Q3 2025 NVIDIA Corp Earnings Call
Good afternoon. My name is Gael and I'll be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's third quarter earnings call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session.
If you would like to ask a question during this time, simply press star followed by the number 1 on your telephone keypad.
Speaker Change: If you would like to withdraw your question, press the star 1 again. Thank you. Stewart Stecker, you may begin your conference.
Thank you.
Speaker Change: Good afternoon, everyone, and welcome to Nvidia's conference call for the third quarter of Fiscal 2025.
Speaker Change: With me today from Nvidia are Jensen Huang, President and Chief Executive Officer, and Colette Kress.
Executive Vice President and Chief Financial Officer.
Speaker Change: I'd like to remind you that our call is being webcast live on Nvidia's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the fourth quarter of fiscal 2025.
Speaker Change: The content of today's call is Nvidia's property. It can't be reproduced or transcribed without our prior written consent.
Speaker Change: During this call, we may make forward-looking statements based on current expectations.
Speaker Change: These are subject to a number of significant risks and uncertainties, and our actual results may differ materially.
Speaker Change: 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.
Speaker Change: All our statements are made as of today, November 20, 2024, based on information currently available to us.
Speaker Change: Except it's required by law, we assume no obligation to update any such statements.
Speaker Change: 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.
Colette Kress: Thank you, Stewart. Q3 was another record quarter. We continued to deliver incredible growth. Revenue of $35.1 billion was up 17% sequentially and up 94% year-on-year and well above our outlook of $32.5 billion.
Colette Kress: All market platforms post a strong sequential and year-over-year growth, fueled by the adoption of NVIDIA, accelerated computing, and AI.
Starting with Datacenter. Another record was achieved in Datacenter.
Colette Kress: Revenue of $30.8 billion, up 17% sequential and up 112% year-on-year.
Colette Kress: Nvidia Hopper demand is exceptional and sequentially Nvidia H200 sales increased significantly to double digit billions, the fastest product ramp in our company's history.
Colette Kress: The H200 delivers up to 2x faster inference performance and up to 50% improved TCO.
Colette Kress: Cloud service providers were approximately half of our data center sales, with revenue increasing more than 2x year-on-year.
Colette Kress: CSPs deployed Nvidia H200 infrastructure and high-speed networking with installations scaling to tens of thousands of DPUs to grow their business and serve rapidly rising demand for AI training and inference workloads.
Colette Kress: The NVIDIA H200 powered cloud instances are now available from AWS, CoreWeave, and Microsoft Azure, with Google Cloud and OCI coming soon.
Colette Kress: Alongside significant growth from our large CSPs, NVIDIA GPU regional cloud revenue jumped 2x year-on-year as North America, India, and Asia-Pacific regions ramped NVIDIA cloud instances and sovereign cloud buildouts.
Consumer Internet Revenue
Colette Kress: More than doubled year-on-year as companies scaled their Nvidia Hopper infrastructure to support next-generation AI models, training, multimodal and agentic AI, deep learning recommender engines, and generative AI inference and content creation workloads.
Colette Kress: Nvidia's Ampere and Hopper infrastructures are fueling inference revenue growth for customers.
is the largest inference platform in the world.
Colette Kress: Our large install base and rich software ecosystem encourage developers to optimize for NVIDIA and deliver continued performance and TCL improvements.
Colette Kress: Rapid advancements in Nvidia software algorithms boosted Hopper inference throughput by an incredible 5x in one year, and cut time to first token by 5x.
Colette Kress: Our upcoming release of Nvidia NIM will boost hopper inference performance by an additional 2.4x.
Colette Kress: Continuous performance optimizations are a hallmark of Nvidia and drive increasingly economic returns for the entire Nvidia installed base.
Colette Kress: Blackwell is in full production after a successfully executed mass change. We shipped 13,000 GPU samples to customers in the third quarter, including one of the first Blackwell DGX engineering samples to OpenAI.
Colette Kress: Blackwell is a full stack, full infrastructure, AI data center scale system with customizable configurations needed to address a diverse and growing AI market.
Colette Kress: From x86 to ARM, training to inferencing GPUs, InfiniBand to Ethernet switches, and NVLink, and from liquid cooled to air cooled.
Colette Kress: Every customer is racing to be the first to market. Blackwell is now in the hands of all of our major partners, and they are working to bring up their data centers.
Colette Kress: We are integrating Blackwell systems into the diverse data center configurations of our customers. Blackwell demand is staggering, and we are racing to scale supply to meet the incredible demand customers are placing on us.
Customers are gearing up to deploy Blackwell at scale.
Speaker Change: Oracle announced the world's first Zetta-scale AI cloud computing clusters that can scale to over 131,000 Blackwell GPUs to help enterprises train and deploy some of the most demanding next-generation AI models.
Speaker Change: Yesterday, Microsoft announced they will be the first CSP to offer, in private preview, Blackwell-based cloud instances powered by NVIDIA, GB200, and Quantum InfiniBand.
Speaker Change: Last week, Blackwell made its debut on the most recent round of MLPerf training results.
Speaker Change: Sweeping the per GPU benchmarks and delivering a 2.2x leap in performance over Hopper.
Speaker Change: The results also demonstrate our relentless pursuit to drive down the cost of compute.
Speaker Change: Just 64 Blackwell GPUs are required to run the GPT-3 benchmark compared to 256 H-100 for a 4x reduction in cost.
