NVIDIA Corporation (NVDA) on Q3 2021 Results - Earnings Call Transcript
Operator: Good afternoon. My name is Jason, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA’s Third Quarter Financial Results Conference 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. 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 third quarter of fiscal 2021. Colette Kress: Thank you, Simona. Q3 was another exceptional quarter with record revenue of $4.73 billion, up 57% year-on-year, up 22% sequentially and well above our outlook. Our new NVIDIA Ampere GPU architecture is ramping with excellent demand across our major market platforms. Q3 was also a landmark quarter, both for us and the industry as a whole. As we announced plans to acquire Arm from SoftBank for $40 billion, we are incredibly excited about the combined company’s opportunities and we are working through the regulatory approval process. For today, we will focus our remarks on our quarterly performance. Starting with gaming. Revenue was a record $2.27 billion, up 37% year-on-year, up 37% sequentially, and ahead of our high expectations. Driving strong growth was our new NVIDIA Ampere architecture-based GeForce RTX 30 series of gaming GPUs. The GeForce RTX 3070, 3080 and 3090 GPUs offer up to two times the performance and two times the power efficiency over the previous Turing-based generation. Our second generation NVIDIA RTX combines ray tracing and AI to deliver the greatest ever generational leap in performance. Operator: Certainly. Your first question comes from the line of John Pitzer from Credit Suisse. Your line is open. John Pitzer: Can you hear me? Jensen Huang: Yes. John Pitzer: Yes. Hey, guys. Congratulations on the solid results. Thank you for letting me ask the question. Just Colette, going back to your commentary around Mellanox, it seems like you’re guiding the January quarter to about $500 million, which means the core data center business is still growing nicely, call it, 6%, 7% sequentially. I’m just kind of curious, when you look at the core data center business, I know there’s not a direct correlation to server business. But, we’re clearly going through a cloud digestion in server and core vertical markets, enterprise, the servers are weak. When you look at your core data center business, do you feel as though that’s having an impact, and this is sort of the digestion that you saw kind of in late fiscal ‘20 -- sorry, fiscal ‘19 into ‘20, but you’re doing it still growing significantly year-over-year, or how would you characterize the macro backdrop? Colette Kress: Sure. Let me clarify for those also on the call. Yes, we expect our data center revenue in total to be down slightly quarter-over-quarter. The computing products, NVIDIA computing product is expected to grow in the mid-single-digits quarter-over-quarter as we continue the NVIDIA AI adoption and particularly as A100 continues to ramp. Our networking, our Mellanox networking is expected to decline meaningful quarter-over-quarter as sales to that China OEM will not recur in Q4, though we still expect the results to be growth of 30% or more year-over-year. The timing of some of this business therefore shifted from Q4 to Q3, but overall H2 is quite strong. So, in referring to overall digestion, the hyperscale business remains extremely strong. We expect hyperscale to grow quarter-over-quarter in computing products as A100 continues to ramp. The A100 continues to gain adoption, not only across those hyperscale customers, but again we’re also receiving great momentum in inferencing with the A100 and the T4. I’ll turn it over here to Jensen to see if he has more that he would like to add. Jensen Huang: Yes. Colette captured it very well. The only thing I would add is that our inference initiative is really gaining great momentum. Inference is one of the hardest computer science problems. Compiling these gigantic neural network computational graphs into a target device really, really -- has proven to be really, really hard. The models are diverse when you count vision to language to speech. And there are so many different types of models being created that model sizes are doubling every couple of months. The latency expectations are increasing all the time, -- or latency is decreasing all the time. And so, the pressure on inference is really great. The technology pressure is really great. And our leadership there is really pulling ahead. We’re in our seventh generation tensorRT. We, over the course of the last couple of years, developed an inference server. It’s called Triton, has been adopted all over the place. We have several hundred customers now using NVIDIA AI to deploy their AI services. This is in the early innings, and I think this is going to be our largest near-term growth opportunity. So, we’re really firing on all cylinders there between the A100s ramping in the cloud, A100s beginning to ramp in enterprise, and all of our inference initiatives are really doing great. John Pitzer: Jensen, maybe to follow on there just on the vertical markets, clearly work-from-home and COVID this year kind of presented a headwind to new technology deployments on-prem. I’m kind of curious, if we expect sort of an enterprise recovery in general