Astera Labs, Inc. (ALAB) Earnings Call Transcript & Summary

Good afternoon, everybody. Let's get started with the next fireside chat. We're very happy to have 2 executives from Astera Labs, Sanjay Gajendra, sorry. -- you guys. Anyway. Sanjay, who's the COO and Co-Founder of the company; as well as Nick Aberle, who is the VP of Finance and runs IR. So gentlemen, thank you so much for joining us.

You guys have been an AI connectivity pure-play relatively recent IPO. It's been an exciting year, 1.5 years for you. Your stock price has gone up nearly 5x over that time. So congratulations on that. I think you're uniquely suited to see what's happening in AI given all the connectivity protocols you address in the customer base, et cetera. So I want to start with a high-level question. And that was simply where do you see we are in the AI cycle? Are you seeing spending changing, accelerating, decelerating, diversifying? At the highest of levels, where do you think we are?

Yes. So let me kick it off. But first of all, thank you for having us here. It's wonderful to be here and talk to you and the rest of the folks here. Going to your question, again, we still fundamentally believe we are in that early innings of AI. I think just based on the number of RFQs and opportunities that we are seeing, it's not slowing down anytime soon. In fact, it just continues to get bigger and bigger. The things that are playing into this, of course, is the hyperscalers, their spend continues to grow. There has definitely been no slowdown in that. The second thing that, of course, we see is there are increased investments that happen on, let's say, the internal or custom accelerators largely because I think companies now understand where the money is with AI, what the workload requirements are that they need to optimize from a TCO standpoint. So there is that realization. Again, it doesn't mean that they don't need the general purpose GPUs from NVIDIA or AMD and other providers. It just means that there are also an understanding of what is needed. So there is, let's say, a little bit more thought and optimization that's happening on those kind of decisions. And then in general, I think the notion of having a robust supply chain and multi-vendor approach to addressing various aspects, whether it's on the GPU side or on the connectivity side, definitely is a change that we're seeing. And our vision is that 3 to 5 years from now, that having a rack, AI rack that is open and scalable with a multi-vendor supply chain is going to be a key requirement just given how broadly AI will continue to be deployed, both for training workloads as well as on the inference side.

So since you're not seeing any slowing in the spending and if anything, some acceleration, are you seeing any changes? Is the size of the processor counts in clusters continuing to increase? And if so, how do we think about your connectivity content scaling with that?

Yes, absolutely. So there's no slowing down, right? You went from, let's say, an 8 GPU cluster in the Hopper generation to now 72 and for Rubin and other, if you use that as a benchmark, you can see that scaling quickly to 288 and beyond. So that, I don't believe it's going to slow down just given how the model complexity and model sizes continue to grow. In terms of connectivity, it's a fantastic place to be in because end of the day, the GPU does what it does or the accelerator does what it does. But as the cluster sizes grow, then the amount of data movement increases tremendously. Because as you probably know, in a GPU cluster, every GPU needs to talk to every other GPU, meaning even if it's a 72 GPU cluster, from a software standpoint, it needs to appear as one GPU sharing the same memory. And the way you achieve that is by creating a connectivity fabric that's super fast, super reliable and is able to ensure that from a software standpoint, things are happening in a way, coherency and things like that, that everything looks like one. And that magic happens through connectivity and connectivity truly is the bottleneck today. If you think about it, meaning there's a lot of innovation, a lot of products and technology to be built because as some of you guys might have heard, even though people pay thousands of dollars for NVIDIA GPU, roughly half the time, it's actually sitting waiting, idling for data or memory. And that simply means that the problem to solve is connectivity. And with its cluster sizes growing, that problem is not going to get simple. It's only going to get more complex, and that's the opportunity we see in Astera.

So why don't I pivot somewhat temporarily to just a little nearer-term dynamic. You mentioned NVIDIA and some of the stuff about shipping to China, not shipping to China. Does that matter to you guys? Is that a little bit of a headwind if the processor companies can't ship the merchant folks? And if they can start, does that become a tailwind for you? Or is it just around the edges and it doesn't really matter?

