides Foxconn with a standard AV sensor and computing platform for their customers to easily build a state-of-an-art safe and secure software defined car. Now we're going to move to the rest of the P&L. GAAP gross margin expanded to 74% and non-GAAP gross margin to 75%, driven by higher Data Center sales and lower net inventory reserve, including a 1 percentage point benefit from the release of previously reserved inventory related to the Ampere GPU architecture products. Sequentially, GAAP operating expenses were up 12% and non-GAAP operating expenses were up 10%, primarily reflecting increased compensation and benefits. Let me turn to the fourth quarter of fiscal 2024. Total revenue is expected to be $20 billion, plus or minus 2%. We expect strong sequential growth to be driven by Data Center, with continued strong demand for both compute and networking. Gaming will likely decline sequentially as it is now more aligned with notebook seasonality. GAAP and non-GAAP gross margins are expected to be 74.5% and 75.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $3.17 billion and $2.2 billion, respectively. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $200 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 15%, plus or minus 1% excluding any discrete items. Further financial information are included in the CFO commentary and other |
5,644 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | 15%, plus or minus 1% excluding any discrete items. Further financial information are included in the CFO commentary and other information available on our IR website. In closing, let me highlight some upcoming events for the financial community. We will attend the UBS Global Technology Conference in Scottsdale, Arizona, on November 28; the Wells Fargo TMT Summit in Rancho Palos Verdes, California on November 29; the Arete Virtual Tech Conference on December 7; and the J.P. Morgan Health Care Conference in San Francisco on January 8. Our earnings call to discuss the results of our fourth quarter and fiscal 2024 is scheduled for Wednesday, February 21. We will now open the call for questions. Operator, will you please poll for questions. |
5,645 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: [Operator Instructions] Your first question comes from the line of Vivek Arya of Bank of America. Your line is open.
Vivek Arya: Thanks for taking my question. Just, Colette, wanted to clarify what China contributions are you expecting in Q4. And then, Jensen, the main question is for you, where do you think we are in the adoption curve in terms of your shipments into the generative AI market? Because when I just look at the trajectory of your data center, is growth -- it will be close to nearly 30% of all the spending in data center next year. So what metrics are you keeping an eye on to inform you that you can continue to grow? Just where are we in the adoption curve of your products into the generative AI market? Thank you.
Colette Kress: So, first let me start with your question, Vivek, on export controls and the impacts that we are seeing in our Q4 outlook and guidance that we provided. We had seen historically over the last several quarters that China and some of the other impacted destinations to be about 20% to 25% of our Data Center revenue. We are expecting in our guidance for that to decrease substantially as we move into Q4. The export controls will have a negative effect on our China business. And we do not have good visibility into the magnitude of that impact even over the long-term. We are though working to expand our Data Center product portfolio to possibly offer new regulation compliance solutions that do not require a license, these products, they may become available in the next coming months. However, we don't expect their contribution to be material or meaningful as a percentage of the revenue in Q4. |
5,646 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Generative AI is the largest TAM expansion of software and hardware that we've seen in several decades. At the core of it, what's really exciting is that, what was largely a retrieval based computing approach, almost everything that you do is retrieved off of storage somewhere, has been augmented now, added with a generative method. And it's changed almost everything. You could see that text-to-text, text-to-image, text-to-video, text-to-3D, text-to-protein, text-to-chemicals, these were things that were processed and typed in by humans in the past. And these are now generative approaches. The way that we access data is changed. It used to be based on explicit queries. It is now based on natural language queries, intention queries, semantic queries. And so, we're excited about the work that we're doing with SAP and Dropbox and many others that you're going to hear about. And one of the areas that is really impactful is the software industry, which is about $1 trillion or so, has been building tools that are manually used over the last couple of decades. And now there's a whole new segment of software called copilots and assistants. Instead of manually used, these tools will have copilots to help you use it. And so, instead of licensing software, we will continue to do that, of course, but we will also hire copilots and assistants to help us use these -- use the software. We'll connect all of these copilots and assistants into teams of AIs, which is going to be the modern version of software, modern version of enterprise business software. And so the transformation of software and the way that software has done is driving the hardware underneath. And you can see that it's transforming hardware in two ways. One is something that's largely independent of generative AI. There's two trends: one is related to accelerated computing, general purpose computing is too wasteful of energy and cost. And now that we have much, much better approaches, call it, accelerated computing, you could save an order of |
5,647 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | energy and cost. And now that we have much, much better approaches, call it, accelerated computing, you could save an order of magnitude of energy, you can save an order of magnitude of time or you can save an order of magnitudes of cost by using acceleration. And so, accelerated computing is transitioning, if you will, general purpose computing into this new approach. And that's been augmented by a new class of data centers. This is the traditional data centers that you were just talking about where we represent about a third of that. But there is a new class of data centers and this new class of data centers, unlike the data centers of the past, where you have a lot of applications running used by a great many people that are different tenants that are using the same infrastructure and that data center stores a lot of files. These new data centers are very few applications, if not one application, used by basically one tenant and it processes data, it trains models and then generates tokens and generates AI. And we call these new data centers AI factories. We're seeing AI factories being built out everywhere, and just by every country. And so if you look at the way where we are in the expansion, the transition into this new computing approach, the first wave you saw with large language model start-ups, generative AI start-ups and consumer Internet companies, and weren't in the process of ramping that. Meanwhile, while that's being ramped, you see that we're starting to partner with enterprise software companies who would like to build chatbots and copilots and assistants to augment the tools that they have on their platforms. You're seeing GPU specialized CSPs cropping up all over the world and they are dedicated to do really one thing, which is processing AI. You're seeing sovereign AI infrastructures, people -- countries that now recognize that they have to utilize their own data, keep their own data, keep their own culture, process that data and develop their own AI. You see that in India. Several -- about |
5,648 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | their own data, keep their own culture, process that data and develop their own AI. You see that in India. Several -- about a year ago in Sweden, you are seeing in Japan. Last week, a big announcement in France. But the number of sovereign AI clouds that are being built is really quite significant. And my guess is that almost every major region will have and surely every major country will have their own AI clouds. And so I think you're seeing just new developments as the generative AI wave propagates through every industry, every company, every region. And so we're at the beginning of this inflection, this computing transition. |
5,649 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Aaron Rakers of Wells Fargo. Your line is open.
Aaron Rakers: Yeah. Thanks for taking the question. I wanted to ask about kind of the networking side of the business. Given the growth rates that you've now cited, I think, it's 155% year-over-year and strong growth sequentially, it looks like that business is like almost approaching $2.5 billion to $3 billion quarterly level. I'm curious of how you see Ethernet involved evolving and maybe how you would characterize your differentiation of Spectrum-X relative to the traditional Ethernet stack as we start to think about that becoming part of the networking narrative above and maybe beyond just InfiniBand as we look into next year? Thank you. |
5,650 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. Thanks for the question. Our networking business is already on a $10 billion plus run rate and it's going to get much larger. And as you mentioned, we added a new networking platform to our networking business recently. The vast majority of the dedicated large scale AI factories standardize on InfiniBand. And the reason for that is not only because of its data rate and not only just the latency, but the way that it moves traffic around the network is really important. The way that you process AI and a multi-tenant hyperscale Ethernet environment, the traffic pattern is just radically different. And with InfiniBand and with software defined networks, we could do congestion control, adaptive routing, performance isolation and noise isolation, not to mention, of course, the day rate and the low latency that -- and a very low overhead of InfiniBand that's natural part of InfiniBand. And so, InfiniBand is not so much just the network, it's also a computing fabric. We've put a lot of software-defined capabilities into the fabric including computation. We will do 40-point calculations and computation right on the switch, and right in the fabric itself. And so that's the reason why that difference in Ethernet versus InfiniBand or InfiniBand versus Ethernet for AI factories is so dramatic. And the difference is profound. And the reason for that is because you've just invested in a $2 billion infrastructure for AI factories. A 20%, 25%, 30% difference in overall effectiveness, especially as you scale up is measured in hundreds of millions of dollars of value. And if you will, renting that infrastructure over the course of four to five years, it really, really adds up. And so InfiniBand's value proposition is undeniable for AI factories. However, as we move AI into enterprise. This is enterprise computing what we'd like to enable every company to be able to build their own custom AIs. We're building customer AIs in our company based on our proprietary data, our proprietary type of skills. For example, |
5,651 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | AIs. We're building customer AIs in our company based on our proprietary data, our proprietary type of skills. For example, recently we spoke about one of the models that we're creating, it's called ChipNeMo; we're building many others. There'll be tens, hundreds of custom AI models that we create inside our company. And our company is -- for all of our employee use, doesn't have to be as high performance as the AI factories we used to train the models. And so we would like the AI to be able to run in Ethernet environment. And so what we've done is we invented this new platform that extends Ethernet; doesn't replace Ethernet, it's 100% compliant with Ethernet. And it's optimized for East-West traffic, which is where the computing fabric is. It adds to Ethernet with an end-to-end solution with Bluefield, as well as our Spectrum switch that allows us to perform some of the capabilities that we have in InfiniBand, not all but some. And we achieved excellent results. And the way we go to market is we go to market with our large enterprise partners who already offer our computing solution. And so, HP, Dell and Lenovo has the NVIDIA AI stack, the NVIDIA AI Enterprise software stack and now they integrate with Bluefield, as well as bundle -- take a market there, Spectrum switch, and they'll be able to offer enterprise customers all over the world with their vast sales force and vast network of resellers a fully integrated, if you will, fully optimized, at least end-to-end AI solution. And so that's basically it, bringing AI to Ethernet for the world's enterprise. |
5,652 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Thank you. Your next question comes from the line of Joe Moore of Morgan Stanley. Your line is open.