Speaker Change: Nvidia Blackwell architecture with NVLink switch enables up to 30x faster inference performance.
Speaker Change: and a new level of inference, scaling, throughput, and response time that is excellent for running new reasoning inference applications like OpenAI's O1 model.
Speaker Change: With every new platform shift, a wave of startups is created. Hundreds of AI-native companies are already delivering AI services with great success.
through Google, Meta, Microsoft, and OpenAI.
are the headliners.
Anthropic, Perplexity, Nistral
Adobe Firefly, Runway, MidJourney.
Lightricks
Speaker Change: Harvey, Codium, Cursor, and Abridge are seeing great success while thousands of AI-native startups are building new services.
Speaker Change: The next wave of AI are enterprise AI and industrial AI.
Speaker Change: Enterprise AI is in full throttle. NVIDIA AI Enterprise, which includes NVIDIA NeMo and NEM microservices, is an operating platform of agentic AI. Industry leaders are using NVIDIA AI to build co-pilots and agents.
working with Nvidia, Cadence, Cloudera, Cohesity, NetApp
Nutanix
Speaker Change: Salesforce, SAP, and ServiceNow are racing to accelerate development of these applications with the potential for billions of agents to be deployed in the coming years.
Speaker Change: Consulting leaders like Accenture and Deloitte are taking NVIDIA AI to the world's enterprises. Accenture launched a new business group with 30,000 professionals trained on NVIDIA AI technology to help facilitate this global build-out.
Speaker Change: Nearly 1,000 companies are using NVIDIA NIMH and the speed of its uptake is evident in NVIDIA AI enterprise monetization.
Speaker Change: We expect Nvidia AI Enterprise Folier revenue to increase over 2X from last year, and our pipeline continues to build.
Speaker Change: Overall, our software, service, and support revenue is annualizing at $1.5 billion, and we expect to exit this year annualizing at over $2 billion.
Speaker Change: Industrial AI and robotics are accelerating. This is triggered by breakthroughs in physical AI, foundation models that understand the physical world.
Speaker Change: Like NVIDIA NeMo for enterprise AI agents, we built NVIDIA Omniverse for developers to build, train, and operate industrial AI and robotics.
Speaker Change: Some of the largest industrial manufacturers in the world are adopting NVIDIA Omniverse to accelerate their businesses, automate their workflows, and to achieve new levels of operating efficiency.
Speaker Change: Foxconn, the world's largest electronics manufacturer, is using digital twins and industrial AI built on NVIDIA Omniverse to speed the bring-up of its Blackwell factories and drive new levels of efficiency.
Speaker Change: and its Mexico facility alone, Foxconn expects to reduce a reduction of over 30% in annual kilowatt hour usage.
Speaker Change: From a geographic perspective, our data center revenue in China grew sequentially due to shipments of export-compliant copper products to industries.
Speaker Change: As a percentage of total data setter revenue, it remains well below levels prior to the onset of export controls.
Speaker Change: We expect the market in China to remain very competitive going forward. We will continue to comply with export controls while serving our customers.
Speaker Change: Our sovereign AI initiatives continue to gather momentum as countries embrace NVIDIA-accelerated computing for a new industrial revolution powered by AI.
Speaker Change: India's leading CSPs include Product Communications and Zoda Data Services are building AI factories for tens of thousands of Nvidia GPUs.
Speaker Change: By year-end, they will have boosted Nvidia's GPU deployment in a country of nearly 10x.
Speaker Change: Infosys, TSC, Wipro are adopting NVIDIA AI Enterprise and upskilling nearly half a million developers and consultants to help clients build and run AI agents on our platform.
Speaker Change: In Japan, SoftBank is building the nation's most powerful AI supercomputer with NVIDIA DGX Blackwell and Quantum InfiniBand.
Speaker Change: SoftBank is also partnering with NVIDIA to transform the telecommunications network into a distributed AI network with NVIDIA AI Aerial.
Speaker Change: and ARAN platform that can process both 5G RAN on AI on CUDA. We are launching the same in the U.S. with T-Mobile.
Speaker Change: Leaders across Japan, including Fujitsu, NEC, and NTT, are adopting NVIDIA AI Enterprise and major consulting companies, including EY, Strategy, and Consulting.
will help bring NVIDIA AI technology to Japan's industries.
Speaker Change: Networking revenue increased 20% year-on-year. Areas of sequential revenue growth include InfiniBand and Ethernet switches, SmartNICs, and BlueField DPUs. Though networking revenue was sequentially down, networking demand is strong and growing, and we anticipate sequential growth in Q4.
Speaker Change: CSPs and supercomputing centers are using and adopting the Nvidia InfiniBand platform to power new H200 clusters.
Speaker Change: Spectrum X Ethernet for AI revenue increased over 3x year-on-year and our pipeline continues to build with multiple CSPs and consumer internet companies planning large cluster deployments.
Speaker Change: Traditional Ethernet was not designed for AI. NVIDIA Spectrum X uniquely leverages technology previously exclusive to InfiniBand to enable customers to achieve massive scale of their GPU compute.
Utilizing Spectrum X, XAI's Colossus 100,000 hopper supercomputer.
Speaker Change: Experienced zero application latency degradation and maintained 95% data throughput versus 60% for traditional Ethernet.
Now moving to gaming and AI PCs.