next year, how do you think that will translate into your vertical market strategy? And is there anything else above and beyond that you can do to help accelerate penetration of AI into that end market? Jensen Huang: Yes. John, that’s a good point. I mean, it’s very clear that the inability to go to work is slowing down the adoption of new technology in some of the verticals. Of course, we’re seeing rapid adoption in certain verticals, like for example using AI in health care to rapidly discover new vaccines and early detection with outbreaks and robotic applications. So, warehouses, digital retail, last mile delivery, we’re just seeing just really, really great enthusiasm around adopting new AI and robotics technology. But in some of the more traditional industries, new capabilities and new technologies are slow to deploy. One of the areas that I’m really super excited about is the work that we’re doing in remote work and making it possible for people to collaborate remotely. We have a platform called Omniverse. It’s in early beta. The feedback from the marketplace has been really great. And so, I’ve got a lot more to report to you guys in the upcoming months around Omniverse. And so -- but anyways, I think, when the industry recover, we serve -- our fundamental purpose as a company is to solve the greatest challenges that impact the industry where ordinary computers can’t. And these challenges are -- serve some of the most important applications in the verticals that we address. And they’re not commodity applications. They’re really impactful, needle-moving applications. So, I have every confidence that when the industries recover, things will get designed. Cars will be designed, and planes will be designed, and ships will be designed, and buildings will be designed. And we’re going to see a lot of design, and we’re going to see a lot of simulation. We’re going to see a lot of robotics applications. Operator: Your next question comes from the line of C.J. Muse from Evercore. Your line is open. C.J. Muse: You talked about in your prepared remarks limited availability of capacity components. You suggested perhaps a few months to catch up. Curious if you can speak to the visibility that you have for both, gaming and data center into your April quarter? Jensen Huang: Yes. Colette, do you want me to take that real quick and maybe you can help me out? Colette Kress: Yes, absolutely. Jensen Huang: So, C.J., first off, we have a lot of visibility into the channel, as you know, especially for gaming. And we know how many weeks of inventory is in what parts of the channel. We’ve been draining down the channel inventory for Turing for some time. And meanwhile, we’ve also expected a very, very successful launch with Ampere. And even with our bullish demand expectation and all of the Amperes that we built, which is one of the fastest ramps ever, the demand is still overwhelming. And this I guess in a lot of ways is kind of expected, the circumstances are -- it’s been a decade since we’ve invented a new type of computer graphics. I mean, two years ago, we invented and it set the industry on course to create the type of images that we see today. But, it’s very clear that the future is going to look something much, much more beautiful. And we invented NVIDIA RTX to do that. And it has two capabilities, one based on ray tracing and the other one is based on artificial intelligence image generation. The combination of those two capabilities is creating images that people are pretty ecstatic about. And at this point, it’s defined the next-generation content. And so when we -- it took us 10 years to invent it. We launched it two years ago and took our second generation to really achieve the level of quality and performance that the industry -- that they really expect. And now, the demand is just overwhelming. And so, we’re going to continue to ramp fast. And this is going to be one of our most successful ramps ever. And it gives our installed base of some 200-million-plus GeForce gamers the best reason to upgrade in over a decade. And so, this is going to be a very large generation for us is my guess. And then, with respect to data center, we’re ramping into A100. A100 is our first generation of GPUs that does several things at the same time. It’s universal. We position it as a universal because it’s able to do all of the applications that we in the past had to have multiple GPUs to do. It does training well. It does inference incredibly well. It does high-performance computing. It does data analytics. And so, it’s able -- the Ampere architecture is able to do all of this at the same time. And so, the utilization for data centers is -- and the utility is really, really fantastic, and the reception has been great. And so, we’re going to ramp into all of the world’s clouds. I think, starting this quarter, we’re now in every major cloud provider in the world, including Alibaba or Golan, and of course the giants, the Amazon, the Azure and Google Clouds. And we’re going to continue to ramp into that. And then, of course, we’re starting to ramp into enterprise, which in my estimation, long term will still be the largest growth opportunity for us, turning every industry into a