Joseph Moore: Great. Thank you. I'm wondering if you could talk a little bit more about Grace Hopper and how you see the ability to leverage kind of the microprocessor, how you see that as a TAM expander. And what applications do you see using Grace Hopper versus more traditional H100 applications? |
5,653 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. Thanks for the question. Grace Hopper is in production -- in high volume production now. We're expecting next year just with all of the design wins that we have in high performance computing and AI infrastructures, we are on a very, very fast ramp with our first data center CPU to a multi-billion dollar product line. This is going to be a very large product line for us. The capability of Grace Hopper is really quite spectacular. It has the ability to create computing nodes that simultaneously has very fast memory, as well as very large memory. In the areas of vector databases or semantic surge, what is called RAG, retrieval augmented generation. So that you could have a generative AI model be able to refer to proprietary data or a factual data before it generates a response, that data is quite large. And you can also have applications or generative models where the context length is very high. You basically store it in entire book into end-to-end system memory before you ask your questions. And so the context length can be quite large this way. The generative models has the ability to still be able to naturally interact with you on one hand. On the other hand, be able to refer to factual data, proprietary data or domain-specific data, you data and be contextually relevant and reduce hallucination. And so that particular use case for example is really quite fantastic for Grace Hopper. It also serves the customers that really care to have a different CPU than x86. Maybe it's a European supercomputing centers or European companies who would like to build up their own ARM ecosystem and like to build up a full stack or CSPs that have decided that they would like to pivot to ARM, because their own custom CPUs are based on ARM. There are variety of different reasons that drives the success of Grace Hopper, but we're off to a just an extraordinary start. This is a home run product.
Operator: Your next question comes from the line of Tim Arcuri of UBS. Your line is open. |
5,654 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Tim Arcuri of UBS. Your line is open.
Tim Arcuri: Hi. Thanks. I wanted to ask a little bit about the visibility that you have on revenue. I know there's a few moving parts. I guess, on one hand, the purchase commitments went up a lot again. But on the other hand, China bans would arguably pull in when you can fill the demand beyond China. So I know we're not even into 2024 yet and it doesn't sound like, Jensen, you think that next year would be a peak in your Data Center revenue, but I just wanted to sort of explicitly ask you that. Do you think that Data Center can grow even in 2025? Thanks. |
5,655 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Absolutely believe the Data Center can grow through 2025. And there are, of course, several reasons for that. We are expanding our supply quite significantly. We have already one of the broadest and largest and most capable supply chain in the world. Now, remember, people think that the GPU is a chip. But the HGX H100, the Hopper HGX has 35,000 parts, it weighs 70 pounds. Eight of the chips are Hopper. The other 35,000 are not. It is -- even its passive components are incredible. High voltage parts. High frequency parts. High current parts. It is a supercomputer, and therefore, the only way to test a supercomputer is with another supercomputer. Even the manufacturing of it is complicated, the testing of it is complicated, the shipping of it complicated and installation is complicated. And so, every aspect of our HGX supply chain is complicated. And the remarkable team that we have here has really scaled out the supply chain incredibly. Not to mention, all of our HGXs are connected with NVIDIA networking. And the networking, the transceivers, the mix, the cables, the switches, the amount of complexity there is just incredible. And so, I'm just -- first of all, I'm just super proud of the team for scaling up this incredible supply chain. We are absolutely world class. But meanwhile, we're adding new customers and new products. So we have new supply. We have new customers, as I was mentioning earlier. Different regions are standing up GPU specialist clouds, sovereign AI clouds coming out from all over the world, as people realize that they can't afford to export their country's knowledge, their country's culture for somebody else to then resell AI back to them, they have to -- they should, they have the skills and surely with us in combination, we can help them to do that build up their national AI. And so, the first thing that they have to do is, create their AI cloud, national AI cloud. You're also seeing us now growing into enterprise. The enterprise market has two paths. One path -- or if I could |
5,656 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | AI cloud. You're also seeing us now growing into enterprise. The enterprise market has two paths. One path -- or if I could say three paths. The first path, of course, just off-the-shelf AI. And there are of course Chat GPT, a fabulous off-the-shelf AI, there'll be others. There's also a proprietary AI, because software companies like ServiceNow and SAP, there are many, many others that can't afford to have their company's intelligence be outsourced to somebody else. And they are about building tools and on top of their tools they should build custom and proprietary and domain-specific copilots and assistants that they can then rent to their customer base. This is -- they're sitting on a goldmine, almost every major tools company in the world is sitting on a goldmine, and they recognize that they have to go build their own custom AIs. We have a new service called an AI foundry, where we leverage NVS (ph) capabilities to be able to serve them in that. And then the next one is enterprises building their own custom AIs, their own custom chatbots, their own custom RAGs. And this capability is spreading all over the world. And the way that we're going to serve that marketplace is with the entire stacks of systems, which includes our compute, our networking and our switches, running our software stack called NVIDIA AI Enterprise, taking it through our market partners, HP, Dell, Lenovo, so on and so forth. And so we're just -- we're seeing the waves of generative AI starting from the start-ups and CSPs, moving to consumer Internet companies, moving to enterprise software platforms, moving to enterprise companies. And then ultimately, one of the areas that you guys have seen us spend a lot of energy on has to do with industrial generative AI. This is where NVIDIA AI and NVIDIA Omniverse comes together and that is a really, really exciting work. And so I think the -- we're at the beginning of a basically across-the-board industrial transition to generative AI to accelerated computing. This is going to affect every |
5,657 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | of a basically across-the-board industrial transition to generative AI to accelerated computing. This is going to affect every company, every industry, every country. |
5,658 | NVDA | 3 | 2,024 | 2023-11-21 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. Thank you. I wanted to clarify something with Colette real quick, and then I had a question for Jensen as well. Colette, you mentioned that you'll be introducing regulation-compliant products over the next couple of months. Yet, the contribution to Q4 revenue should be relatively limited. Is that a timing issue and could it be a source of reacceleration in growth for Data Center in April and beyond or are the price points such that the contribution to revenue going forward should be relatively limited? And then the question for Jensen, the AI foundry service announcement from last week. I just wanted to ask about that, and hopefully, have you expand on it. How is the monetization model going to work? Is it primarily services and software revenue? How should we think about the long term opportunity set? And is this going to be exclusive to Microsoft or do you have plans to expand to other partners as well? Thank you.