Speaker Change: Gaming revenue of $3.3 billion increased 14% sequentially and 15% year-on-year. Q3 was a great quarter for gaming with notebook, console, and desktop revenue all growing sequentially and year-on-year.
Speaker Change: RTX and demand was fueled by strong back-to-school sales as consumers continue to choose GeForce RTX GPUs and devices to power gaming, creative, and AI applications.
Speaker Change: Channel Inventory remains healthy, and we are gearing up for the holiday season.
Speaker Change: We began shipping new GeForce RTX AI PCs with up to 321 AI tops from ASUS and MSI with Microsoft's Copilot Plus capabilities anticipated in Q4.
Speaker Change: These machines harness the power of RTX ray-tracing and AI technologies to supercharge gaming, photo and video editing, image generation, and coding.
Speaker Change: This past quarter we celebrated the 25th anniversary of the GeForce 256, the world's first GPU.
Speaker Change: For transforming computing graphics to igniting the AI revolution, NVIDIA's GPUs have been the driving force behind some of the most consequential technologies of our time.
Moving to ProViz.
Speaker Change: Revenue of $486 million, was up 7% sequentially, and 17% year-on-year.
Speaker Change: RTX workstations continue to be the preferred choice to power professional graphics, design, and engineering-related workloads.
Speaker Change: Additionally, AI is emerging as a powerful demand driver, including autonomous vehicle simulation, generative AI model prototyping for productivity-related use cases, and generative AI content creation in media and entertainment.
Speaker Change: Moving to automotive, revenue was a record $449 million, up 30% sequentially and up 72% year-on-year. Strong growth was driven by self-driving ramps of Nvidia O-RAN and robust end-market demand for NAVs.
Speaker Change: Global Cars is rolling out its fully electric SUV built on NVIDIA OREN and DRIVE OS.
Okay, moving to the rest of the P&L.
Speaker Change: GAAP gross margin was 74.6% and non-GAAP gross margin was 75%, down sequentially primarily driven by a mix shift of the H-100 systems to more complex and higher cost systems within data center.
Speaker Change: Sequentially, GAAP operating expenses and non-GAAP operating expenses were up 9% due to higher compute, infrastructure, and engineering development costs for new product introductions.
Speaker Change: In Q3, we returned $11.2 billion to shareholders in the form of share repurchases and cash dividends.
Speaker Change: So let me turn to the Outlook for the fourth quarter.
Speaker Change: Total revenue is expected to be $37.5 billion, plus or minus 2%, which incorporates continued demand for Hopper Architecture and the initial ramp of our Blackwell products.
Speaker Change: While demand greatly exceeds supply, we are on track to exceed our previous Blackwell revenue estimate of several billion dollars as our visibility into supply continues to increase.
Speaker Change: On gaming, although sell-through is strong in Q3, we expect Q4 revenue to decline sequentially due to supply constraints.
Speaker Change: GAAP and non-GAAP gross margins are expected to be 73% and 73.5% respectively.
Speaker Change: plus or minus 50 basis points. Blackwell is a customizable AI infrastructure with seven different types of Nvidia-built chips, multiple networking options, and for air and liquid-cooled data centers.
Speaker Change: Our current focus is on ramping to strong demand, increasing system availability, and providing the optimal mix of configurations to our customer. As Blackwell ramps, we expect gross margins to moderate to the low 70s.
Speaker Change: When fully ramped, we expect Blackwell margins to be in the mid-70s.
Speaker Change: GAAP and non-GAAP operating expenses are expected to be approximately $4.8 billion and $3.4 billion, respectively. We are a datacenter-scale AI infrastructure company. Our investments include building datacenters for development of our hardware and software stacks and to support new introductions.
Speaker Change: GAAP and non-GAAP other income and expenses are expected to be an income of approximately $400 million, excluding gains and losses from non-affiliated investments.
Speaker Change: GAAP and non-GAAP tax rates are expected to be 16.5%, plus or minus 1%, excluding any discrete items.
Speaker Change: Further financial details are included in the CFO commentary and other information available on our IR websites. In closing, let me highlight upcoming events for the financial community. We will be attending the UBS Global Technology and AI Conference on December 3rd in Scottsdale.
Speaker Change: Please join us at CES in Las Vegas where Jensen will deliver a keynote on January 6th. And we will host a Q&A session for financial analysts the next day on January 7th.
Speaker Change: Our earnings call to discuss results for the fourth quarter of fiscal 2025 is scheduled for February 26, 2025.
Speaker Change: We will now open the call for questions. Operator, can you pull for questions, please?
As a reminder, please limit yourself to one question.
Speaker Change: Your first question comes from the line of CJ Mews of Cantor Fitzgerald. Your line is open.
Speaker Change: Yeah, good afternoon. Thank you. Thank you for taking the question. I guess just a question for you on the debate around whether scaling for large language models have stalled. Obviously, we're very early here, but we'd love to hear your thoughts on this front. How are you helping your customers?
Speaker Change: as they work through these issues. And then obviously part of the context here is we're discussing clusters that have yet to benefit from Blackwell. So is this driving even greater demand for Blackwell? Thank you.
Pre-training scaling is intact and it's continuing.
You know, as you know, this is an empirical law.
Speaker Change: It's not a fundamental physical law, but the evidence is that it continues to scale. What we're learning, however, is that it's not enough.
that we've now discovered two other ways to scale.
