Intel Corporation (INTC) Earnings Call Transcript & Summary

Good afternoon, and welcome to this afternoon session. I'm Vivek Arya from the Bank of America semiconductor semi-cap equipment team. Really delighted and honored to have Sandra Rivera, Executive Vice President of Intel's Data Center and Artificial Intelligence team, joining us this afternoon. We will start with an exciting disclosure statement from me that Intel asked me to read. Then I will just turn it over to Sandra for some opening comments, and then we'll get into Q&A. But please feel free to raise your hands if you have anything that you would like to bring up. But from an Intel disclosure perspective, please note that today's discussion may contain forward-looking statements that are subject to various risks and uncertainties; may reference non-GAAP financial measures. And please refer to Intel's most recent earnings release and annual report on Form 10-K and other filings with the SEC and for more information on the risk factors that could cause actual results to differ materially and additional information on Intel's non-GAAP financial measures, including reconciliations where appropriate, to the corresponding GAAP financial measures. So after that exciting introduction, Sandra, maybe over to you, really appreciate you joining us this afternoon.

Thank you, Vivek. Thank you for having me here. So let me just start out with maybe some opening remarks, and then we'll get into the Q&A. So I just wanted to maybe give a broad backdrop of the market opportunity that we have in front of us. And first is that we are operating in a large and growing TAM. The amount of data that continues to be generated in the world that needs to be processed, moved, stored and acted upon just continues to grow. And so the amount of computing capability that we need to deliver to the world continues to grow. So it's wonderful to know that we have a large market opportunity that we are participating in. Of course, we'll get to AI. AI just, again, is a rising tide that increases the amount of compute required in the world. We are absolutely focused on, frankly, I'll just say, doing fewer things better. And so when we have looked at our CPU portfolio, our GPU portfolio and the overall complement of heterogeneous architectures that we have, we have been very focused on execution, execution, execution and ensuring that we make and meet customer commitments. And this is an acknowledgment clearly that, in recent years, we stumbled a bit, had some setbacks. We have recommitted maniacally to ensuring that we make and meet customer commitment. And our road map, certainly on the CPU side, is on track and delivering all key milestones and feel really good about all of our leading indicators moving forward. And from an overall portfolio perspective, we have, within the Data Center and AI Group, all of the data center technologies required to address what is a very complex set of workloads, clearly, not just AI, but networking, HPC storage, high-performance computing. We have the complement of CPUs and GPUs and AI accelerators, FPGAs and IPU capability in that portfolio, all brought together with software as that homogenizing layer. So we have the hardware and the software required to meet customer demands. And we feel, again, really good about the market opportunity and the expansiveness of the TAM. And from an AI perspective specifically, that idea that we unlock value through the software and through the rich stack of software tools and the tool chain and the developer enabling that we've committed to for decades where we've led the market, in many ways, through technology transitions and leading-edge technology capability, that full complement of capabilities being brought to the AI opportunity is something that, again, we see as market expansive and a tailwind in terms of the market opportunity. So just specifically, looking at what I'll call the AI continuum, our focus is bringing AI to the masses. It's making the affordability and the economics work for everybody. And we certainly see a lot of interest in the very largest language models, of course, the GPT-3, GPT-4 types of language models. Clearly, there is a lot of excitement and very real requirements in the market to be able to address that type of capability. But AI is this complex and vast set of workloads. And we do see that you need to have capabilities in the cloud, in the edge, of course, in the enterprise and then all the way out to the client devices. And the ability for us to continue to integrate AI capabilities in all of our computing platforms is something that we think is highly differentiated and highly valued by customers. And when you look at that continuum, cloud, enterprise, edge to client, we have heterogeneous architectures to address that market opportunity, starting, of course, with the ubiquity of our CPUs, both in the data center with Xeon but also out to the client. In the data center, we're on our fourth generation of integrated AI capability. We started years ago with AVX2, then enhanced that to AVX-512, then moved to VNNI with our third-gen Xeon and then, most recently, with the Advanced Matrix Extensions, AMX, integrated accelerator into 4th Gen Xeon. On the client side, we had the integrated VPU that provides really a market-leading capability and we believe will be the most pervasively deployed PC computing device with integrated acceleration once that product ramps later this year. From an overall perspective, after you get past the front end of data management, data processing, data cleaning, you move on to, of course, the training phase. And in that training phase, you have small- to medium-sized models that the CPU is actually well suited to address. When I say small to medium, it's 10 billion parameters and less. And so you have the CPU on the front end, doing all of that data prep work. Xeon really does a great job there and even the market leader in GPUs has selected 4th Gen Xeon as their platform for the CPU head node. But when you get to the model training, some of those small- to medium-sized models, and typically the ones you see in the enterprise, the CPU does actually a very good job there. But for the larger models, you need a much more parallelized architecture, the domain of GPUs and AI accelerators. And this is where we have our own GPUs, GPU Max as well as Gaudi accelerators to address that large language model capability for model training as well as inference. And that, I would say, is the $100 billion-plus size of parameters. On the edge, this is where, again, in the enterprise and deployment of those models, you do fine-tuning, retraining, all of that distributed inference, again, large footprint for us to address with CPUs. But increasingly, we see an opportunity there with GPUs. We have GPU Flex there. That's a smaller-footprint, lower-power edge inference device that does media processing, that does cloud gaming, other types of VDI, types of workloads on-prem, where our GPU Flex is well suited. And then out to the client devices. Of course, we have not only the integrated AI with our CPUs but also Discrete Graphics with the Arc brand. So a complete portfolio from the cloud, to the enterprise, edge and out to the client, and all of that brought together by the software. The software really is the unlock of the hardware, and we can get into a lot more discussion in terms of the richness of the software stack and how we unlock value with developers and deliver fast time to productivity through the software stack.