Colette Kress: Thanks, Toshiya. On the question regarding potentially new products that we could provide to our China customers. It's a significant process to both design and develop these new products. As we discussed, we're going to make sure that we are in full discussions with the U.S. government of our intent to move products as well. Given our state about where we are in the quarter, we're already several weeks into the quarter. So it's just going to take some time for us to go through and discussing with our customers the needs and desires of these new products that we have. And moving forward, whether that's medium-term or long-term, it's just hard to say both the [Technical Difficulty] of what we can produce with the U.S. government and what the interest of our China customers in this. So we stay still focused on finding that right balance for our China customers, but it's hard to say at this time. |
5,659 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Toshiya, thanks for the question. There is a glaring opportunity in the world for AI foundry, and it makes so much sense. First, every company has its core intelligence. It makes up our company. Our data, our domain expertise, in the case of many companies, we create tools, and most of the software companies in the world are tool platforms, and those tools are used by people today. And in the future, it's going to be used by people augmented with a whole bunch of AIs that we hire. And these platforms just got to go across the world and you'll see and we've only announced a few; SAP, ServiceNow, Dropbox, Getty, many others are coming. And the reason for that is because they have their own proprietary AI. They want their own proprietary AI. They can't afford to outsource their intelligence and handout their data, and handout their flywheel for other companies to build the AI for them. And so, they come to us. We have several things that are really essential in a foundry. Just as TSMC as a foundry, you have to have AI technology. And as you know, we have just an incredible depth of AI capability -- AI technology capability. And then second, you have to have the best practice known practice, the skills of processing data through the invention of AI models to create AIs that are guardrails, fine-tuned, so on and so forth, that are safe, so on and so forth. And the third thing is you need factories. And that's what DGX Cloud is. Our AI models are called AI Foundations. Our process, if you will, our CAD system for creating AIs are called NeMo and they run on NVIDIA's factories we call DGX Cloud. Our monetization model is that with each one of our partners they rent a sandbox on DGX Cloud, where we work together, they bring their data, they bring their domain expertise, we bring our researchers and engineers, we help them build their custom AI. We help them make that custom AI incredible. Then that custom AI becomes theirs. And they deploy it on the runtime that is enterprise grade, enterprise optimized or |
5,660 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Then that custom AI becomes theirs. And they deploy it on the runtime that is enterprise grade, enterprise optimized or outperformance optimized, runs across everything NVIDIA. We have a giant installed base in the cloud, on-prem, anywhere. And it's secure, securely patched, constantly patched and optimized and supported. And we call that NVIDIA AI Enterprise. NVIDIA AI Enterprise is $4,500 per GP per year, that's our business model. Our business model is basically a license. Our customers then with that basic license can build their monetization model on top of. In a lot of ways we're wholesale, they become retail. They could have a per -- they could have subscription license base, they could per instance or they could do per usage, there is a lot of different ways that they could take a -- create their own business model, but ours is basically like a software license, like an operating system. And so our business model is help you create your custom models, you run those custom models on NVIDIA AI Enterprise. And it's off to a great start. NVIDIA AI Enterprise is going to be a very large business for us. |
5,661 | NVDA | 3 | 2,024 | 2023-11-21 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 wanted to know if it weren't for the China restrictions would the Q4 guide has been higher or are you supply-constrained in just reshipping stuff that would have gone to China elsewhere? And I guess along those lines you give us a feeling for where your lead times are right now in data center and just the China redirection such as-is, is it lowering those lead times, because you've got parts that are sort of immediately available to ship?
Colette Kress: Yeah. Stacy, let me see if I can help you understand. Yes, there are still situations where we are working on both improving our supply each and every quarter. We've done a really solid job of ramping every quarter, which has defined our revenue. But with the absence of China for our outlook for Q4, sure, there could have been some things that we are not supply-constrained that we could have sold, but kind of we would no longer can. So could our guidance had been a little higher in our Q4? Yes. We are still working on improving our supply on plan, on continuing growing all throughout next year as well towards that.
Operator: Your next question comes from the line of Matt Ramsay of TD Cowen. Your line is open. |
5,662 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your next question comes from the line of Matt Ramsay of TD Cowen. Your line is open.
Matt Ramsay: Thank you very much. Congrats, everybody, on the results. Jensen, I had a two-part question for you, and it comes off of sort of one premise. And the premise is, I still get a lot of questions from investors thinking about AI training as being NVIDIA's dominant domain and somehow inference, even large model inference takes more and more of the TAM that the market will become more competitive. You'll be less differentiated et cetera., et cetera. So I guess the two parts of the question are: number one, maybe you could spend a little bit of time talking about the evolution of the inference workload as we move to LLMs and how your company is positioned for that rather than smaller model inference. And second, up until a month or two ago, I never really got any questions at all about the data processing piece of the AI workloads. So the pieces of manipulating the data before training, between training and inference, after inference and I think that's a large part of the workload now. Maybe you could talk about how CUDA is enabling acceleration of those pieces of the workload. Thanks. |
5,663 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Sure. Inference is complicated. It's actually incredibly complicated. If you -- we this quarter announced one of the most exciting new engines, optimizing compilers called TensorRT-LLM. The reception has been incredible. You got to GitHub, it's been downloaded a ton, a whole lot of stars, integrated into stacks and frameworks all over the world, almost instantaneously. And there are several reasons for that, obviously. We could create TensorRT-LLM, because CUDA is programmable. If CUDA and our GPUs were not so programmable, it would really be hard for us to improve software stacks at the pace that we do. TensorRT-LLM, on the same GPU, without anybody touching anything, improves the performance by a factor of two. And then on top of that, of course, the pace of our innovation is so high. H200 increases it by another factor of two. And so, our inference performance, another way of saying inference cost, just reduced by a factor of four within about a year's time. And so, that's really, really hard to keep up with. The reason why everybody likes our inference engine is because our installed base. We've been dedicated to our installed base for 20 years, 20-plus years. We have an installed base that is not only largest in every single cloud, it's in every available from every enterprise system maker, it's used by companies of just about every industry. And every -- anytime you see a NVIDIA GPU, it runs our stack. It's architecturally compatible, something we've been dedicated to for a very long time. We're very disciplined about it. We make it our, if you will, architecture compatibility is job one. And that has conveyed to the world, the certainty of our platform stability. NVIDIA's platform stability certainty is the reason why everybody builds on us first and the reason why everybody optimizes on us first. All the engineering and all the work that you do, all the invention of technologies that you build on top of NVIDIA accrues to the -- and benefits everybody that uses our GPUs. And we have such a |
5,664 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | of technologies that you build on top of NVIDIA accrues to the -- and benefits everybody that uses our GPUs. And we have such a large installed base, large -- millions and millions of GPUs in cloud, 100 million GPUs from people’s PCs just about every workstation in the world, and they all architecturally compatible. And so, if you are an inference platform and you're deploying an inference application, you are basically an application provider. And as a software application provider, you're looking for large installed base. Data processing, before you could train a model, you have to curate the data, you have to dedupe the data, maybe you have to augment the data with synthetic data. So, process the data, clean the data, align the data, normalize the data, all of that data is measured not in bytes or megabytes, it's measured in terabytes and petabytes. And the amount of data processing that you do before data engineering, before that you do training is quite significant. It could represent 30%, 40%, 50% of the amount of work that you ultimately do. And what you -- and ultimately creating a data driven machine learning service. And so data processing is just a massive part. We accelerate Spark, we accelerate Python. One of the coolest things that we just did is called cuDF Pandas. Without one line of code, Pandas, which is the single most successful data science framework in the world. Pandas now is accelerated by NVIDIA CUDA. And just out-of-the box, without the line of code and so the acceleration is really quite terrific and people are just incredibly excited about it. And Pandas was designed for one purpose and one purpose only, really data processing, it's for data science. And so NVIDIA CUDA gives you all of that. |
5,665 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Your final question comes from the line of Harlan Sur of J.P. Morgan. Your line is open.