AI, turning every company into AIs and augment it with Al and bringing the iPhone moment to all of the world’s largest industries. And so, we’re ramping into that, and we’re seeing a great deal of enthusiasm. Operator: Your next question comes from the line of Stacy Rasgon from Bernstein Research. Your line is open. Stacy Rasgon: You said that the extra week was contributing incrementally to revenue and OpEx. Can you give us some feeling for how much is contributing to revenue and OpEx in Q4? And does that impact, at least on the revenue side, differ, say between like gaming and data center? And then, how should we think about it impacting seasonality into Q1 as that extra week rolls off? Colette Kress: Sure. Let me try this one, Jensen. Yes, we’ve incorporated that 14th week into our guidance for both, revenue and OpEx. We will likely have incrementally positive impact on revenue, although it is tough to quantify, okay? Our outlook also reflects incremental OpEx for Q4 in primarily two different areas in terms of compensation and depreciation. And given that our employees with such a material power of our OpEx, it will -- it can be close to one-fourteenth of the quarter. Now, when we look a little bit farther, we should think about the incremental positive in both, gaming and data center from that extra week as there hopefully will be extra supply, but not likely as much as one-fourteenth of the quarter of revenue as enterprise demand is essentially project-based, and game demand though is tied to the number of gaming that gamers that might be shopping for the overall holiday. So, again, still very hard for us to determine at this time. Normally, between Q4 and Q1 there is seasonality in gaming, seasonality downward. But, we’ll just have to see as we are still supply-constrained within this Q4 to see what that looks like. From an OpEx standpoint, we’ll probably expect our OpEx to be relatively flattish as we move from Q4 to Q1. Operator: Your next question comes from the line of Vivek Arya from Bank of America. Vivek Arya: Thanks for taking my question and congratulations on the strong growth. Jensen, my question is on competition from internally designed products by some of your larger cloud customers, Amazon and Google and others. We hear about competition from time to time. And I wanted to get your perspective. Is this a manageable risk? Is the right way to think that they are perhaps using more of your product in their public cloud, but they are moving to internal products for internal workloads? Just how should we think about this risk going forward? Thank you. Jensen Huang: Thanks, Vivek. Most of the cloud vendors, in fact, I believe all of the cloud vendors use the same infrastructures largely for their internal cloud and external cloud, or have the ability to or largely do. And, there’s -- the competition, we find to be really good. And the reason for that is this. It just suggests that acceleration -- they make it very clear, that acceleration is right path forward for training and inference. The vast majority of the world’s training models are doubling in size every couple of months, and it’s one of the reasons where our demand is so great. The second is inference. The vast majority of the world’s inference is not on CPUs. And nothing is better than the whole world recognizing that the best way forward is to do inference on accelerators. And when that happens, our accelerator is the most versatile. It is the highest performance. We move the fastest. Our rate of innovation is the fastest because we’re also the most dedicated to it, we’re most committed to it, and we have the largest team in the world to it. Our stack is the most advanced, giving us the greatest versatility and performance. And so, we see spots of announcement here and there. But, they’re also our largest customers. And as you know that we’re ramping quite nicely at Google, we’re ramping quite nicely at Amazon and Microsoft and Alibaba and Oracle and others. And so, I think, the big takeaway is that -- and the great opportunity for us if you look at the vast amount of workload -- AI workload in the world, the vast majority of it today is still on CPUs. And it’s very clear that this is going to be an accelerated workload. And we’re the best accelerator in the world. And, this is going to be a really big growth opportunity for us in near term. In fact, we believe it’s our largest growth opportunity in near term. And we’re in the early innings of it. Operator: Your next question comes from the line of Harlan Sur from JP Morgan. Your line is open. Harlan Sur: Good afternoon. Thanks for taking my question, and great job on the quarterly execution. The Mellanox networking connectivity business was up 80% year-over-year. I think, it was up about 13%, 14% sequentially. I know there was upside in October from one China customer, but it did grow 70% year-over-year last quarter, and you’re still expecting 30% year-over-year growth next quarter. If I remember correctly, I think, InfiniBand is about 40% of that business; Ethernet Cloud is about 60%. Jensen, what are the big drivers, especially since we’re in the midst of a cloud spending digestion cycle? And I just saw that the team announced their