NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

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Thank you. Good morning. Happy new year's, everyone, and welcome to JPMorgan's virtual fireside chat series at the 2026 Consumer Electronics Show. My name is Harlan Sur. I'm the semiconductor and semiconductor capital equipment analyst at the firm. Very pleased to have Colette Kress, Chief Financial Officer of NVIDIA here with us this morning. It's been a tradition past 12 years that have Colette and the NVIDIA team kick off the investor event here at CES. Colette's to start us off with an overview of Jensen's NVIDIA live event yesterday, and then we'll go ahead and kick off the Q&A. Colette, thanks for joining us today. Happy New Year, and let me go ahead and turn it over to you.

Okay. Let me first start. As a reminder, folks, to this discussion may contain forward-looking statements and investors are advised to read our reports filed with the SEC for information related to risks and uncertainties facing our business. And then I'll kind of get back to CES and our announcements essence that we were here yesterday doing. It's an important time for us to remind everyone about the transitions that are taking place in the market today. Those are 3 different transitions and all very important ones. The first 1 is one that we have talked about for several years regarding the need to move to accelerated computing. We're beyond the ability in our current development with using CPUs to advance that work in just the CPU. So folks are moving to accelerated computing throughout the world. Secondly, the development of generative AI is also a key transition. Those are things that are changing a lot of our work today, whether it be search or any of the social media or otherwise, generate AI is also taking part. But in the future, we also see the third and important transitions we move to agentic. Agentic AI is really where it is getting work done, work that can augment the work of many employees, many of our folks at home. All locations are really important, we think, going forward. Those transitions are penetration to that, and they're all occurring in creating an exponential growth in terms of our computer. So that's one of the opening statements that we just kind of want to remind in terms of what we see in AI going forward, but also seeing that we're doing in terms of accelerating today. This event highlights a lot of different focus on not only just AI, and AI for business, but also the work that we are doing in terms of with robotics and really thinking about physical AR going forward. But an important part of the discussion was talking about our next and upcoming version Vera Rubin. Vera Rubin, as we discussed, has definitely taped out and is ready to go, but this is an opportunity to help folks understand that we are well in good shape in terms of bringing this to market in the second half of the year as we are in full production. The important part of Vera Rubin, as we discussed that it is different chips. And I think it's important to talk about that -- what that means in terms of different chips, 6 different ships that have been extreme pre-designed to create a data center infrastructure at scale. This isn't about coming gear and talking about one different piece or discussing that says we are designing and orders building out the rack. It's more than that in terms of the design that every piece continues to be fought through its work between each and every single one of those different types of chips. So 6 chips that we're talking about. First, of course, that Vera Rubin, our GPU that is Vera, our CPU it's our next version of greatest piece of what we can do in scaling up in terms of our NVLink. It also takes us to Spectrum-X in terms of what we have in terms of the super mix, but also what we have with Bluefield and then also our switch for CPO. Six different chips have all been harmonized in terms of what we are bringing to market. We're excited in terms of all the different workloads that would be able to support but some of the key things that we have seen already is to understand that this is a full system that will essentially be able to take the time to drain down to 1/4 of what we had in terms of Blackwell. Additionally, you have the capability of 10x higher throughput and then thirdly, and an important part in terms of the inferencing phase that we say, it's actually 1/10 lower open cost throughout. So these parts and bringing that together, we are getting ready for that to continue to scale in the second half of this year. And then we'll be in full ramp as we move into the next calendar year as well. So those are some of the highlights, and we can talk about more of it in the discussions.

Yes. No, that was a great overview, Colette. And Jensen spent quite a bit of time yesterday focused on physical AI. And the team has framed AI physically as a massive opportunity by powered by platforms and bottles like Cosmos, Omniverse, Isaac, right? And vertical-specific frameworks like Group and Alpamayo, right? Customers are already here at CES. They're already bringing robots in many different verticals to market using Cosmos and Group. The Mercedes announcement yesterday is leveraging the Alpamayo-based reasoning model, right? Is physical AI -- is this already a financially material contributor to your data center revenues? And how should we think about the growth curve over the next few years for physical AI?