Speaker Change: One is post-training scaling. Of course, the first generation of post-training was reinforcement learning human feedback, but now we have reinforcement learning AI feedback and all forms of synthetic data-generated data that assists in post-training.
Speaker Change: Training, Scaling. And one of the biggest, biggest events and one of the most exciting developments is Strawberry.
ChatGPT-01
Speaker Change: OpenAI's O-1, which does inference timescaling, what's called test timescaling. The longer it thinks, the better.
The better and higher quality answer it produces.
Damn it!
Speaker Change: considers approaches like chain of thought and multi-path planning and you know all kinds of techniques necessary to reflect and so on so forth and it is you know intuitively it's a little bit like us doing thinking in our head before we answer a question.
Speaker Change: And so we now have three ways of scaling, and we're seeing all three ways of scaling. And as a result of that, the demand for our infrastructures is really great.
Speaker Change: You see now that at the tail end of the last generation of foundation models, we're at about 100,000 hoppers.
You know, the next generation starts at 100,000 Blackwells.
Speaker Change: And so that kind of gives you a sense of where the industry is moving with respect to...
Speaker Change: Pre-training, Scaling, Post-training, Scaling, and then now very importantly, Inference Time Scaling.
Speaker Change: So the demand is really great for all of those reasons, but remember...
Speaker Change: Simultaneously, we're seeing inference really starting to scale off for our company. You know, we are the largest inference platform in the world today because our installed base is so large and everything that was trained on amperes and hoppers inference incredibly on amperes and hoppers.
Speaker Change: And as we move to Blackwells for training foundation models, it leaves behind it, you know, a large installed base of extraordinary infrastructure for inference.
Speaker Change: And so we're seeing inference demand go up, we're seeing inference time scaling go up, we see the number of...
AI-native companies.
continue to grow.
Speaker Change: Your next question comes from the line of Toshiya Hiree of Goldman Sachs. Your line is open.
Speaker Change: Hi. Good afternoon. Thank you so much for taking the question. Jensen, you executed the mass change earlier this year. There were some reports over the weekend about some heating issues. On the back of this, we've had investors ask about your ability to execute to the roadmap you presented at GTC this year with Ultra coming out next year and the transition to Rubin in 2026.
Speaker Change: Can you sort of speak to that and, you know, some investors are questioning that, so if you can sort of speak to your ability to execute on time, that would be super helpful. And then a quick Part B, on supply constraints, is it a multitude of componentry that's causing this, or is it specifically, you know, co-auth, HBM? You know, is the supply constraints, are the supply constraints getting better? Are they worsening? Any sort of color on that would be super helpful as well. Thank you.
Speaker Change: Thanks. So let's see. Back to the first question. Blackwell production is in full steam.
In fact,
Speaker Change: As Colette mentioned earlier, we will deliver this quarter more black wells than we had previously estimated.
Speaker Change: And so the supply chain team is doing an incredible job working with our supply partners to increase Blackwell. And we're going to continue to work hard to increase Blackwell through next year.
Speaker Change: It is the case that demand exceeds our supply, and that's expected as we're in the beginnings.
of this generative AI revolution, as we all know.
Speaker Change: and we're at the beginning of a new generation of foundation models that are able to do reasoning and able to do long thinking and of course one of the really exciting areas is physical AI.
AI that now understands the structure of the physical world.
Systems that are being stood up.
Speaker Change: by Dell and CoreWeave. I think you saw systems from Oracle stood up. You have systems from Microsoft and they're about to preview their Grace Blackwell systems.
Speaker Change: You have systems that are at Google, and so all of these CSPs are racing to be first.
Speaker Change: The engineering that we do with them is, as you know, rather complicated, and the reason for that is because, although we build full stack and full infrastructure,
Speaker Change: We disaggregate all of this AI supercomputer and we integrate it into all of the custom data centers and architectures around the world.
Speaker Change: That integration process is something we've done several generations now, we're very good at it, but still, there's still a lot of engineering that happens at this point, but as you see from all of the systems that are being stood up, Blackwell's in great shape.
with respect to the supply chain.
There are seven different chips.
Speaker Change: Seven custom chips that we built in order for us to deliver the Blackwell systems.
Speaker Change: The BlackWall systems go in air-cooled or liquid-cooled, NVLink 8 or NVLink 72, or NVLink 8, NVLink 36, NVLink 72. We have x86 or GRACE.
Speaker Change: And the integration of all of those systems into the world's data centers is nothing short of a miracle.
Speaker Change: You know, you have to go back and take a look at...
Speaker Change: And in terms of how much Blackwell Total Systems will ship this quarter, which is measured in billions, the ramp is incredible. And so almost every company in the world
Speaker Change: It seems to be involved in our supply chain, and we've got great partners.
Speaker Change: everybody from, of course, TSMC and Amphenol, the connector company, incredible company, Vertiv and SK Hynix and Micron, Spill, Amcor.
You know
KYEC, and...
There's Foxconn and the many factories that they've built.
Speaker Change: Quanta, WeWin, Dell, HP, Supermicro, Lenovo. The number of companies is just really quite incredible. Quanta, and I'm sure I've missed partners that are involved in the ramping of...
Speaker Change: of Blackwell, which I really appreciate. And so anyways, I think we're in great shape with respect to the Blackwell ramp at this point.