Excellent. Thank you, Sandra. Very comprehensive. Just maybe one kind of near-term question, and then we'll go through the industry structure. So near term, Intel, I think, recently said that you expect Q2 to be at the high end of guidance. And I think data center is part of that. So could you give us some more color, right, what in the data set is doing better than you thought and just kind of a general state of the union on what you see from a demand environment perspective?

Yes. So the year is shaping up to be pretty much as we expected coming into it. We launched 4th Gen Xeon at the beginning of the year, in January. Actually, our customers helped us launch that platform where we see clear leadership vis-à-vis competition is in those high-growth areas of AI, networking, HPC security, storage applications. And so lots of opportunity there. And so when Dave talked about the health of the business, it was both in the server data center side as well as the client side and just being more optimistic that we could be in the top half of the range that he gave. And the linearity of the business is also healthy from a cash flow perspective and what we're seeing in terms of customer demand. So we feel good about the way that the first half of the year has panned out and have been cautiously optimistic about the second half because we clearly over index on enterprise workloads where we have a very strong market segment share position and on China where, again, we have a strong brand, a strong market segment share position. And so as the customers are perhaps looking at their second half and balancing what they feel comfortable committing to in terms of both on-prem deployments -- and on-prem could be cloud infrastructure as well. I mean, typically, you have cloud on-prem as well as in public clouds and what they're moving to the public cloud. We have reasons to be still cautiously optimistic about that second half. But right now, we've been working through a lot of the inventory burn issues, particularly in the enterprise side. And we think that we start to see a little bit more movement in the second half, probably more in Q4. But first half has played out the way we expected it and feel good about our position there. Again, server and enterprise and client second half, seeing some reasons to think that things might come in a bit healthier but still being cautious just because we've had some inventory burn issues to get through. And I think that some customers are still being a little bit tentative in trying to decide where they make their big CapEx investments.

Got it. So the near-term excitement, is it kind of cloud? Is it enterprise? Is it China? Like, was there one factor that stood out to give you a little more optimism about Q2 data center?

Well, I'm not saying anything more than what Dave said. But the performance of the business in the first half was as we expected it. We have a very strong position in enterprise. But they were burning through more inventory, so that was a bit depressed in the first half than what we had planned for. Our position in the hyperscalers is actually quite strong. We continue to see our CSP customers deploying with 4th Gen Xeon. In fact, just last week or the week before, Google launched their C3 Sapphire Rapids 4th Gen Xeon instance with our IPU. So that was a product that we codeveloped, codesigned with them. And we will continue to see the hyperscalers rollout 4th Gen Xeon-based instances throughout the year. And actually, the pipeline for 4th Gen Xeon is quite healthy. We have over 600 designs won. We have 400 that are already shipping. And every single large cloud service partner in the world is going to be deploying on 4th Gen Xeon. And so we'll see that continue to play out throughout the year.