Physical AI is yet another great opportunity once we advance the agentic AI. And you're correct, they all are different types of models that are going to be needed for the physical AI. The important part of what we brought to market and what we discussed about is really the need for the open source model. And right now, if you think about the top proprietary models, the next in line is the formation of all of the open models and how important these are. Now these open models are important definitely for the enterprise and the work that they're doing, but being able to manage for physical AI, the abundance of modeling there coming up in store and what it was being designed, whether that be for research and whether that be entry to developing the content for them, those models are now in service and here today. So here on the CES floor, even here in terms of our offering, we have full. We're about visibility but also what you have in terms of automotive. Your question stems in terms of are we seeing that today? And yes, Mercedes is coming to market in terms of their very hard work that we have done over the last 8 years to move to a very high-end, self-driving capability in the car, really focused in terms of the safety and the lock. The Mercedes have now been able to take the lead as one of the safest cars that will be in the market. So yes, we are earning definitely revenue from our work in terms of Mercedes as well as many others that are using our platform, whether that be back in the data center, and that's an important piece to keep in mind the amount of data that is selective and put together in terms of the data here as well as what is also inside of the course as well. As we move forward, taking that to an area such as physical AR for robotics is also going to be extremely important. The learning, the simulation of ours of what we've seen in terms of automotive carries very nicely for the purpose in terms of what we will be able to do with robotics as well. So yes, achieve part of that, we see much work in terms of our Jetson platform, our Omniverse platform and then also now in terms of our open model helping these important parts of physical AI.

That's great. And you touched upon a very relevant topic in the opening remarks, which is this is a team is in production with your next generation Vera Rubin, an accelerated compute platform, on track to launch in the second half of this year in line with your aggressive product cadence, 6 chips, as you mentioned, in the Vera Rubin portfolio, initial performance relative to Blackwell is very compelling, right? 5x better performance, 3x better training performance. And as you mentioned, and most important to your customers the 10x lower potential cost per token. As you look at the strong demand curve ahead of you and we've all heard about -- we all track value chain, the supply chain. But if you look at the strong demand curve ahead of you, what are the product areas or categories of the supply chain that you could see constraining your shipments as you start to unlock Vera Rubin in second half of the year. Could it be 3-nanometer wafer supply? Would it be coLOS? The memory, any bottlenecks that you foresee as you think about strong demand ramp in the second half of the year?

Yes. I think it's right to indicate, yes, there's tremendous amount of demand that is out there for both the AI and telecomputing. And we have been focusing on the significant amount of demand. And then the need of what type of supply we'd have to purchase. Keep in mind, the work that we deal in terms of building any one of these data center infrastructure systems, from the very beginning to the very end. You could be anywhere from 3 quarters to a year to be completed. That means a lot of our supply purchasing is not taking place in terms of what we need for tomorrow, today. It has been in the works, in the works for a couple of years because what it takes is focusing not only on just our supply, but the capacity needs that they have wanted and that is an important part of our processes, thinking through every stable one of our generation and our future generations and working with our suppliers. We feel very solid about that in terms of what we see in this new calendar year and what we have in terms of supply. As we move forward, it's something to think about as more and more growth goes, how much more can our suppliers did. But we feel good in terms of what we have ordered, what we've have been confirmed for and in terms of our supply that we will take for this year.

That's perfect. Why don't we take a step back for a second as we enter the new year, and does it build the concern focus as it relates to NVIDIA in terms of how the market thinks about the engineered team and the trajectory of growth is as we step into the year, the market is always focused on. And by the time that we second to the year, we already have a pretty good view of customers' CapEx in being change, right? So the market -- as the market always is very forward-looking, right? And I think the market is starting to think about the infrastructure growth trajectory looking into calendar year '27, right? And if I go back to October of last year when Jensen talked about $500 billion of visibility backlog to found in '26, right? That's both on Blackwell and Rubin GPU fabs, right? And we know that lead times may lack scale based solutions are 9 to 12 months. And it takes a significant amount, as you mentioned, supply chain management and coordination, capacity buildouts, et cetera, right? But the best proxy, I think, for continued CapEx and infrastructure spending, buyer, customers, if you look at your customers' forecast and orders beyond '26, right, which I assume NVIDIA team is already focused on. I'm not asking you to quantify, but given what you see in your orders and customer forecast, are you already seeing a continued spending growth profile by your customers into calendar?