next-gen 400-gig InfiniBand solution, which should drive another strong adoption cycle with your supercomputer customers. When does this upgrade cycle start to fire? Jensen Huang: Yes. Let’s see. Our data center business consists of supercomputing centers, which is small, high-performance computing, which is a much larger part of super computing, much larger than supercomputing, and then hyperscale and enterprise, which about 50-50. Of the data center business, the accelerated computing part is not very much associated with digestion than others. It’s much more associated with workloads and our new product cycles, the TCO that we bring in and AI inference, the type of models that the cloud service providers are deploying, whether they’re deploying new AI models, based on deep learning, and how much of it that we -- how much of those workloads that we’ve completed, the porting to our accelerators and ready it for deployment. And so, those are the factors associated with accelerated computing. It’s really about the apps, it’s really about the workloads and really driven by AI. On the other hand, the networking part of our business is more connected to CPU business because they’re much more broad-based. The networking part of our business is driven by this idea of new hyperscale data center architecture called disaggregation. It’s software disaggregation, not necessarily hardware disaggregation. Software disaggregation, where this type of software called Kubernetes orchestrate microservices that are deployed across the data center. So, one service, one application isn’t monolithic running on one computer anymore. It’s distributed across multiple computers and multiple nodes, so that the hyperscale data centers can more easily scale up and scale out according to the workloads and according to the demand on the data center. And so, this disaggregation has caused the networking between the compute nodes to be of all vital importance. And because Mellanox is the lowest-latency, highest-performance, highest-bandwidth network that you can get the TCO benefit at the data center scale is really fantastic. And so, when they’re building out data centers, Mellanox is going to be much more connected to that. In the enterprise side of it, depending on new CPU cycles, it could affect them. If a CPU cycle were to delay a little bit, it would affect them by a quarter, it would pull-in by a quarter -- it would affect them by pull-in of a quarter. And so, those are kind of the dynamics of it. I think, that the net-net of it is that it’s a foregone conclusion at this point that AI is going to be the future of the way software is run. AI is the most powerful technology force of our time. And acceleration is the best path forward. And so, that’s what drives our computing business. And the networking business has everything to do with the way architecture of data centers, cloud data centers, which is architected with micro services now. And that’s what foundationally drives their -- our networking work business demand. And so, we’re really well-positioned in these two fundamental dynamics because as we know, AI is the future and cloud computing is the future. Both of those dynamics are very favorable to us. 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 a question that was asked before in a bit of a different way. If I look at the core business, excluding Mellanox, the core data center business, it was up about 6% sequentially the past two quarters, and your guidance sort of implies up about that much again in January, which is certainly good, and there is some cloud digestion. But, of course you have Ampere still ramping as well, which should be a pretty good tailwind. So, there seems to be some offsetting factors. So, I guess, I wonder if you feel like your core data center revenue is still being constrained right now by some market digestion. And kind of how you sort of balance or handicap these two factors? Thanks. Jensen Huang: Our growth is -- in the near term is more affected by the cycle time of manufacturing and flexibility of supply. We are in a good shape to -- and all of our supply is -- informs our guidance. But, we would appreciate shorter cycle times; we would appreciate more agile supply chains. But, the world is constrained at the moment. And so, we just have to make the best of it. But, even in that condition - even in that condition, we’ve -- all of that is building for our guidance, and we expect to grow. Operator: Your next question comes from the line of Aaron Rakers from Wells Fargo. Your line is open. Aaron Rakers: Yes. Thanks for taking the question, and also congratulations on the quarter. I wanted to go back to kind of the Mellanox question. I know prior to the acquisition, Mellanox was growing maybe in the mid to high-20% range. These last two quarters, it’s grown over 75%. I guess, the simple question is how do you think about the growth rate for Mellanox going forward? And on that topic, we started to hear you talk more about BlueField and data processing units. I think, in your commentary, you alluded to server OEM design wins incorporating these DPUs. What are you looking at, or when should we think about the DPU business really starting to inflect and