Harlan Sur: Good afternoon. Thanks for taking my question. If you look at the history of the tech industry like those companies that have been successful have always been focused on ecosystem; silicon, hardware, software, strong partnerships and just as importantly, right, an aggressive cadence of new products, more segmentation over time. The team recently announced a more aggressive new product cadence in data center from two years to now every year with higher levels of segmentation, training, optimization in printing CPU, GPU, DPU networking. How do we think about your R&D OpEx growth outlook to support a more aggressive and expanding forward roadmap, but more importantly, what is the team doing to manage and drive execution through all of this complexity? |
5,666 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Gosh. Boy, that's just really excellent. You just wrote NVIDIA's business plan, and so you described our strategy. First of all, there is a fundamental reason why we accelerate our execution. And the reason for that is because it fundamentally drives down cost. When the combination of TensorRT-LLM and H200 reduce the cost for our customers for large model inference by a factor of four, and so that includes, of course, our speeds and feeds, but mostly it's because of our software, mostly the software benefits because of the architecture. And so we want to accelerate our roadmap for that reason. The second reason is to expand the reach of generative AI, the world's number of data center configurations -- this is kind of the amazing thing. NVIDIA is in every cloud, but not one cloud is the same. NVIDIA is working with every single cloud service provider and not one of the networking control plane, security posture is the same. Everybody's platform is different and yet we're integrated into all of their stacks, all of their data centers and we work incredibly well with all of them. And not to mention, we then take the whole thing and we create AI factories that are standalone. We take our platform, we can put them into supercomputers, we can put them into enterprise. Bringing AI to enterprise is something generative AI Enterprise something nobody's ever done before. And we're right now in the process of going to market with all of that. And so the complexity includes, of course, all the technologies and segments and the pace. It includes the fact that we are architecturally compatible across every single one of those. It includes all of the domain specific libraries that we create. The reason why every computer company, without thinking, can integrate NVIDIA into their roadmap and take it to market. And the reason for that is, because there is market demand for it. There is market demand in healthcare, there is market demand in manufacturing, there is market demand, of course, in AI, including |
5,667 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | is market demand in healthcare, there is market demand in manufacturing, there is market demand, of course, in AI, including financial services, in supercomputing and quantum computing. The list of markets and segments that we have domain specific libraries is incredibly broaden. And then finally, we have an end-to-end solution for data centers; InfiniBand networking, Ethernet networking, x86, ARM, just about every permutation combination of solutions -- technology solutions and software stacks provided. And that translates to having the largest number of ecosystem software developers; the largest ecosystem of system makers; the largest and broadest distribution partnership network; and ultimately, the greatest reach. And that takes -- surely that takes a lot of energy. But the thing that really holds it together, and this is a great decision that we made decades ago, which is everything is architecturally compatible. When we develop a domain specific language that runs on one GPU, it runs on every GPU. When we optimize TensorRT for the cloud, we optimized it for enterprise. When we do something that brings in a new feature, a new library, a new feature or a new developer, they instantly get the benefit of all of our reach. And so that discipline, that architecture compatible discipline that has lasted more than a couple of decades now, is one of the reasons why NVIDIA is still really, really efficient. I mean, we're 28,000 people large and serving just about every single company, every single industry, every single market around the world. |
5,668 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Thank you. I will now turn the call back over to Jensen Huang for closing remarks. |
5,669 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Our strong growth reflects the broad industry platform transition from general purpose to accelerated computing and generative AI. Large language models start-ups consumer Internet companies and global cloud service providers are the first movers. The next waves are starting to build. Nations and regional CSPs are building AI clouds to serve local demand. Enterprise software companies like Adobe and Dropbox, SAP and ServiceNow are adding AI copilots and assistants to their platforms. Enterprises in the world's largest industries are creating custom AIs to automate and boost productivity. The generative AI era is in full steam and has created the need for a new type of data center, an AI factory; optimized for refining data and training, and inference, and generating AI. AI factory workloads are different and incremental to legacy data center workloads supporting IT tasks. AI factories run copilots and AI assistants, which are significant software TAM expansion and are driving significant new investment. Expanding the $1 trillion traditional data center infrastructure installed base, empowering the AI Industrial Revolution. NVIDIA H100 HGX with InfiniBand and the NVIDIA AI software stack define an AI factory today. As we expand our supply chain to meet the world's demand, we are also building new growth drivers for the next wave of AI. We highlighted three elements to our new growth strategy that are hitting their stride: CPU, networking, and software and services. Grace is NVIDIA's first data center CPU. Grace and Grace Hopper are in full production and ramping into a new multi-billion dollar product line next year. Irrespective of the CPU choice, we can help customers build an AI factory. NVIDIA networking now exceeds $10 billion annualized revenue run rate. InfiniBand grew five-fold year-over-year, and is positioned for excellent growth ahead as the networking of AI factories. Enterprises are also racing to adopt AI and Ethernet is the standard networking. This week we announced an Ethernet for |
5,670 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Enterprises are also racing to adopt AI and Ethernet is the standard networking. This week we announced an Ethernet for AI platform for enterprises. NVIDIA Spectrum-X is an end-to-end solution of Bluefield SuperNIC, Spectrum-4 Ethernet switch and software that boosts Ethernet performance by up to 1.6x for AI workloads. Dell, HPE and Lenovo have joined us to bring a full generative AI solution of NVIDIA AI computing, networking and software to the world's enterprises. NVIDIA software and services is on track to exit the year at an annualized run rate of $1 billion. Enterprise software platforms like ServiceNow and SAP need to build and operate proprietary AI. Enterprises need to build and deploy custom AI copilots. We have the AI technology, expertise and scale to help customers build custom models with their proprietary data on NVIDIA DGX Cloud and deploy the AI applications on enterprise grade NVIDIA AI Enterprise. NVIDIA is essentially an AI foundry. NVIDIA's GPUs, CPUs, networking, AI foundry services and NVIDIA AI Enterprise software are all growth engines in full throttle. Thanks for joining us today. We look forward to updating you on our progress next quarter. |
5,671 | NVDA | 3 | 2,024 | 2023-11-21 17:00:00 | NVIDIA Corporation | 32,307 | Operator: This concludes today's conference call. You may now disconnect. |
5,672 | NVDA | 2 | 2,024 | 2023-08-23 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 Second 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 will 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 second 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 third 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, August 23, 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. |
5,673 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Thanks, Simona. We had an exceptional quarter. Record Q2 revenue of $13.51 billion was up 88% sequentially and up 101% year-on-year, and above our outlook of $11 billion. Let me first start with Data Center. Record revenue of $10.32 billion was up 141% sequentially and up 171% year-on-year. Data Center compute revenue nearly tripled year-on-year, driven primarily by accelerating demand from cloud service providers and large consumer Internet companies for HGX platform, the engine of generative AI and large language models. Major companies, including AWS, Google Cloud, Meta, Microsoft Azure and Oracle Cloud as well as a growing number of GPU cloud providers are deploying, in volume, HGX systems based on our Hopper and Ampere architecture Tensor Core GPUs. Networking revenue almost doubled year-on-year, driven by our end-to-end InfiniBand networking platform, the gold standard for AI. There is tremendous demand for NVIDIA accelerated computing and AI platforms. Our supply partners have been exceptional in ramping capacity to support our needs. Our data center supply chain, including HGX with 35,000 parts and highly complex networking has been built up over the past decade. We have also developed and qualified additional capacity and suppliers for key steps in the manufacturing process such as [indiscernible] packaging. We expect supply to increase each quarter through next year. By geography, data center growth was strongest in the U.S. as customers direct their capital investments to AI and accelerated computing. China demand was within the historical range of 20% to 25% of our Data Center revenue, including compute and networking solutions. At this time, let me take a moment to address recent reports on the potential for increased regulations on our exports to China. We believe the current regulation is achieving the intended results. Given the strength of demand for our products worldwide, we do not anticipate that additional export restrictions on our Data Center GPUs, if adopted, would have an |