Yes. So let's go back in terms of our GTC D.C.. That was an opportunity to help you understand that the combination of Blackwell and Vera Rubin together is about a half a trillion through that period time of through '26. But the important part, correct, is thinking of now let's start talking about 2027. And you think that would take to stand up the compute and up a full stage data center. That is years to do so from the land power shell to finishing up the buildout to eventually in terms of putting in and compute and getting that ready. So where we see our customers and they can see an event like today, they know that Vera Rubin is here. There's already been discussions in terms of how can we think about the amount of demand and where they will put that in their land power cell that they have up and coming in terms of the year '27. So that's the right way to think about it. We're still working on in '26. There is still a shortage of demand, and they are still looking to are ther quick ads that we could also add in '26 to help fuel what we need in terms of our demand. So both of these things are happening at the same time, but this is being very hopeful to them. They have good understanding from an engineering, what's capable and now they can start thinking through the volume of what they will need for their data center builds. So yes, that is exactly where we're focusing on is on as well.

On the market concerns around an AI bubble, Jensen, and as you mentioned in your prepared remarks, right, you've articulated 3 compute platform ships that are all happening at once which should mitigate a spending level, right? And often feel like the market sort of message is. And first is the transition from CPU compute to GPU accelerated compute, right? I mean we're seeing this in so many traditional CPU-based compute workloads and dominated segments of the market where, over time, they're moving from CPU compute to GPU accelerated compute rate. Jensen always talked about this, but EDA, chip design software is a perfect example where most of the chip design software workloads were run on high-performance server CPUs not that long ago. But today, they are all -- many of them are running up GPU accelerated compute architectures. You see that in the simulation market. You see that in the data base markets and so on, right? So that's one of the first sort of transition, right, the CPU to GPU accelerated compute in the existing traditional compute base. The second driver is, as you mentioned, the strong adoption of Gen AI. And the third transition, again, as you mentioned, is agentic AI. And of course, the onset of new foundation models that will power things like physical AI, right? So we stand along all 3 of those compute platform shows like where are we in terms of the adoption curve contribution to your current data center revenue profile? What specifically looking into 2030, right, let's take a longer-term view on this, but looking into 2030, how are all of these 3 shifts, how are they going to profile into that sort of $3 trillion to $4 trillion of data center spending that the NVIDIA team is forecasting during that period of time?

Great set of questions. First, looking at the accelerated computing. Accelerated computing, it's already here, and many of us have seen it and working with it almost every single day. There's a massive transformation of how search is completed, recommender engines, and essentially almost all in terms of the consumer Internet and how we market through our 2 businesses and our consumers. That's an important piece. But keep in mind, it is going to be in multiple decades solution to try and get throughout all. There's a lot amount of moving to a software 2.0 and transitioning from CPU to software to a different form of accelerated computing to the software. So we're in the early parts of it. It's moving quite fast. Folks do see the great benefits for the accelerated computing and being able to manage with a significant amount of data there are, you're going to see some time moving forward. However, moving also in terms of our work that we see with generative and agentic AI. The important part of that also created in an exponential growth in the need for the amount of compute that's necessary. Because one of the very big part of moving to agentic was the long thinking, was what can I do to get a response on a very difficult challenging question and that additional long thinking takes a lot more inferencing demand and takes a lot more token generation as well. So we are also now seeing a surge in that demand as we move forward. And our vision can see looking at AI as we go forward, has nothing more in the early stages as we move towards these various statistics, data solutions that will augment a lot of the work that we do in our offices as well as we do in terms of personal life. So we know these big markets are driving a lot of this different demand. And in no side do we see any type of shortage or any type of stopping from that. There's a lot more work to get completed. And the world as a whole still has to get that completed, not just here in terms of some parts and what we see here in the United States. We have a lot of different sovereign AI going on and so that we have many, many different industries. You have to go industry by industry. You can look at social media, but you have to look at health care, you have to look at automotive, you have to look at industrial, manufacturing. All of these different have unique ways for a perfect work that has to go transition and can be introduced in terms of AI as well. So a lot still to go. And why we indicated that by the end of the decade, we are definitely going to be up there in the multiple, multiple trillions, in the 3% to 4% of the amount that we'll be able to spend in terms of building out the accelerated computing and the AI types.

Maybe more near term kind of focusing on calendar '26. Going back again to Jensen's comments at GTC back in October when you talked about just $0.5 trillion of revenue is in those backlogs of cumulative Blackwells and move in shipments to '26. Obviously, as you move forward in time, you continue to get updated forecast and orders. Ex China, let's talk about China a little bit later, but ex China, has that $0.5 trillion worth of visibility and backlog number through '26 continue to improve. And at what point are you supply constrained and need to push any more orders into.