Speaker Change: And then lastly, your question about our execution of our roadmap. We're on an annual roadmap, and we're expecting to continue to execute on our annual roadmap.
Speaker Change: And by doing so, we increase the performance, of course, of our platform. But it's also really important to realize that when we're able to increase performance,
Speaker Change: and do so at X factors at a time, we're reducing the cost of training, we're reducing the cost of inferencing, we're reducing the cost of AI so that it could be much more accessible.
Speaker Change: You know, of course, tens of megawatts in the past, and now it's, you know, most data centers are now 100 megawatts to several hundred megawatts, and we're planning on gigawatt data centers.
It doesn't.
Speaker Change: It doesn't really matter how large the data centers are, the power is limited.
And when you're in the Power Ltd. data center,
The best, the highest performance per watt.
translate directly into the highest revenues for our partners.
Speaker Change: And so on the one hand, our annual roadmap reduces cost.
Speaker Change: But on the other hand, because our perf per watt is so good...
Speaker Change: Compared to anything out there, we generate for our customers the greatest possible revenues. And so that annual rhythm is really important to us, and we have every intention of continuing to do that. And everything's on track, as far as I know.
Speaker Change: Your next question comes from the line of Timothy Arcuri of UBS. Your line is open.
Speaker Change: Thanks a lot. I'm wondering if you can talk about the trajectory of how Blackwell's going to ramp this year. I know, you know, Jensen, you did just talk about Blackwell being better.
Speaker Change: I think you had said several billions of dollars in January, sounds like you're going to do more than that. But I think in recent months also, you said that Blackwell crosses over Hopper in the April quarter. So I guess I had two questions. First of all,
Speaker Change: Is that still the right way to think about it, that Blackwell will crossover Hopper in April? And then, Colette, you kind of talked about Blackwell bringing down Gross Margin to the low...
Speaker Change: 70s as it ramps. So I guess if April is the crossover, is that the worst of the pressure on gross margins? So you're gonna be kind of in the low 70s as soon as April. I'm just wondering if you can sort of shape that for us. Thanks.
Colette, why don't you start?
Colette Kress: Sure, let me first start with your question, Tim. Thank you, regarding our gross margins. And we discussed that our gross margins, as we are ramping Blackwell in the very beginning, and the many different configurations, the many different chips that we are bringing to market, we are going to focus on making sure we have the best experience for our customers as they stand that up.
Colette Kress: We will start growing in for our gross margins, but we do believe those will be in the low 70s in that first part of the round. So, you're correct, as you look at the quarters following after that, we will start increasing our gross margins, and we hope to get to the mid-70s quite quickly as part of that round.
Hopper demand will continue.
Colette Kress: through next year, fairly the first several quarters of the next year.
Meanwhile, we will ship more Blackwells next quarter than this.
Colette Kress: and we'll ship more Blackwells the quarter after that than our first quarter. And so that kind of puts it in perspective. We are really at the beginnings.
Colette Kress: that runs on CPUs to machine learning that creates neural networks that runs on GPUs.
Colette Kress: And that fundamental shift from coding to machine learning is widespread at this point. There are no companies who are not going to do machine learning.
Colette Kress: and so machine learning is also what enables generative AI and so on the one hand the first thing that's that's happening is
Colette Kress: A trillion dollars worth of computing systems and data centers around the world is now being modernized for machine learning.
Colette Kress: On the other hand, secondarily, I guess, is that on top of these systems, we're going to be creating a new type of capability called AI.
Colette Kress: And when we say generative AI, we're essentially saying that these data centers are really AI factories. They're generating something. Just like we generate electricity, we're now going to be generating AI.
Colette Kress: And if the number of customers is large, just as the number of consumers of electricity is large, these generators are going to be running 24-7. And today, you know, many AI services are running 24-7, just like an AI factory.
Colette Kress: And so we're going to see this new type of system come online, and I call it an AI factory because that's really as close to what it is. It's unlike a data center of the past. And so these two fundamental trends are really just beginning.
Colette Kress: And so we expect this to happen, this growth, this modernization and the creation of a new industry to go on for several years.
Speaker Change: Your next question comes from the line of Vivek Arya of Bank of America Securities. Your line is open.
Speaker Change: Thanks for taking my question. Colette, just to clarify, do you think it's a fair assumption to think Nvidia could recover to kind of mid-70s growth margin in the back half of
Calendar 25. Just wanted to clarify that. And then
Speaker Change: Jensen, my main question, you know, historically when we have seen hardware deployment cycles, they have inevitably included some digestion along the way.
Speaker Change: When do you think we get to that phase? Or is it just too premature to discuss that because you're just at the start of Blackwell? So how many quarters of shipments do you think is required to satisfy this first wave? Can you
Speaker Change: continue to grow this into calendar 26? How should, you know, we be prepared to see what we have seen historically, right? The periods of digestion along the way of a long-term kind of secular hardware deployment.
Speaker Change: Okay, Vivek, thank you for the question. Let me clarify your question regarding gross margins. Could we reach the mid-70s in the second half of next year? And yes, I think it is a reasonable assumption or goal for us to do, but we'll just have to see how that mix of RAMP goes. But yes, it is definitely possible.
Speaker Change: The way to think through that, Vivek, is I believe that there will be no digestion until we modernize a trillion dollars for the data centers.
If you just look at the world's data centers...
Speaker Change: The vast majority of it is built for a time when we wrote applications by hand and we ran them on CPUs. It's just not a sensible thing to do anymore.