5,674 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | products worldwide, we do not anticipate that additional export restrictions on our Data Center GPUs, if adopted, would have an immediate material impact to our financial results. However, over the long term, restrictions prohibiting the sale of our Data Center GPUs to China, if implemented, will result in a permanent loss and opportunity for the U.S. industry to compete and lead in one of the world's largest markets. Our cloud service providers drove exceptional strong demand for HGX systems in the quarter, as they undertake a generational transition to upgrade their data center infrastructure for the new era of accelerated computing and AI. The NVIDIA HGX platform is culminating of nearly two decades of full stack innovation across silicon, systems, interconnects, networking, software and algorithms. Instances powered by the NVIDIA H100 Tensor Core GPUs are now generally available at AWS, Microsoft Azure and several GPU cloud providers, with others on the way shortly. Consumer Internet companies also drove the very strong demand. Their investments in data center infrastructure purpose-built for AI are already generating significant returns. For example, Meta, recently highlighted that since launching Reels, AI recommendations have driven a more than 24% increase in time spent on Instagram. Enterprises are also racing to deploy generative AI, driving strong consumption of NVIDIA powered instances in the cloud as well as demand for on-premise infrastructure. Whether we serve customers in the cloud or on-prem through partners or direct, their applications can run seamlessly on NVIDIA AI enterprise software with access to our acceleration libraries, pre-trained models and APIs. We announced a partnership with Snowflake to provide enterprises with accelerated path to create customized generative AI applications using their own proprietary data, all securely within the Snowflake Data Cloud. With the NVIDIA NeMo platform for developing large language models, enterprises will be able to make custom LLMs for advanced |
5,675 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | With the NVIDIA NeMo platform for developing large language models, enterprises will be able to make custom LLMs for advanced AI services, including chatbot, search and summarization, right from the Snowflake Data Cloud. Virtually, every industry can benefit from generative AI. For example, AI Copilot such as those just announced by Microsoft can boost the productivity of over 1 billion office workers and tens of millions of software engineers. Billions of professionals in legal services, sales, customer support and education will be available to leverage AI systems trained in their field. AI Copilot and assistants are set to create new multi-hundred billion dollar market opportunities for our customers. We are seeing some of the earliest applications of generative AI in marketing, media and entertainment. WPP, the world's largest marketing and communication services organization, is developing a content engine using NVIDIA Omniverse to enable artists and designers to integrate generative AI into 3D content creation. WPP designers can create images from text prompts while responsibly trained generative AI tools and content from NVIDIA partners such as Adobe and Getty Images using NVIDIA Picasso, a foundry for custom generative AI models for visual design. Visual content provider Shutterstock is also using NVIDIA Picasso to build tools and services that enables users to create 3D scene background with the help of generative AI. We've partnered with ServiceNow and Accenture to launch the AI Lighthouse program, fast tracking the development of enterprise AI capabilities. AI Lighthouse unites the ServiceNow enterprise automation platform and engine with NVIDIA accelerated computing and with Accenture consulting and deployment services. We are collaborating also with Hugging Face to simplify the creation of new and custom AI models for enterprises. Hugging Face will offer a new service for enterprises to train and tune advanced AI models powered by NVIDIA HGX cloud. And just yesterday, VMware and NVIDIA announced a |
5,676 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | enterprises to train and tune advanced AI models powered by NVIDIA HGX cloud. And just yesterday, VMware and NVIDIA announced a major new enterprise offering called VMware Private AI Foundation with NVIDIA, a fully integrated platform featuring AI software and accelerated computing from NVIDIA with multi-cloud software for enterprises running VMware. VMware's hundreds of thousands of enterprise customers will have access to the infrastructure, AI and cloud management software needed to customize models and run generative AI applications such as intelligent chatbot, assistants, search and summarization. We also announced new NVIDIA AI enterprise-ready servers featuring the new NVIDIA L40S GPU built for the industry standard data center server ecosystem and BlueField-3 DPU data center infrastructure processor. L40S is not limited by [indiscernible] supply and is shipping to the world's leading server system makers (ph). L40S is a universal data center processor designed for high volume data center standing out to accelerate the most compute-intensive applications, including AI training and inventing through the designing, visualization, video processing and NVIDIA Omniverse industrial digitalization. NVIDIA AI enterprise ready servers are fully optimized for VMware, Cloud Foundation and Private AI Foundation. Nearly 100 configurations of NVIDIA AI enterprise ready servers will soon be available from the world's leading enterprise IT computing companies, including Dell, HP and Lenovo. The GH200 Grace Hopper Superchip which combines our ARM-based Grace CPU with Hopper GPU entered full production and will be available this quarter in OEM servers. It is also shipping to multiple supercomputing customers, including Atmos (ph), National Labs and the Swiss National Computing Center. And NVIDIA and SoftBank are collaborating on a platform based on GH200 for generative AI and 5G/6G applications. The second generation version of our Grace Hopper Superchip with the latest HBM3e memory will be available in Q2 of calendar |
5,677 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | The second generation version of our Grace Hopper Superchip with the latest HBM3e memory will be available in Q2 of calendar 2024. We announced the DGX GH200, a new class of large memory AI supercomputer for giant AI language model, recommendator systems and data analytics. This is the first use of the new NVIDIA [indiscernible] switch system, enabling all of its 256 Grace Hopper Superchips to work together as one, a huge jump compared to our prior generation connecting just eight GPUs over [indiscernible]. DGX GH200 systems are expected to be available by the end of the year, Google Cloud, Meta and Microsoft among the first to gain access. Strong networking growth was driven primarily by InfiniBand infrastructure to connect HGX GPU systems. Thanks to its end-to-end optimization and in-network computing capabilities, InfiniBand delivers more than double the performance of traditional Ethernet for AI. For billions of dollar AI infrastructures, the value from the increased throughput of InfiniBand is worth hundreds of [indiscernible] and pays for the network. In addition, only InfiniBand can scale to hundreds of thousands of GPUs. It is the network of choice for leading AI practitioners. For Ethernet-based cloud data centers that seek to optimize their AI performance, we announced NVIDIA Spectrum-X, an accelerated networking platform designed to optimize Ethernet for AI workloads. Spectrum-X couples the Spectrum or Ethernet switch with the BlueField-3 DPU, achieving 1.5x better overall AI performance and power efficiency versus traditional Ethernet. BlueField-3 DPU is a major success. It is in qualification with major OEMs and ramping across multiple CSPs and consumer Internet companies. Now moving to gaming. Gaming revenue of $2.49 billion was up 11% sequentially and 22% year-on-year. Growth was fueled by GeForce RTX 40 Series GPUs for laptops and desktop. End customer demand was solid and consistent with seasonality. We believe global end demand has returned to growth after last year's slowdown. We have a large |
5,678 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | consistent with seasonality. We believe global end demand has returned to growth after last year's slowdown. We have a large upgrade opportunity ahead of us. Just 47% of our installed base have upgraded to RTX and about 20% of the GPU with an RTX 3060 or higher performance. Laptop GPUs posted strong growth in the key back-to-school season, led by RTX 4060 GPUs. NVIDIA's GPU-powered laptops have gained in popularity, and their shipments are now outpacing desktop GPUs from several regions around the world. This is likely to shift the reality of our overall gaming revenue a bit, with Q2 and Q3 as the stronger quarters of the year, reflecting the back-to-school and holiday build schedules for laptops. In desktop, we launched the GeForce RTX 4060 and the GeForce RTX 4060 TI GPUs, bringing the Ada Lovelace architecture down to price points as low as $299. The ecosystem of RTX and DLSS games continue to expand. 35 new games added to DLSS support, including blockbusters such as Diablo IV and Baldur’s Gate 3. There's now over 330 RTX accelerated games and apps. We are bringing generative AI to gaming. At COMPUTEX, we announced NVIDIA Avatar Cloud Engine or ACE for games, a custom AI model foundry service. Developers can use this service to bring intelligence to non-player characters. And it harnesses a number of NVIDIA Omniverse and AI technologies, including NeMo, Riva and Audio2Face. Now moving to Professional Visualization. Revenue of $375 million was up 28% sequentially and down 24% year-on-year. The Ada architecture ramp drove strong growth in Q2, rolling out initially in laptop workstations with a refresh of desktop workstations coming in Q3. These will include powerful new RTX systems with up to 4 NVIDIA RTX 6000 GPUs, providing more than 5,800 teraflops of AI performance and 192 gigabytes of GPU memory. They can be configured with NVIDIA AI enterprise or NVIDIA Omniverse inside. We also announced three new desktop workstation GPUs based on the Ada generation. The NVIDIA RTX 5000, 4500 and 4000, offering up to 2x |