So the demand as we see continues to increase as folks are to looking to enable more compute for a lot of areas, the long, tough time and thinking. And so we see this every single day and since our time that we said $0.5 trillion, of course, we've seen new announcements of new deals, new different both focused in terms of the CSPs, the law makers as well as many of our new cloud looking to add more on to that. So yes, more has occurred, and we are now starting to see folks work in terms of providing the orders. We have orders for Vera Rubin and focusing more and more in terms of thinking out a full year of volume, what you may need in terms of Vera Rubin. So we're in a great position in getting better understanding. We've worked over the many, many years that has the more insight that we provide them in terms of our infrastructure is there, the easier it is in terms of the planning and process of that. So their demand needs are quite strong, and we are definitely in that process. So yes, that 500 -- that $500 million has definitely gotten larger. And now we'll probably look in terms of next year as well to start building up in terms of all the different demands that we have there. But we cannot say anything more than demand is quite strong.

That's great. No, that's exactly what we're looking for, and that's exactly what we thought. Maybe switching gears because Jensen and the team did a great job, and you did a great job of laying out the performance specs, as I mentioned to you before, right, 5x inferencing performance on Vera Rubin versus Blackwell. That's on the inferencing side, 3x better training? And then what's most important is the economics to your customers and you guys are driving 10x lower cost per token on Vera Rubin versus Blackwell, but I think the market has gotten a better appreciation for -- you talked about codevelopment and as you bring more systems and rack scale solutions to the market. It is a solution that is optimize not only around compute, it's optimized around compute. It's optimized around networking. It's optimized around storage and networking right. And so let's talk about networking, right? And lots of focus on networking lately, especially as NVIDIA and the initially transitioned to rack-scale solutions. There's a significant step-up in networking dollar content, given the scale of connectivity with your NVLink networking and switching portfolio, networking attached to your compute revenues was around 19% in your fiscal Q3 of last year. And we define networking attaches networking revenues divided by competed revenues, right? That was about 19% in Q3, at up to 21% in the July quarter. So on average, about 20% networking attached to your rack scale compute systems, here then the average attach over the prior 9 quarters, which was around 7%, I think, due to the scale-up adoption, right as we move to rack-scale. Looks like you continue to also get traction on spectrum ex your Ethernet product line, is 20% of baseline on networking attached? And as you drive more spectrum and your recently announced Spectrum-6 platform and you've got some GS for scale across, maybe the mix trends move more towards below the mid-20% range in mid- to longer term, right? I'm not sure, but I wanted to get your views on that.

Yes. It's a great way to start here talking about our network. We can definitely discuss where we've been historically and where we see going forward on the networking. One of the ways that we have been looking at the networking is how much in terms of when they are buying the full systems, which always all of them are, how many of them are attracting in terms of networking. And that's a different than looking at it from a dollar perspective, but just the attach rate. It is -- that is a very, very clean metric to understand. That number is nearing 90%. 90% are attaching strong form of all the networking included in there. Let's remind folks that as our networking business is #1 in the world. From moving to a very, very small scale. But now with the full development of all different types of switching capabilities, best agreed in terms of NVLink. Nobody has even figured out how to even do a lot of what we've done is really establishing both adoption of not only our InfiniBand, which has been a important part for super computing for decades and decades, it is world-class, but the quickness of providing those key features in Ethernet and the adoption of our Ethernet for their businesses as well has been a huge success kind of stepping back and looking at this AI important way. It's not enough to just have a GPU check, it's not enough to how to base. You're missing such an important part of what the networking does to capture the capabilities of scaling the multiple and multiple ones together, but also dealing with the complexity of traffic and the complexity of responses that you need at some point, we needed training and some point we may be able to manage that all with all of our different inferencing platforms with our networking has been a huge success. So even as we go forward and move to Vera Rubin, already working at some of the most important capabilities and how important that networking has been there. They are also part and focusing in terms of our work in terms of the switch for CPU. That's been an important part of those to know the amount of savings and capabilities that you can establish through a CPO environment, and we're going to be excited to go to market for them as well. But really looking at what we see, it's very interesting. Even if they have a part of our compute, very common in terms of networking is still being chosen for different systems. Even if they have one of their own ASICs, they will often use our switching capability as well. So we're in a full design at end-to-end, and we're really excited in terms of how the networking has also been established within Vera Rubin.