Speaker Change: If every company's CapEx, if they're ready to build a data center tomorrow, they ought to build it for a future of machine learning and generative AI.
because they have plenty of old data centers.
Speaker Change: And so what's going to happen over the course of next...
Speaker Change: You know, X number of years, and let's assume that over the course of four years...
Speaker Change: The world's data centers could be modernized as we grow into IT. As you know, IT continues to grow about 20-30% a year, let's say.
Speaker Change: And so, let's say by 2030, the world's data centers for computing is, call it a couple trillion dollars. We have to grow into that. We have to modernize the data center from coding to machine learning.
And that's number one.
Speaker Change: The second part of it is Gems of AI, and we're now producing a new type of capability the world's never known. A new market segment that the world's never had.
Speaker Change: If you look at OpenAI, it didn't replace anything. It's something that's completely brand new. In a lot of ways, as when the iPhone came, it was completely brand new. It wasn't really replacing anything.
Speaker Change: And so we're going to see more and more companies like that. And they're going to create and generate.
Out of their services, essentially intelligence.
Speaker Change: Some of it would be digital artist intelligence, like Runway. Some of it would be basic intelligence, like OpenAI. Some of it would be legal intelligence, like Harvey.
Speaker Change: Digital Marketing Intelligence like Riders, so on and so forth. And the number of these companies, what are they called, AI-native companies,
Speaker Change: are just in hundreds, you know, and almost every platform shift, there was, there were internet companies, as you recall, there were cloud-first companies, there were mobile-first companies.
Speaker Change: Now they're AI natives. And so these companies are being created because people see that there's a platform shift and there's a brand new opportunity to do something completely new.
Speaker Change: And so my sense is that we're going to continue to build out, to modernize IT, modernize computing, number one. And then number two, create these AI factories that are going to be for a new industry for the production of artificial intelligence.
Speaker Change: Your next question comes from the line of Stacy Ragson of Bernstein Research. Your line is open.
Speaker Change: Hi guys, thanks for taking my questions. Colette, I had a clarification and a question for you. The clarification, just when you say low 70s gross margins, does 73.5 count as low 70s or do you have something else in mind?
And for my question...
Speaker Change: You're guiding total revenues and so I mean total data center revenues in the next quarter must be up You know quote-unquote several billion dollars, but it sounds like Blackwell Now should be up more than that
Speaker Change: But you also said Hopper was still strong. So like, is Hopper down?
Speaker Change: Sequentially the next quarter, and if it is, like, why? Is it because of the supply constraints? Is, you know, China's been pretty strong. Is China kind of rolling off a bit into Q4? So any color you can give us on sort of the Blackwell ramp and the Blackwell versus Hopper behavior into Q4 would be really helpful. Thank you.
Speaker Change: So first starting on your first question there, Stacey, regarding our growth margin and defined low.
Speaker Change: Low, of course, is below the mids, and let's say we might be at...
Speaker Change: 71, maybe about 72, 72 and a half. We're going to be in that range. We could be higher than that as well. We're just going to have to see how it comes through. We do want to make sure that we are ramping and continuing that improvement, the improvement in terms of our yields, the improvement in terms of the product as we go through the rest of the year. So we'll get up to the mid-70s by that point.
Speaker Change: The second statement was a question regarding our hopper and what is our hopper doing. We have seen substantial growth for H200, not only in terms of orders, but the quickness in terms of those that are standing that up.
Speaker Change: It is an amazing product and it's the fastest growing and ramping that we've seen.
We will continue to be selling Hopper.
in this quarter, in Q4 for sure.
Speaker Change: That is across the board in terms of all of our different configurations, and our configurations include what we may do in terms of China.
Speaker Change: But keep that in mind that folks are also, at the same time, looking to build out their blackwell. So we've got a little bit of both happening in Q4, but yes, is it possible for Hopper to grow between Q3 and Q4? It's possible, but we'll just have to see.
Speaker Change: Your next question comes from the line of Joseph Moore of Morgan Stanley. Your line is open.
Speaker Change: Great, thank you. I wonder if you could talk a little bit about what you're seeing in the inference market. You know, you've talked about Strawberry and some of the ramifications of longer-scaling inference projects, but you've also talked about the possibility that as some of these hopper clusters age, that you could use some of the hopper latent chips for inference. So, you know, I guess do you expect inference to outgrow training in the next kind of 12-month time frame? And just generally your thoughts there.
Speaker Change: Our hopes and dreams is that someday the world does a ton of inference.
And that's when AI has really succeeded.
Speaker Change: You know, it's when every single company is doing inference inside their companies.
Speaker Change: for the marketing department and forecasting department and, you know, supply chain group and, you know, their legal department and engineering, of course, and coding, of course, and, you know, so we hope that every company is doing inference
Speaker Change: 24-7, and that there will be a whole bunch of AI-native startups.
Speaker Change: thousands of AI native startups that that are generating tokens and generating AI.
Speaker Change: And every aspect of your computer experience from using, you know, Outlook to PowerPointing or when you're sitting there with Excel, you're constantly generating tokens.
Speaker Change: And every time you read a PDF, open a PDF, it generated a whole bunch of tokens.
One of my favorite applications is NotebookLM.
This is Google.