become a material driver for the business? Thank you. Jensen Huang: Long term, every computer in the world will be built like a data center. And every node of a data center is going to be a data center in itself. And the reason for that is because we want the attack surface to be basically zero. And today, most of the data centers are only protected as a periphery. But in the future, if you would like cloud computing to be the architecture for everything and every data center is multi-tenant, every data center is secure, then you’re going to have to secure every single node. And each one of those nodes are going to be a software-defined networking, software-defined storage, and it’s going to have per application security. And so, the processing that it will need to offload, the CPUs, is really quite significant. In fact, we believe that somewhere between 20% to 40% of today’s data centers -- cloud data centers is the capacity, the throughput, the computational load is consumed running basically the infrastructure overhead. And that’s what the DPUs intended -- was designed to do. We’re going to offload that, number one; and number two, we’re going to make every single application secure. And confidential competing, zero trust computing will become a reality. And so, the importance is really quite tremendous. And I believe therefore that every single server in the world will have a DPU inside someday, just because we care so much about security and just because we care so much about throughput and TCO. And it’s really the most cost-effective way of building a data center. And so, I expect our DPU business to be quite large. And so, that’s the reason why we’re putting so much energy into it. It’s a programmable data center on a chip, think of that way, and data center infrastructure on a chip. It is the reason why we’re working with VMware on taking the operating system in the data center, the software-defined operating system in the data center, putting bring it on BlueField. And so, this is a very important initiative for us. I’m very excited about it, as you can imagine. Operator: Your next question comes from the line of Ambrish Srivastava from BMO Capital Markets. Your line is open. Ambrish Srivastava: Yes. Thank you very much. Colette -- and I apologize if I missed it, but for Mellanox, do you expect it to get back to the growth trajectory on a sequential basis in the April quarter? And I’m assuming that the shortfall in the current quarter is from a pull-in from Huawei? Colette Kress: So, our impact to our Q4 guidance for Mellanox, yes, is impacted by a sale to a China OEM for Mellanox. That will not recur in Q4. And as we look forth into Q1 of April, we’re going to take this a quarter at a time and provide thoughts and guidance for that once we turn the corner to the new fiscal year. Jensen Huang: At the highest level, Colette, I think, the -- it’s safe to say that high-speed networking is going to be one of the most important things in cloud data centers as we go forward. And the vast majority of the world’s data center is still built for the traditional hyper-converged architecture, which is all moving over to microservices-based disaggregate --software-defined disaggregated architectures. And that journey is still in its early days. And so, I fully expect future cloud data centers -- all future data centers are going to be connected with high-speed networking inside. They call it east-west traffic. And, all of the traffic will be secured. And so, imagine building firewalls into every single server. And imagine every single transaction, every single transmission inside the data center to be high speed and fully encrypted. And so, pretty amazing amount of computation is going to have to be installed into future data centers. But, that’s an accepted requirement now. And I think, our networking business, Mellanox is in the early innings of growth. Operator: Your final question today comes from the line of William Stein from Truist Securities. Your line is open. William Stein: Great. Thanks for taking my question. You’ve given us some pieces of this puzzle. But, I’m hoping maybe you can address directly the sort of SKU-by-SKU rollout of Ampere. We know that we didn’t have a ton of SKUs last quarter. There were more in this quarter that you just announced. Now, you’re doing sort of this refresh it sounds like with double the memory on the A100. Is the T4 going to be refreshed? And if so, when does that happen? And, are there other, either systems or chips that are still waiting for the Ampere refresh that could potentially contribute to an extended cycle as we look at the next year? Jensen Huang: Yes. In terms of the total number of SKUs that we’ve ramped of Ampere, we’re probably somewhere along a third to a half of the SKUs at this point, maybe a little bit less. Yes, it’s less. The way that you could think through it, you could reverse engineer it, it is like this. You know what our gaming lineup looks like for desktops. And so, traditionally, we try to have a new architecture in every single segment. And we’ve not gone below 499 yet. And so, there’s a very big part of the marketplace that we’re still in the process of addressing. And then, the second thing