Yes. And as a reflection of the traction on networking the team announced its -- you've always been a leader in band switching, right? And as your customers were clearly signaling to the NVIDIA team that they were moving to more of an Ethernet-based switching from the team bought the market has to go your Spectrum Ethernet switching platform. That went from like 0 to $10 billion to annualize in like record time, right? And I think that last you updated us your annualized run rate on Spectrum X was like $10 billion annualized. I think that was in the July quarter. And the October quarter, that looks like that, that stepped up to sort of annualized run rate for your Spectrum some platform. Jensen and you and the team announced their next-generation Spectrum-6 platform, right? This is 120 terabits per second throughput switch, right? One of the fastest switches in the world. You're bringing that to market with Vera Rubin, right? So if you think about the $12 billion, $13 billion sort of annualized run rate in the October quarter, you've got a new platform coming out of Spectrum-6. You look at your order book for Vera Rubin. Like where could this number on Spectrum be as we move through -- as we move through next year?

So not getting a forecast going forward, but to understand where we already are in terms of the attachment. We're going to see something resonate in terms of our growth in terms of consumer and our growth in networking data time. The only difference that you do have is just the timing of when each of those systems are put together in a full data center infrastructure that they're doing. You may have -- parts of that networking is the first things that are put in place in terms of the data center and with some of the last part of the data center as networking, that's the only thing that really changes the growth. But so we are expecting nearly these things, not more of an attach rate in terms of what we are seeing in networking and growth moving forward.

Great. And then maybe switching over to China. I know you've got some questions yesterday in the financial analyst Q&A. But following the U.S. government's approval of the H200 sales into China, it appears customer interest actually looks very strong way. So the question is, has the team started receiving orders from approved China entities for the H200? More importantly, how rapidly can the team start shipping H200 to these customers? And how should we frame our kind of revenue opportunity over the next 12 to 24 months? What I remember Jensen had previously last year quantified the China revenue opportunity for calendar '25, that $50 billion growing at a 50% CAGR, right? 50% growth implies $75 billion of potential revenue demand for NVIDIA this year. Is that how we should think about the China revenue and growth profile and opportunity?

Great question. Let's first talk about the H200. We're very pleased that the U.S. government saw that this was the right opportunity for us to fairly be able to compete worldwide and providing a really good product to China. And that's what this is all about. The ability for us to ship H200 to our customers still requires a license from the U.S. government and the U.S. government work tediously right now on that process in order for them to determine the licenses for the customers. So the customers have requested the licenses, and we are now awaiting that part of that. But also on the same side, we have heard from these customers from a demand perspective. That's important for us so that we can prepared as those 2 things come together. The POs and the completion of the licenses with the U.S. government will set us on our way to begin shipping the H200 to China. We hope that, that gets done soon. But again, it's not all something that we can right now control, but we do are very pleased in terms of the U.S. government's decision to do that. So we're going to wait and see what will happen. It kind of steps back though and says, what is the demand in terms of China, it's a very, very important economy and has a tremendous amount of strong engineers and AI engineers compared to also what we see here in the U.S. So it's also a very big business as Jensen articulated, and it's not a static business. It's going to grow very similar in terms of what we are seeing here in the United States. If we can continue selling, going forward with any of those different licenses that U.S. government has. So more to be determined at that, but let's just wait to see how we can get our H200 out.

Got it. And then on the recently announced a nonexclusive licensing deal with Grok, Grok was focused on this SRAM-based, high-throughput inferencing engine. Very good for low user count and low model parameter influencing, seems like more of an enterprise-focused solution versus NVIDIA's inferencing fronting solutions, which focuses on very high user cloud, massive contact input capability, right, more targeting foundational model developers. I wanted to get your views on the rationale for the Grok transition? And how NVIDIA thinks of integrating their technology into your product road maps and target markets?

We're very pleased to both have the Grok IP with us. And that's what we created with an IP license stemming from Grok and their pieces. But the other most important part of it was an exceptional team that has now joined us as well. You are correct, their work in terms of inferencing, low-latency inferencing has been a lot of work that they have done. We're seeing tremendous engineering horsepower to do so. We found it is quite exciting and something very similar of our thoughts and work going forward as well. Bringing them onboard with that IT were excited in terms of what the teams could work together. So excited we got it done before the holiday. We have that completed and we're already with -- many of them are already with us beginning that work. So stay tuned. We don't have anything yet in terms of the exact timing when something will come to market but this is an important area. The complexity of inferencing, the size of inferencing interest market and different needs there's going to be and being such an exceptional team, we will be able to put something great together.

In terms of some of the market concerns that we continue to hear about, right, and one of them is the concern around the gap between a few of the foundational model builders and the current financial profiles and the data center compute capacity, right, that they've committed to over the next pages, OpenAI, Anthropic, et cetera, right? They're committing to a lot of capacity to you, competitors, some of the large hyperscale. Obviously, these AI labs will have to raise money, right? So how do you think about the risk to NVIDIA's business?