Speaker Change: Google application that came out. I used the living daylights out of it just because it's fun, you know, and I put every PDF, every archive paper into it just to listen to it as well as, you know, scanning through it and
Speaker Change: And so I think that's the goal, is to train these models so that people use it.
Speaker Change: And there's now a whole new era of AI, if you will, a whole new genre of AI called physical AI.
Speaker Change: You know just as large language models understand the human language and how we the thinking process if you will
Speaker Change: Physical AI understands the physical world, and it understands the meaning of the structure, and understands what's sensible and what's not, and what could happen and what wouldn't. Not only does it understand, but it can predict, you know, roll out a short future.
Speaker Change: That capability is incredibly valuable for industrial AI and robotics, and so that's fired up so many.
Speaker Change: AI-native companies and robotics companies and physical AI companies that you're probably hearing about and And it's really really the reason why we built Omniverse
Speaker Change: Omniverse is so that we can enable these AIs to be created and learn in Omniverse and learn from synthetic data generation and reinforcement learning physics feedback instead of human feedback, it's now physics feedback.
Speaker Change: To have these capabilities, Omniverse was created so that we can enable physical AI. And so the goal is to generate tokens. The goal is to inference.
Speaker Change: And we're starting to see that growth happening, so I'm super excited about that. Now, let me just say one more thing. Inference is super hard.
Speaker Change: And the reason why inference is super hard is because you need the accuracy to be high, on the one hand.
Speaker Change: You need the throughput to be high so that the cost could be as low as possible, but you also need the latency to be low.
Speaker Change: and, you know, computers that are high throughput as well as low latency.
is incredibly hard to build.
And these applications have long context lengths because they...
Speaker Change: They want to understand, they want to be able to inference.
Speaker Change: within understanding the context of what they're being asked to do. And so the context length is growing larger and larger. On the other hand, the models are getting larger, they're multi-modality. You know, just the number of dimensions that inference is innovating is incredible.
Speaker Change: And this innovation rate is what makes Nvidia's architecture so great.
Speaker Change: And so that ability to innovate in every single direction at the same time, having a large install base so that whatever you create could land on an Nvidia computer and be deployed broadly all around the world in every single data center.
Speaker Change: all the way out to the edge, you know, into robotic systems. You know, that capability is really quite phenomenal.
Speaker Change: Your next question comes from the line of Aaron Walkers, sorry, Aaron Rakers of Wells Fargo. Your line is open.
Aaron Rakers: Yeah, thanks for taking the question. I wanted to ask you, as we kind of focus on the Blackwell cycle and think about the data center business, when I look at the results this last quarter, you know, Colette, you mentioned that obviously the networking business was down about 15% sequentially, but then your comments were that you were seeing very strong demand. You mentioned also that you had multiple cloud, you know, CSP design wins for these large-scale clusters. So I'm curious if you could unpack what's going on in the networking business and where maybe you've seen some constraints and just your confidence in the pace of SpectrumX progressing to that multiple billions of dollars that you previously had talked about. Thank you.
Speaker Change: Let's first start with the networking. The growth year over year is tremendous and our focus since the beginning of
Speaker Change: Our acquisition of Mellanox has really been about building together the work that we do in terms of, in the data center. The networking is such a critical part of that. Our ability to sell our networking with many of our systems that we are doing in data center is continuing to grow and do quite well.
Speaker Change: So this quarter is just a slight dip down, and we're going to be right back up in terms of growing. We're getting ready for Blackwell and more and more systems that we'll be using, not only our existing networking, but also the networking that is going to be incorporated in a lot of these large systems that we are providing them to.
Thank you for watching!
Speaker Change: Your next question comes from the line of Atif Malik of Citi. Your line is open.
Atif Malik: Thank you for taking my question. I have two quick ones for Colette. Colette, on the last earnings call, you mentioned that sovereign demand is in low double-digit billions. Can you provide an update on that?
Atif Malik: And then, can you explain the supply constraint situation in gaming? Is that because you're shifting your supply towards data center?
Colette Kress: So, first starting in terms of sovereign AI, such an important part of growth, something that has really surfaced with the onset of generative AI and building models in the individual countries around the world.
Colette Kress: And we see a lot of them, and we talked about a lot of them in the call today and the work that they are doing. So our sovereign AI and our pipeline going forward is still absolutely intact, as those are working to build these foundational models in their own language, in their own culture, and working in terms of the enterprises within those countries.
Colette Kress: and I think you'll continue to see this be a growth opportunity that you may see with our regional clouds that are being stood up.
Colette Kress: and or those that are focusing in terms of AI factories.
Colette Kress: for many parts of the sovereign AI. This is areas where this is growing not only in terms of in Europe, but you're also seeing this in terms of growth in terms of in the Asia-Pac as well.
Colette Kress: Let me flip to your second question that you asked regarding gaming. So our gaming right now from a supply, we're busy trying to make sure that we can ramp all of our different products.
Colette Kress: And in this case, our gaming supply, given what we saw selling through, was moving quite fast. Now the challenge that we have is how fast could we get that supply getting ready into the market for this quarter. Not to worry, I think we'll be back on track with more supply as we turn the corner into the new calendar year. We're just going to be tight for this quarter.
Speaker Change: Your next question comes from the line of Ben Reitzes of Milius Research. Your line is open.
Speaker Change: Yeah, hi. Thanks a lot for the question. I wanted to ask Colette and Jensen, you know, with regard to sequential growth. So, very strong sequential growth this quarter, and you're guiding to about 7%.