Got it. Now kind of the big picture question, Sandra, is that there seems to be this kind of zero-sum game between the CPU and pick your choice of accelerator. And of course, Intel has many accelerator options also. Is it as black and white as that? Like, if the accelerators then -- sorry, does the CPU just have to lose and disappear and go away?

Yes. It's a great question, and we don't actually see it as an either/or. So we see AI as more of a rising tide than a balloon squeeze. And I think in the near term, certainly, the growth rate for GPUs is outpacing the growth rate for CPUs, and we expect to see that throughout '23. But as I was describing earlier, when you look at who are the purveyors of the very largest language models and who can afford tens of millions, over 100 million, to train a unique large language model, there's not that many companies in the world that can actually afford to do that or want to do that. We see so much of the growth opportunity happening when you actually get to deployments. And typically, enterprises want to train on their own data. They want to do that in their own secure data perimeter. They want to contextualize the queries around, again, their data sets, their acronyms, their unique domain-specific types of capabilities. And this is where an example recently with Boston Consulting Group, we were able to work with them to train on certainly, large language models, open source models, the BLOOM 176-billion parameter model using Gaudi. But when we went to deploy on-prem and they had 50 years' worth of data, they wanted to do that in their contextualized environment with their own data set in their secure perimeter, and we were able to do that in less than 12 weeks. And they just see so much value in that time to productivity and the security of again having their data set trained in a way that isn't putting things up in a public model or in a public forum. So I think there's just so many examples like that, Vivek, where the AI tailwind, I think, really will be market expansive for everybody. And it's a big market. We're in the early innings, right? There is so much opportunity out there, and we want to be the company that customers trust for their broad scale deployments, particularly as we move into that inference and fine-tuning and retraining stage of where we are with that continuum.

Got it. How is the outlook on Sapphire Rapids? Because I think you mentioned that it's being really targeted at the fastest growth, right, workloads. Obviously, AI is one of them. So if we kind of fast forward and we are having this fireside a year from now, how do you think Intel would have done in the AI CPU side versus your competition with Sapphire Rapids?

We are holding our own. We feel really good about where we're performing with 4th Gen Xeon. So we had projected that we would be shipping about that 1 million unit mark by the middle of this year. We're still on track for that. And while we over index on those high-growth workloads in terms of performance leadership and power efficiency and per TCO vis-à-vis competition, we still address a broad range of workloads beyond just those highest-growth ones with a highly performing, highly versatile CPU platform. And a lot of the capability really comes from the software optimization. I mean there's so much that we do, investing in our software resources and engaging directly with customers and doing that optimization work, that gets you significant improvement not just from an overall performance perspective but a performance per TCO perspective. We had one example recently. When a customer was doing database compression, Microsoft SQL Server 2022 database compression, with integrated QuickAssist Technology, we were able to demonstrate that you can go from having 50 servers running that workload to 29 servers because it's much more efficient. And that was a direct comparison of one of our 32-core 4th Gen Xeons to the competition's latest 4th Gen 32-core systems. So we do see that the approach that we've taken with Sapphire Rapids, with 4th Gen Xeon, integrating those accelerators not just for AI but their networking capabilities, is bringing real value to customers. And so we're tracking to our goals for the year. And a year from now, I think that we'll have demonstrated that 4th Gen Xeon is a very competitive product, that the platform differentiation we've had with the health and the quality of being able to drive those memory transitions, DDR4 to DDR5, the interconnect PCIe Gen 4, PCIe Gen 5, the CXL capabilities, that having the quality of the platform and the deployability from day 1 when we launched 4th Gen Xeon really will have proven to be a big differentiator. But in terms of where I'll be sitting a year from now, I will have delivered 5th Gen Xeon at the end of this year on time, on spec. And customers are pretty excited about that drop in performance boost they get to the existing platform in addition to or expanding from 4th Gen Xeon. And then I will also have delivered Sierra Forest, our efficiency core product; and then I guess, 6th Gen Xeon; Granite Rapids will be shortly after. So I will be in a much, much better position in terms of real strong leadership across the breadth of the portfolio. And customers are leaning into that. Today, they have samples. They're doing the volume validation with us on not just 5th Gen Xeon but the Sierra Forest and Granite Rapids for next year. And the health of the product is great. And so all the leading indicators are really, really strong. And so I'm looking forward to a year from now because I know I'll be even stronger here than I am today.