The model makers are very both foundational model makers, but also in terms of open source models as well. Most of them, if you look at them as a whole are being a very methodical piece by piece as they continue building a new training model. Okay, let's move to the inferencing and now let's get started for my next and moving in that methodical way. Many of them have had and worked in terms of how do I source to raising of cash, the raising of equity, the combination of the 2 and how do I work that carefully either with the funds or looking at it in terms of on theirselves. I think a lot of that is very solid diligence in terms of what we'll probably see continue going forward. They are essential. These foundational models are essential from a concept's perspective in terms of what we're going forward. So working and forming and storming with how to get that completed. I think it has gone very well. Sure. They're looking in terms of long term to help us understand. This is not our ability to complete AI in the next couple of years. This is decades. So they may talk about it in terms of gigawatts of size as we go forward. But the reality is, it's really about the year by year or quarter by quarter, how do they need to build, where do they need to build? Are they in the research side? Are they looking in the inferencing? And I think that process is fine. Many of them are also with the CSPs. That's a very big help for them. Their -- quality of what the CSPs can provide for them so that they can concentrate on building up their models is a great combination, and we're happy to support that. And many of the work that we are doing is through the CSP and therefore, the model makers is sold, whether those CSPs be in their cloud or some of our long-standing tremendously great CSPs that we've had. It's working quite diligently in terms of all that work. So I think we're going to see more of that to come. But again, we just have to take this day by day, step by step and start to rethink about what they're planning to put together.

Colette, we're at the Consumer Electronics Show. And the one thing that we noticed was a distinct absence of new GeForce gaming platforms this year. And then I guess the question to you is are there concerns of continuing supply of DRAM and HBM memory for gaming? How are you prioritizing allocating these components, gaming versus data center? Do you think that there is potential for demand destruction in the seasonally stronger second half of the year given that especially DRAM pricing looks to continue to increase to the remainder of this calendar year?

Our gaming business has been a homerun where our representation with our gamers continues to be tremendously strong and coming out with what we had with Blackwell was also hitting great strides. At the very beginning, we underestimated in terms of that growth. And that growth was so fast at the very beginning, but we have now brought that up to good level. But given our size of where we are as a percentage of our gaming markets, we're going to contain some both of prioritization, what will they need as we go forward. But still more in terms of later on in terms of this year and next in terms of to focus. But the best part that we're pleased about is these platforms and enables creative and AR type of platforms that they can use are really an important business model. So stay tuned as we think through, demand is, again, quite strong. And we're going to try and make sure it will not serve as much as demand as we can.

And then my last question, and I appreciate the time spent here. You've guided to mid-70s gross margins, while acknowledging, right, to the potential for rising input costs looking into this year levers matter the most to compact the margin, you get mix? Is it pricing, cost downs? Is it supply chain efficiencies? And where are you least willing to compromise as you think about on these levels?

Yes. It's always an interesting discussion on all the gross margin piece of that. It really showed a focus of us not just getting the confuse out, but doing it very in a great position, both with our manufacturers, our suppliers and in terms of our internal teams in terms of how we can do this well. We have split very close right now at that mid-70s right now. We don't want to look at this as, yes, we're here to grow, grow, grow that higher. We are here to keep what we said as it is it's mid-70 days right now as we go forward. It takes a lot of different banks. When you work at the complexity of the system, you are focusing in terms of every last patient component. We have already done a significant amount of reordering. We do understand what it took for the capacity of many of our suppliers and we're very supportive the many different suppliers that have pulled that together. But that now moves us working together with manufacturing. How do we improve that cycle time? How did we think about improving all of the different focus of the business as a whole? Not only can we do better and focus on that cycle time, we did also improve the cycle time of them just getting that to customers and the faster the customers. Remember, as we move into this new year, we still have a combination of different platforms that we're building. It's not just one product. And that will both enable and also be a mix, so we have to keep in mind as we move into this new year. So right now and what you've seen all of our steps for Vera Rubin as well as what you see with GB300, very serious in terms of that process and getting that together. So we do feel that confidence that will also be something that we can work well. But let's not look at it as something easy. We will continue to work to stay about that same page.

Absolutely correct. We're just about out of time. I want to thank you as always, for your participation and your support. We look forward to strong growth ahead this year for the NVIDIA team and another solid year of execution by the team as well. So thank you very much for your participation and support.

Thank you so much. Have a great day.

Thank you.