5,679 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | announced three new desktop workstation GPUs based on the Ada generation. The NVIDIA RTX 5000, 4500 and 4000, offering up to 2x the RT core throughput and up to 2x faster AI training performance compared to the previous generation. In addition to traditional workloads such as 3D design and content creation, new workloads in generative AI, large language model development and data science are expanding the opportunity in pro visualization for our RTX technology. One of the key themes in Jensen's keynote [indiscernible] earlier this month was the conversion of graphics and AI. This is where NVIDIA Omniverse is positioned. Omniverse is OpenUSD's native platform. OpenUSD is a universal interchange that is quickly becoming the standard for the 3D world, much like HTML is the universal language for the 2D [indiscernible]. Together, Adobe, Apple, Autodesk, Pixar and NVIDIA form the Alliance for OpenUSD. Our mission is to accelerate OpenUSD's development and adoption. We announced new and upcoming Omniverse cloud APIs, including RunUSD and ChatUSD to bring generative AI to OpenUSD workload. Moving to automotive. Revenue was $253 million, down 15% sequentially and up 15% year-on-year. Solid year-on-year growth was driven by the ramp of self-driving platforms based on [indiscernible] or associated with a number of new energy vehicle makers. The sequential decline reflects lower overall automotive demand, particularly in China. We announced a partnership with MediaTek to bring drivers and passengers new experiences inside the car. MediaTek will develop automotive SoCs and integrate a new product line of NVIDIA's GPU chiplet. The partnership covers a wide range of vehicle segments from luxury to entry level. Moving to the rest of the P&L. GAAP gross margins expanded to 70.1% and non-GAAP gross margin to 71.2%, driven by higher data center sales. Our Data Center products include a significant amount of software and complexity, which is also helping drive our gross margin. Sequential GAAP operating expenses were up 6% and |
5,680 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | of software and complexity, which is also helping drive our gross margin. Sequential GAAP operating expenses were up 6% and non-GAAP operating expenses were up 5%, primarily reflecting increased compensation and benefits. We returned approximately $3.4 billion to shareholders in the form of share repurchases and cash dividends. Our Board of Directors has just approved an additional $25 billion in stock repurchases to add to our remaining $4 billion of authorization as of the end of Q2. Let me turn to the outlook for the third quarter of fiscal 2024. Demand for our Data Center platform where AI is tremendous and broad-based across industries on customers. Our demand visibility extends into next year. Our supply over the next several quarters will continue to ramp as we lower cycle times and work with our supply partners to add capacity. Additionally, the new L40S GPU will help address the growing demand for many types of workloads from cloud to enterprise. For Q3, total revenue is expected to be $16 billion, plus or minus 2%. We expect sequential growth to be driven largely by Data Center with gaming and ProViz also contributing. GAAP and non-GAAP gross margins are expected to be 71.5% and 72.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $2.95 billion and $2 billion, respectively. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $100 million, excluding gains and losses from non-affiliated investments. GAAP and non-GAAP tax rates are expected to be 14.5%, plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight some upcoming events for the financial community. We will attend the Jefferies Tech Summit on August 30 in Chicago, the Goldman Sachs Conference on September 5 in San Francisco, the Evercore Semiconductor Conference on September 6 as well as the Citi Tech Conference |
5,681 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | on September 5 in San Francisco, the Evercore Semiconductor Conference on September 6 as well as the Citi Tech Conference on September 7, both in New York. And the BofA Virtual AI conference on September 11. Our earnings call to discuss the results of our third quarter of fiscal 2024 is scheduled for Tuesday, November 21. Operator, we will now open the call for questions. Could you please poll for questions for us? Thank you. |
5,682 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Thank you. [Operator Instructions] We'll take our first question from Matt Ramsay with TD Cowen. Your line is now open.
Matt Ramsay: Yes. Thank you very much. Good afternoon. Obviously, remarkable results. Jensen, I wanted to ask a question of you regarding the really quickly emerging application of large model inference. So I think it's pretty well understood by the majority of investors that you guys have very much a lockdown share of the training market. A lot of the smaller market -- smaller model inference workloads have been done on ASICs or CPUs in the past. And with many of these GPT and other really large models, there's this new workload that's accelerating super-duper quickly on large model inference. And I think your Grace Hopper Superchip products and others are pretty well aligned for that. But could you maybe talk to us about how you're seeing the inference market segment between small model inference and large model inference and how your product portfolio is positioned for that? Thanks. |
5,683 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Yeah. Thanks a lot. So let's take a quick step back. These large language models are fairly -- are pretty phenomenal. It does several things, of course. It has the ability to understand unstructured language. But at its core, what it has learned is the structure of human language. And it has encoded or within it -- compressed within it a large amount of human knowledge that it has learned by the corpuses that it studied. What happens is, you create these large language models and you create as large as you can, and then you derive from it smaller versions of the model, essentially teacher-student models. It's a process called distillation. And so when you see these smaller models, it's very likely the case that they were derived from or distilled from or learned from larger models, just as you have professors and teachers and students and so on and so forth. And you're going to see this going forward. And so you start from a very large model and it has a large amount of generality and generalization and what's called zero-shot capability. And so for a lot of applications and questions or skills that you haven't trained it specifically on, these large language models miraculously has the capability to perform them. That's what makes it so magical. On the other hand, you would like to have these capabilities in all kinds of computing devices, and so what you do is you distill them down. These smaller models might have excellent capabilities on a particular skill, but they don't generalize as well. They don't have what is called as good zero-shot capabilities. And so they all have their own unique capabilities, but you start from very large models.
Operator: Okay. Next, we'll go to Vivek Arya with BofA Securities. Your line is now open. |
5,684 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Okay. Next, we'll go to Vivek Arya with BofA Securities. Your line is now open.
Vivek Arya: Thank you. Just had a quick clarification and a question. Colette, if you could please clarify how much incremental supply do you expect to come online in the next year? You think it's up 20%, 30%, 40%, 50%? So just any sense of how much supply because you said it's growing every quarter. And then Jensen, the question for you is, when we look at the overall hyperscaler spending, that buy is not really growing that much. So what is giving you the confidence that they can continue to carve out more of that pie for generative AI? Just give us your sense of how sustainable is this demand as we look over the next one to two years? So if I take your implied Q3 outlook of Data Center, $12 billion, $13 billion, what does that say about how many servers are already AI accelerated? Where is that going? So just give some confidence that the growth that you are seeing is sustainable into the next one to two years.
Colette Kress: So thanks for that question regarding our supply. Yes, we do expect to continue increasing ramping our supply over the next quarters as well as into next fiscal year. In terms of percent, it's not something that we have here. It is a work across so many different suppliers, so many different parts of building an HGX and many of our other new products that are coming to market. But we are very pleased with both the support that we have with our suppliers and the long time that we have spent with them improving their supply. |
5,685 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: The world has something along the lines of about $1 trillion worth of data centers installed, in the cloud, in enterprise and otherwise. And that $1 trillion of data centers is in the process of transitioning into accelerated computing and generative AI. We're seeing two simultaneous platform shifts at the same time. One is accelerated computing. And the reason for that is because it's the most cost-effective, most energy effective and the most performant way of doing computing now. So what you're seeing, and then all of a sudden, enabled by generative AI, enabled by accelerated compute and generative AI came along. And this incredible application now gives everyone two reasons to transition to do a platform shift from general purpose computing, the classical way of doing computing, to this new way of doing computing, accelerated computing. It's about $1 trillion worth of data centers, call it, $0.25 trillion of capital spend each year. You're seeing the data centers around the world are taking that capital spend and focusing it on the two most important trends of computing today, accelerated computing and generative AI. And so I think this is not a near-term thing. This is a long-term industry transition and we're seeing these two platform shifts happening at the same time.
Operator: Next, we go to Stacy Rasgon with Bernstein Research. Your line is open.