Sandra, what do you think is that piece where Intel is kind of putting the most focus? Is it maybe the answer is all of the above? Like process, is it architecture? Is it features? Is it just that, once you're kind of knocked off as the incumbent, it just takes some time to get back? So which of those things do you think Intel is working on the hardest? And what does it need to do so that a year from now, right, or whatever, 2 years from now, that you will be in a place where we are not seeing those kind of market share changes, right, against your CPU competitor?

Yes. Well, process technology, for sure, is a huge focus. And if we look at what constitutes product leadership, it is a combination of process technology as well as architecture and engineering. And on the process technology side, we are absolutely executing to Pat's vision of 5 nodes in 4 years. And if you look at Intel 7, Intel 4, Intel 3, Intel 20A and Intel 18A, those are the 5 nodes. Intel 7 is done, right? That's what's delivered with 4th Gen Xeon. Intel 4 is being delivered now with Meteor Lake, the high-volume client product. The sister node to Intel 4 is Intel 3, which we are delivering next year with both Sierra Forest and Granite Rapids. And so the health of 4 and 3 is really, really good. And Intel 3 is really just a more optimized, more dense library. So it's higher performing for data center and server implementations, but it's very similar to Intel 4, which means that the process is healthy, and we feel really good about, again, all the power ons happening now and all the volume validation going on with customers. So by next year, 3 of those nodes, check, check, check. And then when we get to '25, really next year, we'll have, in '24, 20A with a client product, again, being the pipe cleaner for that process. 18A is the sister node. And that's when we plan the Clearwater Forest, which is the follow-on to our Sierra Forest E-core product that we're delivering in the first half of next year. And with 20A, we're going to get RibbonFET technology gate-all-around on the transistor. With 18A, we get the backside power delivery to the transistor. And so both of those innovations coming together in 18A is really exciting for us. So process is hugely important. Both Intel 3 and 18A are the foundry nodes, so we, of course, are going to drive a lot of volume on both 3 and 18A with our own products, but the Intel foundry goal is to ensure that if we have a volume of customer on Intel 3. We are working hard to close a volume customer on 18A, and so that's crucial. The second big area of innovation and product leadership comes from architecture and engineering. And for us, I think that we have to own the fact that we lost a bit of our engineering discipline over recent years. But in the last, certainly, 2 years since I've been leading this organization, our focus has been execution, execution, execution; rationalizing the road map, which were painful decisions and trade-offs that we made. But we wanted to go to our customers and say, "When we make a commitment, we're going to meet the commitment." And again, I'm happy to say that we are so much healthier today. All the leading indicators look great. And so that focus on our priorities, doing fewer things better and executing for our customers and coming to market with a predictable cadence of high-quality products, is what our customers had counted on us for decades and they can count on us again in terms of product leadership, process leadership and being on time when we say we're going to be.

Got it. One new and interesting development is kind of this emergence of these combination CPU/GPU platform, right, whether it's Grace/Hopper from NVIDIA or MI300 from AMD. How do you look at that? Is that a big deal? Is it a small deal? Do you think it's going to cannibalize the current market structure, which is kind of discrete CPUs and discrete accelerators? Or do you think it's kind of a niche thing? It handles certain workload, but it's not really going to be a big deal over time.

