Penguin Solutions, Inc. (PENG) Earnings Call Transcript & Summary

Well, hello, everybody, and welcome to the Penguin Solutions fireside chat at the Goldman Sachs Communacopia and Technology Conference. I have the privilege of hosting Kash Shaikh, CEO of Penguin Solutions. My name is Kat Murphy, and I cover Penguin and IT hardware here at Goldman Sachs. We'll have about 35 minutes for today's discussion, inclusive of Q&A towards the end.

So maybe to go ahead and get started. September marks 7 months since you've joined Penguin as CEO, joined at an interesting time as the company transitioned from a holdco into an AI solutions-oriented business. For the purposes of this audience, can you help us understand your current business mix and where Penguin is really positioned to go after this investment cycle around AI.

Thanks, Kat. So in the last 7 months, we have focused on 3 main areas: First, prioritizing our data center AI infrastructure business and our integrated memory business. These 2 businesses have very high demand because of the super cycle of the infrastructure and they represent our AI-driven businesses. Second, we have increased our investments in product innovation. And then third, we have accelerated go-to-market execution with a clear focus on 2 main segments: new cloud segment and the enterprise segment for our AI infrastructure business. We also introduced what we call our AI factory platform. This platform combines differentiated products like an OEM offering as well as a system integrator like end-to-end services design, build, deploy and manage. This allows us to be the single partner for our customers building the factories, AI factories as well as operating the factories on their behalf. And the results show the progress in Q3, which was our last earnings that we announced publicly, our AI-driven businesses represented 74% of company net sales, and they grew 104% year-over-year. Company also had the record net sales at company level. And based on our guidance that we provided in the last earnings forecast, we are expecting yet another record quarter at company level, and it will be our first over $500 million net sales for the company.

Great. You also provided a preliminary outlook on earnings last quarter for fiscal '27 and guided to 30% year-over-year growth to consolidated level, which implies an acceleration sequentially. Can you talk about what is informing your confidence in that sequential acceleration? I know there's some moving parts within the business. And where in the business across those 3 opportunities that you outlined, you see the most incremental upside opportunity?

So as we mentioned in our last earnings -- our confidence comes from, first of all, the strength of the market we play in. We have 2 tailwinds, very high demand data center infrastructure market, very high demand memory market. And we have a very unique product market fit for both of these product lines. And then combine that with the visibility that we have with our bookings, backlog as well as a pipeline that is really driving the outlook for FY '27. And in terms of the upside over the outlook, it really depends on how fast we can convert some of the large factory opportunities that we have in our AI infrastructure business.

Got you. You've reported historically the company in 3 different segments, but I want to focus on advanced compute and then the integrated memory opportunity. First, on the advanced compute side, can you break down the lineup of hardware, software and services within your portfolio and how you're going after both the NeoCloud and enterprise opportunities that you talk to with those products?

Yes. So within our advanced computing, we primarily focus on our AI infrastructure solutions business. This is where we offer the AI factory platform, and this AI factory platform is a very unique combination of products. These products include cluster wear AI, which is an AI factory operating system, just like an operating system like Windows will provide a platform to combine all of the resources between memory as well as applications and an CPUs, our AI factory cluster were operating system provides a similar consolidation of the cluster as well as what we have done with our cluster AI recently in the last 6 months, we have also created agentic experiences -- as an example, the operators can now use the natural language to find out the health of the GPUs as well as the utilization of the GPUs. So that is 1 of the key differentiator from the product perspective and our platform. We have also invested in our memory AI product line. This is a product line where we are leveraging our unique insights from decades of experience in the memory architecture as well as the AI infrastructure, data center build-outs. And this memory AI product line provides benefits, especially for the inference workloads as AI move from training to inference powering agent AI. This is where the contact sizes of the messages are very long. And these requires memory architectures that can help accelerate the LLM responses and performance. So that's the next product line. We have also introduced compute AI product line within this portfolio. What it is, is essentially is a set of GPUs and accelerators from NVIDIA as well as from AMD. So we can provide the products to our customers that help them manage the factories as well as build out the factories. Then we combine them with our end-to-end services. So we get involved in much earlier in the cycle with our customers. So we come in we designed the factories on their behalf. And as we are designing the factory, we recommend our products, partner products. And then we also build out as an do-the-system integration for them. And then we signed contracts with them 3 to 5 years to manage those factories. So this is this unique combination of offering like an OEM product company combine that with an end-to-end system integration offering that allows the customer to work with us as a single build partner as well as a single operate partner for the AI factories, primarily focusing on neoCloud build-outs as well as large enterprises.

That's very helpful. So you talked about AI factories, memory AI, compute AI services. That's kind of the appropriate way to summarize them. And you're going after different customer verticals. For the neo clouds, which of those 4 products resonate most and for the enterprise, is there a different kind of go-to-market or sales motion that you're trying to engage with your customers on.