Stacy Rasgon: Hi, guys. Thanks for taking my question. I was wondering, Colette, if you could tell me like how much of Data Center in the quarter, maybe even the guide is like systems versus GPU, like DGX versus just the H100? What I'm really trying to get at is, how much is like pricing or content or however you want to define that [indiscernible] versus units actually driving the growth going forward. Can you give us any color around that? |
5,686 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Colette Kress: Sure, Stacy. Let me help. Within the quarter, our HGX systems were a very significant part of our Data Center as well as our Data Center growth that we had seen. Those systems include our HGX of our Hopper architecture, but also our Ampere architecture. Yes, we are still selling both of these architectures in the market. Now when you think about that, what does that mean from both the systems as a unit, of course, is growing quite substantially, and that is driving in terms of the revenue increases. So both of these things are the drivers of the revenue inside Data Center. Our DGXs are always a portion of additional systems that we will sell. Those are great opportunities for enterprise customers and many other different types of customers that we're seeing even in our consumer Internet companies. The importance there is also coming together with software that we sell with our DGXs, but that's a portion of our sales that we're doing. The rest of the GPUs, we have new GPUs coming to market that we talk about the L40S, and they will add continued growth going forward. But again, the largest driver of our revenue within this last quarter was definitely the HGX system.
Jensen Huang: And Stacy, if I could just add something. You say it’s H100 and I know you know what your mental image in your mind. But the H100 is 35,000 parts, 70 pounds, nearly 1 trillion transistors in combination. Takes a robot to build – well, many robots to build because it’s 70 pounds to lift. And it takes a supercomputer to test a supercomputer. And so these things are technology marvels, and the manufacturing of them is really intensive. And so I think we call it H100 as if it’s a chip that comes off of a fab, but H100s go out really as HGXs sent to the world’s hyperscalers and they’re really, really quite large system components, if you will.
Operator: Next, we go to Mark Lipacis with Jefferies. Your line is now open. |
5,687 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next, we go to Mark Lipacis with Jefferies. Your line is now open.
Mark Lipacis: Hi. Thanks for taking my question and congrats on the success. Jensen, it seems like a key part of the success -- your success in the market is delivering the software ecosystem along with the chip and the hardware platform. And I had a two-part question on this. I was wondering if you could just help us understand the evolution of your software ecosystem, the critical elements. And is there a way to quantify your lead on this dimension like how many person years you've invested in building it? And then part two, I was wondering if you would care to share with us your view on the -- what percentage of the value of the NVIDIA platform is hardware differentiation versus software differentiation? Thank you. |
5,688 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | A – Jensen Huang: Yeah, Mark, I really appreciate the question. Let me see if I could use some metrics, so we have a run time called AI Enterprise. This is one part of our software stack. And this is, if you will, the run time that just about every company uses for the end-to-end of machine learning from data processing, the training of any model that you like to do on any framework you'd like to do, the inference and the deployment, the scaling it out into a data center. It could be a scale-out for a hyperscale data center. It could be a scale-out for enterprise data center, for example, on VMware. You can do this on any of our GPUs. We have hundreds of millions of GPUs in the field and millions of GPUs in the cloud and just about every single cloud. And it runs in a single GPU configuration as well as multi-GPU per compute or multi-node. It also has multiple sessions or multiple computing instances per GPU. So from multiple instances per GPU to multiple GPUs, multiple nodes to entire data center scale. So this run time called NVIDIA AI enterprise has something like 4,500 software packages, software libraries and has something like 10,000 dependencies among each other. And that run time is, as I mentioned, continuously updated and optimized for our installed base for our stack. And that's just one example of what it would take to get accelerated computing to work. The number of code combinations and type of application combinations is really quite insane. And it's taken us two decades to get here. But what I would characterize as probably our -- the elements of our company, if you will, are several. I would say number 1 is architecture. The flexibility, the versatility and the performance of our architecture makes it possible for us to do all the things that I just said, from data processing to training to inference, for preprocessing of the data before you do the inference to the post processing of the data, tokenizing of languages so that you could then train with it. The amount of -- the workflow is much |
5,689 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | processing of the data, tokenizing of languages so that you could then train with it. The amount of -- the workflow is much more intense than just training or inference. But anyways, that's where we'll focus and it's fine. But when people actually use these computing systems, it's quite -- requires a lot of applications. And so the combination of our architecture makes it possible for us to deliver the lowest cost ownership. And the reason for that is because we accelerate so many different things. The second characteristic of our company is the installed base. You have to ask yourself, why is it that all the software developers come to our platform? And the reason for that is because software developers seek a large installed base so that they can reach the largest number of end users, so that they could build a business or get a return on the investments that they make. And then the third characteristic is reach. We're in the cloud today, both for public cloud, public-facing cloud because we have so many customers that use -- so many developers and customers that use our platform. CSPs are delighted to put it up in the cloud. They use it for internal consumption to develop and train and to operate recommender systems or search or data processing engines and whatnot all the way to training and inference. And so we're in the cloud, we're in enterprise. Yesterday, we had a very big announcement. It's really worthwhile to take a look at that. VMware is the operating system of the world's enterprise. And we've been working together for several years now, and we're going to bring together -- together, we're going to bring generative AI to the world's enterprises all the way out to the edge. And so reach is another reason. And because of reach, all of the world's system makers are anxious to put NVIDIA's platform in their systems. And so we have a very broad distribution from all of the world's OEMs and ODMs and so on and so forth because of our reach. And then lastly, because of our scale and velocity, we were able |
5,690 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | OEMs and ODMs and so on and so forth because of our reach. And then lastly, because of our scale and velocity, we were able to sustain this really complex stack of software and hardware, networking and compute and across all of these different usage models and different computing environments. And we're able to do all this while accelerating the velocity of our engineering. It seems like we're introducing a new architecture every two years. Now we're introducing a new architecture, a new product just about every six months. And so these properties make it possible for the ecosystem to build their company and their business on top of us. And so those in combination makes us special. |
5,691 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next, we'll go to Atif Malik with Citi. Your line is open.
Atif Malik: Hi. Thank you for taking my question. Great job on results and outlook. Colette, I have a question on the core L40S that you guys talked about. Any idea how much of the supply tightness can L40S help with? And if you can talk about the incremental profitability or gross margin contribution from this product? Thank you.
Jensen Huang: Yeah, Atif. Let me take that for you. The L40S is really designed for a different type of application. H100 is designed for large-scale language models and processing just very large models and a great deal of data. And so that's not L40S' focus. L40S' focus is to be able to fine-tune models, fine-tune pretrained models, and it'll do that incredibly well. It has a transform engine. It's got a lot of performance. You can get multiple GPUs in a server. It's designed for hyperscale scale-out, meaning it's easy to install L40S servers into the world's hyperscale data centers. It comes in a standard rack, standard server, and everything about it is standard and so it's easy to install. L40S also is with the software stack around it and along with BlueField-3 and all the work that we did with VMware and the work that we did with Snowflakes and ServiceNow and so many other enterprise partners. L40S is designed for the world's enterprise IT systems. And that's the reason why HPE, Dell, and Lenovo and some 20 other system makers building about 100 different configurations of enterprise servers are going to work with us to take generative AI to the world's enterprise. And so L40S is really designed for a different type of scale-out, if you will. It's, of course, large language models. It's, of course, generative AI, but it's a different use case. And so the L40S is going to -- is off to a great start and the world's enterprise and hyperscalers are really clamoring to get L40S deployed.
Operator: Next, we'll go to Joe Moore with Morgan Stanley. Your line is open. |
5,692 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next, we'll go to Joe Moore with Morgan Stanley. Your line is open.
Joseph Moore: Great. Thank you. I guess the thing about these numbers that's so remarkable to me is the amount of demand that remains unfulfilled, talking to some of your customers. As good as these numbers are, you sort of more than tripled your revenue in a couple of quarters. There's a demand, in some cases, for multiples of what people are getting. So can you talk about that? How much unfulfilled demand do you think there is? And you talked about visibility extending into next year. Do you have line of sight into when you get to see supply-demand equilibrium here?
Jensen Huang: Yeah. We have excellent visibility through the year and into next year. And we're already planning the next-generation infrastructure with the leading CSPs and data center builders. The demand – easiest way to think about the demand, the world is transitioning from general-purpose computing to accelerated computing. That's the easiest way to think about the demand. The best way for companies to increase their throughput, improve their energy efficiency, improve their cost efficiency is to divert their capital budget to accelerated computing and generative AI. Because by doing that, you're going to offload so much workload off of the CPUs, but the available CPUs is -- in your data center will get boosted. And so what you're seeing companies do now is recognizing this -- the tipping point here, recognizing the beginning of this transition and diverting their capital investment to accelerated computing and generative AI. And so that's probably the easiest way to think about the opportunity ahead of us. This isn't a singular application that is driving the demand, but this is a new computing platform, if you will, a new computing transition that's happening. And data centers all over the world are responding to this and shifting in a broad-based way.