It's a bit of an unknown right now. I mean, clearly, we are delivering to customers, everyone is delivering to customers, products that, from a platform perspective, deliver both CPU and GPU integrated capability, again, at the platform level. And the thing that, that gives customers, which they like, is that flexibility in the system architecture and in addressing the workload requirements. So that model works very well. Typically, especially the hyperscalers, they don't deploy a node or a rack. They deploy very large-scale clusters, and they have very sophisticated software that lands the workload on the optimum hardware architecture underneath. And so that model is really the way that the market consumes compute today and particularly for AI. How the co-packaged approach will play out, honestly, I don't think anybody knows yet. It is predefining a certain ratio that you have to know or think you can project where those workloads are going to land. AI is moving so quickly, I'm not sure that anybody can truly say, 6 months from now, what it's going to look like necessarily. But it's something that clearly we're keeping an eye on. We have our own plans as well in terms of some of the future GPU innovations that we're driving forward, again, looking at what does it mean to share memory, to share power; are you really optimizing or suboptimizing any one of those proponents. But in the near term, Vivek, I mean, the market is big and wide and growing for discrete CPUs and GPUs and, at the platform level, bringing those together.

Got it. Now in late June, I believe Intel has announced, right, the day when you will describe how you have separate reporting for the design. And how does it really change your business on a day-to-day basis? How does it win you more share with customers? Like, how does it change what you do in the data center side?

Yes. So that entire IDM model or IDM 2.0 model is actually quite helpful in the decisions that we're making in our own product execution. And so some examples of that are we do use a lot of hot lots. And typically, as a GM, I don't always think as hard about that as I probably should in terms of the cost of hot lots -- not just the cost but the disruption to the factory in terms of their utilization and efficiency. We also have a lot of test time that we drive in our products that, again, might -- overshooting a bit in terms of the complexity and the content in those test scripts. And clearly, another area is just the decision to step a particular piece of silicon, one of our products. If or as we are getting much more transparency on the real cost to do that, not just the cost in terms of how expensive that is for the organization but the opportunity cost in underutilization or inefficiencies in the fab, I think it certainly is very helpful to me as the GM of the business and the other GMs at Intel but also to our process and manufacturing partners where they need to charge us more for maybe being less predictable and more demanding customers sometimes. And we need to probably think through where the optimization points are in terms of our internal costing. So for us, it's actually a very welcome change in how we look at the business. I feel I have way more data make more informed decisions and better decisions that will play through in the P&L. And similarly, for the manufacturing and the fabrication side of the business, they need to ensure that they have an efficient and compelling value proposition as they are attracting customers to foundry, which is health of the PDKs and costing that's competitive and defect densities and yields and all of those factors that really become their set of issues to work through. And I don't -- I just buy a wafer at a predetermined cost. And then I know what my costs are if I want to expedite some of that capability.

Got it, more transparency. And then just the last thing. What is that trade-off between having a lot of accelerator options that you can customize to many kinds of customers and workloads, right, versus having the focus on one? Because Intel has the programmable systems business, right? You have the Gaudi accelerator and your CPUs do acceleration. You have the GPU Max you mentioned. So how do you make sure that you have the right resource allocation and don't have too much fragmentation of where these resources are being allocated?

Yes. Well, I think that it's pretty clear that it is not a one size fits all, right? It's not just AI, but workloads are so diverse and so expansive that we do need different architectures, scaler architectures and vector architectures and matrix architectures and spatial architectures, and so that full complement of CPU, GPU, AI accelerators and FPGAs and IPU is another scale-out tool that we have in the tool chest. All of these are required to meet the diversities of our customers' workloads. The key for us is having a consistent software stack. And I think that this is the thing that we clearly see, that developer productivity and time to an outcome is the biggest measure of value for customers. And particularly when you get into large scale-out clusters, it really isn't just the device. You have to think about the networking, the fabric, the system architecture, the platform capability, the cooling technology, in some cases, memory pooling, how you're addressing those capabilities. And so it is not like a one-size-fits-all approach. We have to invest in innovations across that portfolio. But our focus is really on addressing customer workloads. And that comes in through the software. And a lot of that optimization work, frankly, does happen in software. So process technology always gets you a performance boost, anywhere from 15%, 20%, 25%, 30%. Architecture and design gets you another performance boost, again, another 20%, 25%, 30%. But software is the multiplier. So you can get 5x, 10x, 20x performance boost through software. So we really do believe you need that rich set of underlying heterogeneous architectures, but it's the software that is the most critical, and that's where the biggest area of investment is going to be for us going forward.

Perfect. Great. Thank you, Sandra. Really appreciate your time. Appreciate your insights. Thank you all.

Thanks, Vivek. Yes, good to see you.