Speaker Change: Your comments on Blackwell imply that we re-accelerate from there as you get more supply.
Speaker Change: In the first half, it would seem that there would be some catch-up, so I was wondering how prescriptive you could be there. And then, Jensen, just overall, with the change in administration that's going to take place here in the U.S. and the China situation, have you gotten any sense or any conversations about...
Speaker Change: You know tariffs or anything with regard to your China business Any sense of what may or may not go on? It's probably too early, but Wondering if you had any thoughts there. Thanks so much
We cut one quarter at a time.
Speaker Change: We are working right now on the quarter that we're in and building what we need to ship in terms of Blackwell. We have every supplier on the planet working seamlessly with us to do that. And once we get to next quarter, we'll help you understand in terms of that ramp that we'll see to the next quarter going after that.
Bye.
Speaker Change: Whatever the new administration decides, we will, of course, support the administration.
Speaker Change: and that's our that's our the highest mandate and then after that do the best we can.
Speaker Change: and just as we always do and so so we have we have to simultaneously and we will comply with any regulation that comes along fully and support our customers to the best of our abilities and and compete in the marketplace. We'll do all of these three things simultaneously.
Speaker Change: Your final question comes from the line of Pierre Farragut of New Street Research. Your line is open.
Speaker Change: Hey, thanks for taking my question. Jensen, you mentioned in your comments, you know, you have the pre-trainings, the actual language models, and you have reinforcement learning that becomes more and more important in training and in inference as well. And then you have inference itself. And I was wondering if you have a sense, you know, like a high-level typical sense of...
Speaker Change: Out of an overall AI ecosystem, like maybe one of your clients or one of the large models,
that are out there.
Speaker Change: Today, how much of the compute goes into each of these buckets? How much for the pre-training, how much for the reinforcement, and how much into inference today? Do you have any sense for how it's splitting and where the growth is the most important as well?
Well, today, it's vastly in pre-training of foundation model.
Speaker Change: Because, as you know, post-training, the new technologies are just coming online.
Speaker Change: And whatever you could do in pre-training and post-training, you would try to do so that the inference cost could be as low as possible for everyone.
Speaker Change: However, there are only so many things that you could do a priori.
Speaker Change: And so, you'll always have to do on-the-spot thinking and in-context thinking and reflection. And so, I think that the fact that all three are scaling is actually very sensible.
Speaker Change: based on where we are. And in the area of foundation model, now we have multi-modality foundation models and the amount of petabytes of video that these foundation models are going to be trained on, it's incredible.
Speaker Change: and so my expectation is that for the foreseeable future we're going to be scaling pre-training
Thank you for watching. I will see you next time.
Speaker Change: Post-training, as well as inference time scaling, and which is the reason why I think we're going to need more and more.
Speaker Change: Compute, and we're going to have to drive as hard as we can to keep...
Increasing the performance.
Speaker Change: by X factors at a time so that we can continue to drive down the cost.
Speaker Change: continue to increase their revenues and, you know, get the AI revolution going.
Thank you.
Speaker Change: Thank you. We'll now turn the call back over to Jensen Huang for closing remarks.
Jensen Huang: Thank you. The tremendous growth in our business is being fueled by two fundamental trends that are driving global adoption of NVIDIA computing. First, the computing stack is undergoing a reinvention, a platform shift.
from coding to machine learning.
Jensen Huang: from executing code on CPUs to processing neural networks on GPUs.
Jensen Huang: The trillion-dollar install base of traditional data center infrastructure is being rebuilt for Software 2.0, which applies machine learning to produce AI.
with AI Factories Manufacturing Digital Intelligence.
A new industrial revolution.
that can create a multi-trillion dollar AI industry.
Jensen Huang: Demand for Hopper and anticipation for Blackwell, which is now in full production, are incredible for several reasons.
Jensen Huang: There are more foundation model makers now than there were a year ago.
Jensen Huang: The computing scale of pre-training and post-training continues to grow exponentially.
There are more AI-native startups than ever.
and the number of successful inference services is rising.
Jensen Huang: And with the introduction of ChatGPT-01, OpenAI-01, a new scaling law called test-time scaling has emerged.
All of these consume...
Jensen Huang: a great deal of computing. AI is transforming every industry, company, and country.
Enterprises are adopting agentic AI to revolutionize workflows.
Over time,
Jensen Huang: AI co-workers will assist employees in performing their jobs faster and better.
Jensen Huang: Investments in industrial robotics are surging due to breakthroughs in physical AI.
Jensen Huang: Driving new training infrastructure demand as researchers train world foundation models on petabytes of video and Omniverse synthetically generated data. The age of robotics is coming.
Jensen Huang: Countries across the world recognize the fundamental AI trends we are seeing.
Jensen Huang: and have awakened to the importance of developing their national AI infrastructure.
Jensen Huang: The age of AI is upon us, and it's large and diverse.
Nvidia's expertise
Jensen Huang: Scale and ability to deliver full stack and full infrastructure let us serve the entire multi-trillion dollar AI and robotics opportunities ahead.
from every hyperscale cloud.
Enterprise, Private Cloud,
to Sovereign Regional AI Clouds.
On-Prem to Industrial Edge and Robotics.
Thanks for joining us today, and catch up next time.
This concludes today's conference call. You may now disconnect.