I want to say, again, China is a significant market. I think they do see -- there is a lot of data out there. There's a lot of momentum around AI workloads and all that stuff. But it's also a slightly different set of dynamics given the geopolitical overhang that we have. So for us, yes, we do see some single-digit revenue we make out of China. But at the same time, we are not like banking on it to the point that our strategy and the models we're putting together are skewed to it just given you never know what could happen. So for us, I want to say, if something good happens, meaning geopolitically speaking and things open up, we will always see that as an upside versus planning that in our base models. And that's how we've been approaching it, and let's see how things work out. The one good news is that at least so far, connectivity is not as much under the microscope when you compare it to the processor and technologies related to that.

Right. It's more secondary, but so important. So I want to get into some of the diversification dynamics. And so we'll get into a little bit of the customer and processor end market side of things. I guess, first on the hyperscaler side of things, I think in the last year, you've gone from 3%, 10% or more customers to now I think in the last quarter, you had 5% or 6% of those. What's leading to that diversification?

So what's leading is really a dynamic within the market in the sense that the cloud and hyperscaler customers, it is a limited set of customers that truly matter, right? So when you have customers ramping, you can immediately see that in how the share shifts. That's one thing. The second thing is that the appetite for new technology varies between hyperscaler to hyperscaler. There are some hyperscalers that are always hungry for new technology than -- as soon as it's available, they want to deploy that in their fleets. And that's what you see. I mean you have certain customers, predominantly, of course, NVIDIA and AWS and all these guys. They are the first ones to deploy once the technology comes forward. So there is an element of timing, meaning as you think about with time, you will see more people come in and there is always a distribution of, let's say, 12- to 24-month difference between the first one to the next set of folks that follow. The third factor I would say is that the business model. If you think about a hyperscaler like Meta, they're mostly running internal workloads, Instagram, WhatsApp, Facebook and so on compared to someone like Amazon or Azure from Microsoft, they're running your traditional cloud services, meaning rental models. They don't know who is going to require what, whether they need an NVIDIA instance or an AMD instance and so on. So the point I'm trying to make is that depending on the number of customers, the limited market, the aptitude for technology as well as the business model that the hyperscalers are driving, those things will have a factor in how our market share changes. And that's -- in some ways, it's an occupational hazard if you think about the hyperscaler data center market.

Yes, it's naturally concentrated no matter what you do, and you wouldn't be winning if it wasn't. What do you see as the -- most of the key design wins happen at the hyperscalers for you or as part of reference designs with various processor makers?

So it's a combination, I want to say. But increasingly, what happens is that since many of the hyperscalers are doing their own accelerators, so for us, that is an opportunity where for the non-NVIDIA ecosystem, we get to play both on the front-end network. So think of scale-out topologies, GPUs connecting to CPUs and all that. But more importantly, we get to play on the back-end network where GPUs talk to each other, right? This is the scale-up portion of the network. So in general, given our focus right now with Scorpio X-Series, which is a fabric device that is used for interconnecting GPUs and so on. So there is a lot of momentum, a lot of engagements we're having directly with the hyperscalers. And when you think about reference designs, whether it's on a GPU platform and CPU platform, it's definitely a channel and an avenue for us. But at the same time, I want to say the engagement has to happen with the hyperscalers so we can drive rest of the decisions. And then you have the dominant merchant silicon vendors like NVIDIA and AMD that have been great partners with us, and we continue to be on their platforms.

So on the processor side of things, late last year and earlier this year, you talked a lot about diversifying into the custom architectures, -- away from, but in addition to the merchant side of things. How do you view the opportunity on the custom side versus the merchant side going forward? Is it a big battle between the 2 within your business? Or can they both grow?