is laptops. None of the Ampere architecture has launched for laptops. And then, there’s workstations. And you do the same thing with desktops and workstations and laptops workstations. And none of those have gone out yet. And then, there’s data center. And our data center business for cloud, you’ve seen some of the early versions of it, A100. But, then, there’s cloud computing for graphics, there’s cloud gaming. For those enterprise, edge enterprise applications, enterprise data analytics applications. And so, there’s a fair number of exciting new products we still have in front of us. Operator: That concludes our Q&A for today. I now turn the call back to Ms. Jankowski for closing remarks. Simona Jankowski: Actually, that will be for Jensen. Operator: My apologies. Jensen Huang: Okay. Thank you. Thank you, Simona. This was a terrific quarter. NVIDIA is firing on all cylinders. And the RTX has reinvented graphics and has made real-time ray tracing the standard of next generation content, creating the best ever reason to upgrade for hundreds of millions of NVIDIA gamers. AI, where software writes software no humans can, is the most powerful technology force of our time and is impacting every industry. NVIDIA AI again swept MLPerf training and now inference as well, extending our leadership in this important new way of doing computing. NVIDIA AI’s new Triton inference server, a platform that I will speak a lot more about in the future and a lot more frequently because it’s important, and our full stack optimized platform are gaining rapid adoption to operate many of the world’s most popular AI-enhanced services, opening a major growth opportunity. Data centers are the new unit of computing. Someday, we believe there will be millions of autonomous data centers distributed all over the globe. NVIDIA’s BlueField DPU programmable data center on a chip and our rich software stack will help place AI data centers in factories, warehouses, 5G base stations and even on wheels. And with our pending acquisition of Arm, the company that builds the world’s most popular CPU, we will create the computing company for the age of AI, with computing extending from the cloud to trillions of devices. Thank you for joining us today. I wish all of you happy holidays. And please do stay safe, and I look forward to seeing you guys next time. Operator: That concludes today’s conference call. You may now disconnect.
NVIDIA Faces Headwinds on GPU Pricing, HSBC Downgrades to Hold
NVIDIA (NASDAQ:NVDA) saw its rating lowered from Buy to Hold by HSBC analysts, who also reduced the price target to $120 from $175, citing concerns about fading pricing momentum in the GPU market.
HSBC had previously turned bullish on NVIDIA in April 2023, betting that the company's dominance in AI chips and its ability to command premium prices were underestimated. For much of the past two years, that thesis held up as NVIDIA surged on the back of strong demand for AI-related hardware.
Now, however, the story appears to be shifting. According to the note, HSBC sees no meaningful increase in average selling prices (ASP) between NVIDIA’s new-generation B200 and B300 GPUs, or between the GB200 and GB300 NVL72 rack architectures, suggesting that the company’s once-powerful pricing advantage may be plateauing.
The analysts also pointed out that NVIDIA’s forthcoming Vera Rubin platform doesn’t deliver notable performance improvements in terms of GPU density, sticking with 72 GPUs per rack—a number unchanged since the Blackwell generation. A substantial leap in specifications may not come until the Rubin Ultra release in 2027, potentially limiting near-term growth catalysts.
With pricing tailwinds losing steam and product specs leveling off, HSBC believes that earnings upside may now be capped, prompting the downgrade and lower valuation outlook.
NVIDIA Corporation (NASDAQ:NVDA) Leads with AI Advancements
- NVIDIA Blackwell Ultra AI factory platform is set to revolutionize AI applications, potentially increasing NVIDIA's revenue opportunity for AI factories by 50 times.
- The NVIDIA HGX B300 NVL16 system offers 11 times faster inference on large language models, indicating significant performance improvements over the previous generation.
- Financial metrics show a strong market position with a P/E ratio of 39.76 and optimistic price targets suggesting potential price increases of 52.67% and 61.15%.
NVIDIA Corporation, listed on the NASDAQ as NVDA, is a leading player in the technology sector, renowned for its graphics processing units (GPUs) and AI advancements. The company has recently introduced the NVIDIA Blackwell Ultra AI factory platform, a major leap in AI reasoning capabilities. This platform is set to revolutionize AI applications, enhancing training and test-time scaling inference, which are vital for improving AI accuracy.
The Blackwell Ultra platform, built on the Blackwell architecture, includes the NVIDIA GB300 NVL72 rack-scale solution and the NVIDIA HGX B300 NVL16 system. These systems offer 1.5 times more AI performance than the previous NVIDIA GB200 NVL72. This advancement is expected to increase NVIDIA's revenue opportunity for AI factories by 50 times compared to those built with the NVIDIA Hopper.