Operator: Next, we go to Toshiya Hari with Goldman Sachs. Your line is now open. |
5,693 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next, we go to Toshiya Hari with Goldman Sachs. Your line is now open.
Toshiya Hari: Hi. Thank you for taking the question. I had one quick clarification question for Colette and then another one for Jensen. Colette, I think last quarter, you had said CSPs were about 40% of your Data Center revenue, consumer Internet at 30%, enterprise 30%. Based on your remarks, it sounded like CSPs and consumer Internet may have been a larger percentage of your business. If you can kind of clarify that or confirm that, that would be super helpful. And then Jensen, a question for you. Given your position as the key enabler of AI, the breadth of engagements and the visibility you have into customer projects, I'm curious how confident you are that there will be enough applications or use cases for your customers to generate a reasonable return on their investments. I guess I ask the question because there is a concern out there that there could be a bit of a pause in your demand profile in the out years. Curious if there's enough breadth and depth there to support a sustained increase in your Data Center business going forward. Thank you.
Colette Kress: Okay. So thank you, Toshiya, on the question regarding our types of customers that we have in our Data Center business. And we look at it in terms of combining our compute as well as our networking together. Our CSPs, our large CSPs are contributing a little bit more than 50% of our revenue within Q2. And the next largest category will be our consumer Internet companies. And then the last piece of that will be our enterprise and high performance computing. |
5,694 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Toshi, I'm reluctant to guess about the future and so I'll answer the question from the first principle of computer science perspective. It is recognized for some time now that general purpose computing is just not and brute forcing general purpose computing. Using general purpose computing at scale is no longer the best way to go forward. It's too energy costly, it's too expensive, and the performance of the applications are too slow. And finally, the world has a new way of doing it. It's called accelerated computing and what kicked it into turbocharge is generative AI. But accelerated computing could be used for all kinds of different applications that's already in the data center. And by using it, you offload the CPUs. You save a ton of money in order of magnitude, in cost and order of magnitude and energy and the throughput is higher and that's what the industry is really responding to. Going forward, the best way to invest in the data center is to divert the capital investment from general purpose computing and focus it on generative AI and accelerated computing. Generative AI provides a new way of generating productivity, a new way of generating new services to offer to your customers, and accelerated computing helps you save money and save power. And the number of applications is, well, tons. Lots of developers, lots of applications, lots of libraries. It's ready to be deployed. And so I think the data centers around the world recognize this, that this is the best way to deploy resources, deploy capital going forward for data centers. This is true for the world's clouds and you're seeing a whole crop of new GPU specialty -- GPU specialized cloud service providers. One of the famous ones is CoreWeave and they're doing incredibly well. But you're seeing the regional GPU specialist service providers all over the world now. And it's because they all recognize the same thing, that the best way to invest their capital going forward is to put it into accelerated computing and generative AI. We're |
5,695 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | thing, that the best way to invest their capital going forward is to put it into accelerated computing and generative AI. We're also seeing that enterprises want to do that. But in order for enterprises to do it, you have to support the management system, the operating system, the security and software-defined data center approach of enterprises, and that's all VMware. And we've been working several years with VMware to make it possible for VMware to support not just the virtualization of CPUs but a virtualization of GPUs as well as the distributed computing capabilities of GPUs, supporting NVIDIA's BlueField for high-performance networking. And all of the generative AI libraries that we've been working on is now going to be offered as a special SKU by VMware's sales force, which is, as we all know, quite large because they reach some several hundred thousand VMware customers around the world. And this new SKU is going to be called VMware Private AI Foundation. And this will be a new SKU that makes it possible for enterprises. And in combination with HP, Dell, and Lenovo's new server offerings based on L40S, any enterprise could have a state-of-the-art AI data center and be able to engage generative AI. And so I think the answer to that question is hard to predict exactly what's going to happen quarter-to-quarter. But I think the trend is very, very clear now that we're seeing a platform shift. |
5,696 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Next, we'll go to Timothy Arcuri with UBS. Your line is now open.
Timothy Arcuri: Thanks a lot. Can you talk about the attach rate of your networking solutions to your -- to the compute that you're shipping? In other words, is like half of your compute shipping with your networking solutions more than half, less than half? And is this something that maybe you can use to prioritize allocation of the GPUs? Thank you. |
5,697 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: Well, working backwards, we don't use that to prioritize the allocation of our GPUs. We let customers decide what networking they would like to use. And for the customers that are building very large infrastructure, InfiniBand is, I hate to say it, kind of a no-brainer. And the reason for that because the efficiency of InfiniBand is so significant, some 10%, 15%, 20% higher throughput for $1 billion infrastructure translates to enormous savings. Basically, the networking is free. And so, if you have a single application, if you will, infrastructure or it’s largely dedicated to large language models or large AI systems, InfiniBand is really a terrific choice. However, if you’re hosting for a lot of different users and Ethernet is really core to the way you manage your data center, we have an excellent solution there that we had just recently announced and it’s called Spectrum-X. Well, we’re going to bring the capabilities, if you will, not all of it, but some of it, of the capabilities of InfiniBand to Ethernet so that we can also, within the environment of Ethernet, allow you to – enable you to get excellent generative AI capabilities. So Spectrum-X is just ramping now. It requires BlueField-3 and it supports both our Spectrum-2 and Spectrum-3 Ethernet switches. And the additional performance is really spectacular. BlueField-3 makes it possible and a whole bunch of software that goes along with it. BlueField, as all of you know, is a project really dear to my heart, and it’s off to just a tremendous start. I think it’s a home run. This is the concept of in-network computing and putting a lot of software in the computing fabric is being realized with BlueField-3, and it is going to be a home run.
Operator: Our final question comes from the line of Ben Reitzes with Melius. Your line is now open. |
5,698 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Operator: Our final question comes from the line of Ben Reitzes with Melius. Your line is now open.
Benjamin Reitzes: Hi. Good afternoon. Good evening. Thank you for the question, putting me in here. My question is with regard to DGX Cloud. Can you talk about the reception that you're seeing and how the momentum is going? And then Colette, can you also talk about your software business? What is the run rate right now and the materiality of that business? And it does seem like it's already helping margins a bit. Thank you very much. |
5,699 | NVDA | 2 | 2,024 | 2023-08-23 17:00:00 | NVIDIA Corporation | 32,307 | Jensen Huang: DGX Cloud's strategy, let me start there. DGX Cloud's strategy is to achieve several things: number one, to enable a really close partnership between us and the world's CSPs. We recognize that many of our -- we work with some 30,000 companies around the world. 15,000 of them are startups. Thousands of them are generative AI companies and the fastest-growing segment, of course, is generative AI. We're working with all of the world's AI start-ups. And ultimately, they would like to be able to land in one of the world's leading clouds. And so we built DGX Cloud as a footprint inside the world's leading clouds so that we could simultaneously work with all of our AI partners and help blend them easily in one of our cloud partners. The second benefit is that it allows our CSPs and ourselves to work really closely together to improve the performance of hyperscale clouds, which is historically designed for multi-tenancy and not designed for high-performance distributed computing like generative AI. And so to be able to work closely architecturally to have our engineers work hand in hand to improve the networking performance and the computing performance has been really powerful, really terrific. And then thirdly, of course, NVIDIA uses very large infrastructures ourselves. And our self-driving car team, our NVIDIA research team, our generative AI team, our language model team, the amount of infrastructure that we need is quite significant. And none of our optimizing compilers are possible without our DGX systems. Even compilers these days require AI, and optimizing software and infrastructure software requires AI to even develop. It's been well publicized that our engineering uses AI to design our chips. And so the internal -- our own consumption of AI, our robotics team, so on and so forth, Omniverse teams, so on and so forth, all needs AI. And so our internal consumption is quite 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 |
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