I think -- I mean, short answer is both can grow because there is space for everyone. The class of GPUs that folks like NVIDIA are doing are in a completely different category. These are general purpose GPUs that can be used to run a variety of different workloads, right? Whereas you have custom accelerators that are being designed to solve a unique class of products. I don't think any custom accelerator is being designed to go head-to-head with NVIDIA. So you will have space for both depending on the kind of workloads and also kind of business model that a hyperscaler is driving. For us, like I noted a little bit earlier, we like the non-NVIDIA ecosystem from an attach rate standpoint because we get to play both on the front end and on the back end. right? So that's why we see much more larger content. And if you think about our growth itself, we have been working towards this 5-step strategy that we have laid out where first step of our growth, if you think from IPO time frame, was based on the NVIDIA Hopper generation. We were on all the UBB and HGX platforms that they were shipping, right? In Q3 of last year, we transitioned to, like you noted, custom accelerators. Our revenue went up -- step up starting third quarter of last year. Now we have entered the third step in our journey, which started with Q2, where we are not only selling Retimer class device, we're selling the switch devices for the Blackwell-based platforms. So our dollar content went up from a little less than $100 per accelerator to now multiple hundreds of dollars per accelerator from an attach rate standpoint for the design wins we have. Step 4 that we're working towards is to further diversify our portfolio with what we call Scorpio X-Series. So these are fabric devices for GPU-to-GPU interconnect. The Scorpio P-Series, which I noted as step 3 is for head node connectivity for GPU, CPU and so on. So X-Series puts us in a completely different position from a socket standpoint because the scale-up switch is one of the anchor sockets, one of the early decision factors that hyperscalers make. So we have several design wins with design wins starting to ramp later this year to high-volume production in '26. We have over 10 customers that we have engaged on the scale-up fabric side. And then, of course, the step 5 for us is the vision that we are driving for a complete rack level connectivity solution that is based on UALink, which is a standard being defined and developed for scale-up network in an open standard format that will go into production in 2027. So if you think about our story, it's one in which we are adding more and more product lines to service the same application with products designed from the ground up for servicing the AI workloads. And then we are expanding the customer base that we are working with, both from a traditional, let's say, hyperscaler type of customers to other Tier 2, Tier 3 players that are essentially starting to deploy large clouds based on the new cloud model and things like that. So in summary, I think we are well set. It's just a matter of ensuring we continue to execute.

There's lots of ways I could go with that answer, but I'll pick the ASIC side to wrap up on that, and then we can get into this UALink and some of those other protocols. Is the number of ASIC vendors out there that can be potential customers of yours continuing to grow? And I know it's not a zero-sum game versus the merchant side of the equation. But it seems like that opportunity in and of itself is diversifying and growing really rapidly. Is that true?

Absolutely. Like I noted, I think the hyperscalers have realized that they -- a, they've realized where the money is with their AI workflows and they want to build accelerators to address that and optimize to that. And second thing is no one wants to be held in -- or restricted in some ways of how they do their business. I mean, today, if you think about it, there are a couple of ways in which they're sort of in a jail, right? There is the vertical integration that NVIDIA is pursuing and then you have folks like Broadcom that are trying to provide a certain type of solution that combines with their custom ASICs and networking and all that. We truly believe that there is a third way of achieving this, which is to provide an open rack, open standard that services the nuances of various different use cases. And that's clearly where we see the momentum right now, and that's what we're focusing on.

Do you believe the ASIC vendors who just happen to also have big networking divisions within their company, Broadcom, Marvell, to name 2, do they have a -- is it more challenging for you because they have those networking divisions that would compete with you? Do they have a bundling advantage?

It's easy to imagine that way. But if you think about it, even like there are several examples you can think of where, for example, let's say, Google, they use custom ASIC services for their TPUs, but they do their own networking and so on. So I think it's probably a little bit of a, I guess, simplified way of thinking to imagine that somehow you can bundle these 2 things because, again, what you need on the custom accelerator, the requirements of it, the feature set of it, even though it could be manufactured on the back end by someone, the design of that is done by hyperscalers, right? And hyperscalers also don't want to be held hostage. Like I noted with some of the solutions. So what's happening is it's having the reverse effect where we are getting invited more into these parties so that they could get the best out of the both world, right, meaning we are developing technology that is truly differentiated, truly developed like for AI type of workload. And then in combination with the accelerators that the hyperscalers are designing, they're able to achieve the technical performance or superiority commercially and contractually and pricing and other leverage that they want, they're able to gain that as well. So I think in general, the hyperscalers are not foolish. They don't want to be held in a jail and they're reacting in a way that you would expect them to do, which is to try to get the best of both the technical world as well as from a commercial standpoint.

So why don't we pivot into some of the product lines and then some of the topics around that. And let's just start with Scorpio. I know it's not your biggest product line, but it's growing really rapidly right now, and it ties into the scale up out dynamic that you talked about. Why don't we start with the scale up side of things? Just talk about the various protocols. You guys are big supporters of the UALink. Obviously, you have NVLink and then Broadcom is doing its way with the SUE side of things. Talk about the advantage of UALink and why you think that's the right way to go.