The NVIDIA HGX B300 NVL16 system is particularly noteworthy, offering 11 times faster inference on large language models. It provides 7 times more compute and 4 times larger memory compared to the Hopper generation. This makes it ideal for complex workloads, including agentic AI and physical AI, which are crucial for applications like robots and autonomous vehicles.
NVIDIA's financial metrics reflect its strong market position. The company has a price-to-earnings (P/E) ratio of 39.76, indicating investor confidence in its earnings potential. Its price-to-sales ratio is 22.13, and the enterprise value to sales ratio is 22.14, both suggesting a high market valuation. NVIDIA's low debt-to-equity ratio of 0.13 indicates a conservative use of debt, while a current ratio of 4.44 highlights its robust liquidity.
Analysts have set optimistic price targets for NVDA. Ruben Roy from Stifel Nicolaus set a target of $180, and John Vinh from KeyBanc set a target of $190, both significantly higher than the current stock price of $117.90. These targets suggest potential price increases of approximately 52.67% and 61.15%, respectively, reflecting positive market sentiment towards NVIDIA's future growth prospects.
Nvidia’s Blockbuster Q4 Results, but Stock Falls 2%
Nvidia (NASDAQ:NVDA) delivered yet another standout quarter, exceeding Wall Street’s expectations on both earnings and revenue while also providing a stronger-than-anticipated outlook for the current quarter. However, despite the impressive results, shares saw a more than 2% pullback in early trading on Thursday.
For the quarter, Nvidia posted adjusted earnings per share of $0.89, rising from $0.81 a year earlier, on revenue of $39.3 billion, marking a 78% year-over-year surge. Both figures came in ahead of expectations, with analysts forecasting $0.84 EPS and $38.16 billion in revenue.
The company’s data center unit, which accounts for the majority of its business, reported $35.6 billion in revenue, a 16% increase from the prior quarter, outperforming estimates of $34.1 billion.
Looking ahead, Nvidia projects first-quarter revenue of $43 billion, surpassing Wall Street’s $42.05 billion estimate. The company also expects a gross margin of 70.6%, reflecting continued strong profitability.
Nvidia emphasized that its next-generation AI Blackwell chips are already seeing billions of dollars in sales, as the company ramps up massive-scale production of AI supercomputers. The bullish guidance reinforced confidence in the ongoing AI-driven demand boom, despite rising competition from Chinese AI firms like DeepSeek.
Rosenblatt Reaffirms Buy on NVIDIA Ahead of Q4 Earnings
Rosenblatt analysts reiterated a Buy rating on NVIDIA (NASDAQ:NVDA) with a $220 price target, expressing confidence in the company’s upcoming earnings report and its long-term growth trajectory.
NVIDIA is set to release its fiscal Q4 2025 earnings on February 26, and the analysts expect a modest beat and upward revision to guidance, driven by continued strength in AI demand and robust sales momentum.
A key focus for investors will be updates on Blackwell, NVIDIA’s next-generation GPU architecture. The analysts anticipate management will reaffirm that Blackwell shipments will begin in fiscal Q4 2025, with demand continuing to outpace supply well into fiscal 2026. Shipments are expected to accelerate throughout the year, leading to a strong second half of fiscal 2026.
Despite rising competition from GPU and ASIC accelerator rivals, the analysts remain bullish on NVIDIA’s ability to maintain dominance in the AI and data center space, citing the company’s complex and highly valuable product roadmap.
Looking ahead, Rosenblatt sees 2025 as a pivotal year for NVIDIA, with the Blackwell ramp expected to be a key driver of growth. Investors will also be watching for further roadmap updates at the upcoming GTC event in March, where NVIDIA is likely to unveil more details on its evolving AI strategy.
NVIDIA Corporation (NASDAQ:NVDA) Faces Challenges Amid AI Competition
- NVIDIA Corporation (NASDAQ:NVDA) stock drops by 17% due to concerns over DeepSeek's new AI model.
- Citigroup maintains a "Buy" rating for NVDA, with a price target of $175, indicating a potential 40% upside.
- NVIDIA's strong client base and sustained demand for its GPUs and AI hardware suggest a resilient outlook despite market volatility.
NVIDIA Corporation, listed as NASDAQ:NVDA, is a leading player in the technology sector, known for its advanced graphics processing units (GPUs) and AI hardware. The company has been at the forefront of AI development, providing essential components for training and deploying AI models. Despite recent challenges, Citigroup has maintained a "Buy" rating for NVDA, with the stock priced at $120.07 as of February 2, 2025.
Nvidia's stock recently faced a significant decline, dropping by 17% on January 27. This was largely due to concerns over a new AI model from Chinese start-up DeepSeek. DeepSeek's R1 model, trained for just $6 million, competes with more expensive models from companies like OpenAI. This development has raised questions about the future demand for Nvidia's chips, which are crucial for AI applications.
The emergence of DeepSeek's cost-effective AI model has unsettled investors, leading to a 3.67% drop in Nvidia's stock earlier this week. The model's ability to compete with established offerings has sparked debate about the necessity of Nvidia's high-cost data center chips. This has created uncertainty about Nvidia's growth prospects, especially if major tech companies adopt similar AI training techniques.
Despite these challenges, some analysts remain optimistic about Nvidia's future. Citigroup analyst Atif Malik has set a price target of $175 for NVDA, indicating a potential 40% upside from its current price. This optimism is supported by sustained demand from major clients like Microsoft and Meta, who are boosting capital expenditure expectations due to supply constraints.
Nvidia's market capitalization is approximately $2.94 trillion, with a trading volume of 385.2 million shares. The stock has fluctuated between $119.19 and $127.85 recently, with a 52-week high of $153.13 and a low of $66.25. While the market reacts to DeepSeek's advancements, Nvidia's strong client base and continued demand for its hardware suggest a resilient outlook.
NVIDIA Maintains Momentum Amid Rising AI Competition, Citi Reiterates Buy Rating
Citi analysts reaffirmed a Buy rating on NVIDIA (NASDAQ:NVDA) with a price target of $175, emphasizing the company's continued dominance in the advanced GPU market despite emerging competition in the AI landscape.
Recent developments, such as the introduction of DeepSeek’s R1, a Chinese-made large language model (LLM) touted for its low compute costs and high performance, have prompted investor concerns about potential disruptions to NVIDIA’s GPU dominance. As a result, NVIDIA shares dropped more than 12% intra-day today.
However, Citi raised questions about the feasibility of these claims, highlighting that such advancements likely relied on advanced GPUs for fine-tuning and model creation through techniques like distillation.
While the emergence of competitors could challenge U.S. firms in developing cutting-edge AI models, the analysts pointed to the U.S.’s ongoing access to the most advanced chips as a critical advantage. In restrictive environments, leading AI companies are unlikely to shift away from advanced GPUs, given their superior cost-efficiency and performance scalability.
Further underscoring NVIDIA’s strong position, the analysts cited recent announcements of significant AI capital expenditures, including projects like Stargate, which reflect the sustained demand for high-performance chips. These developments reaffirm the need for NVIDIA’s advanced GPU offerings in powering AI innovation at scale.
With its market-leading technology and strong alignment with the growing AI-driven capex trends, NVIDIA remains well-positioned to capitalize on the ongoing expansion of the AI and compute markets.
DA Davidson Maintains Neutral Stance on NVIDIA, Citing Peak Concerns for 2025
DA Davidson analysts reaffirmed a Neutral rating on NVIDIA (NASDAQ:NVDA), with a price target of $135 on the stock. The analysis reflects caution regarding NVIDIA’s growth prospects, particularly as 2025 could mark a peak year for the company’s performance, with 2026 projections appearing challenging.
The analysts reviewed their initial outlook from a year ago, noting that many concerns raised at the time still hold. While NVIDIA has experienced significant growth, the analysts remain among the more cautious voices on Wall Street, with 2026 estimates positioned as the lowest among analysts. This skepticism stems from potential challenges in sustaining current growth trends beyond 2025.
In the shorter term, investor attention is expected to center on supply-side issues. These include export restrictions on sales to China and reported quality concerns with Blackwell GPUs. However, the analysts note that supply constraints could potentially extend NVIDIA’s growth cycle as demand remains robust.
Despite NVIDIA's recent successes, DA Davidson advises caution, emphasizing uncertainties in maintaining momentum into 2026 and beyond.