So it depends on the journey of the customer, right, whether it is a neo cloud customer or an enterprise customer. In some cases, we start small as in they may be looking for and we provide them GPUs with our compute AI product line, whether they are enterprise or neo cloud and then we expand with our services or other products. So that's our land and expand strategy across both enterprises and neo cloud providers. However, in some cases, the land is pretty significant. What we are seeing especially in the last 6 months, what we have seen the acceleration of the neo cloud providers because both the hyperscalers that are giving them the business as well as the business they get from the enterprises that creates a very unique opportunity for Penguin Solution where these new cloud providers are relatively newer providers. So they are setting up the entire data center. So they will go out and let's say, lease the data center and the power, then we go in and we do everything from them as a design it, procure the hardware for them and the software, build it out, and then we are signing contract to help them manage the factory. So they can focus on getting the offtakers as in their customers, and we are the builder and the operator for the factories. So it really depends on where they are in the journey. In some cases, it's a smaller land with Xpand, in some cases, a pretty significant land and then they continue to expand with us as they are building out new data centers.

Something that's also unique to Penguin is your agnostic approach to other third-party hardware within some of these ecosystems that you're helping set up -- can you talk about how that is a point of differentiation and why and what in Penguin's portfolio, maybe this goes to the cluster are software operating system layer allows you to bring forward a best-in-breed solution rather than a sole vendor solution?

Yes. So we believe in first of all, an approach that is much more focused on meeting the objectives of our customers. Let's say, it is a neo cloud customer. The neo cloud customers toric sign the SLAs with their offtakers or their customers. And based on those SLAs we get engaged, as I mentioned much earlier in the cycle with them. And as we are designing those AI infrastructure for the factories, we built the architecture in a way that it meets their requirements, whether it is our products or it may be the products of our partners such as Dell and along with the cluster ware and the advantage of the cluster ware, especially with our engagement with the customers, which stomach earlier than the product companies we have the discussion and conversation at the architecture layer. As you have these SLAs for you to meet these SLAs. This is the kind of architecture we represent and also the fact that our cluster ware is hardware agnostic. So whether it is NVIDIA, whether it is AMD products, we can provide them the support of this operating system that is helping them manage their GPUs irrespective of the vendor and helping them achieve their objective without necessarily either just dropping the hardware on their doorstep or necessarily forcing them to use a single vendor, which is usually not the case because, as I said, depending on the SLAs, they have different requirements, and we meet the customer where we believe we meet their requirements versus just forcing our products.

Maybe to help illustrate all of the opportunities that you have and the ways you're participating in these types of engagements. Could you talk through an example? I know publicly, you've talked about a Tier 1 financial customer that you won jointly with Dell. Maybe talk more about what that sales motion look like where you're involved versus where Dell's involved and how you're capturing value across that design, build, deploy and manage cycle?

Yes. So this large -- and it's actually a good example of some of the things I mentioned. So this large bank put out an RFP and their driver was they were consuming the infrastructure from a new cloud provider. However, they had some new applications that they are developing, which they believe that will help them create new revenue streams. So they put out an RFP and in that RFP, obviously, we competed against the product companies, product OEMs. We competed against system integrators and their requirement was they are building -- they were building this on-prem as the factory the first time, and they needed someone who not only can provide them the product, but help them manage this factory because these factories are pretty complicated and it requires a deeper understanding of the products and architectures to be able to deliver on the requirements. And we were able to win this -- and then when I sit down with the CIO to understand why us versus, obviously, we had very large product companies on the other hand, and very large system integration company. It came down to 3 areas at the high level. First of all, they were looking for something that is much more comprehensive as it not necessarily just give me this product, this product and this product. They had their objectives, which is I have new revenue opportunities, and I need someone to help me with a factory. But my goal is to develop these applications that will help me with revenue generation opportunities. So the fact that we provide the whole design, build and deploy to meet their objectives was an advantage so that they are not piece laying all of these products to be able to stitch together their first on-premise factory, which is pretty significant side. So that was number one. Number 2 was our cluster ware AI product because, again, that cluster ware makes it easier to manage and automate the monitoring and management of the AI factories. And the third one, interestingly was our memory AI product line. And they were the first customer who actually bought it, considered it now that we have -- now we have other customers, but they wanted to make sure they were considering the applications that are much more context that inference applications. And they wanted to make sure that they have technologies that can enable the performance and faster response for the LLM. So those were the 3 differentiators for us in terms of winning that project.

That's very helpful. I want to touch both on the customer piece as well as the memory AI and maybe transition to the memory side of the story as well. But first on cluster ware, -- how are you using ClusterWare as part of your sales motion? Is it something -- maybe said differently, is this something that is included in part of the pitch as to how you can manage an estate across kind of the end-to-end nature of it -- or is it something eventually you could package and sell separately? What are the alternatives to cluster ware? Like what are AI factories who aren't using cluster are doing as an alternative. Anything to just contextualize the moat that you may have built with that platform?

Yes. So the current -- first of all, the current motion of cluster ware is primarily with our services. So as we get involved, whether it is design or management services, cluster ware the software, our managed services team is using. However, our goal in cluster war is to make it stand-alone PAUSE and especially with the agentic experiences that we are creating, these identic experiences will allow users who are not as much familiar with our product and easily use it as I mentioned, with natural language interface that we have provided. So short-term, primarily a differentiator for us to win larger projects with our services business. Longer-term, our goal is to make it available to the customers, whether these are neo cloud customers and enterprise customers. And that will also help us more with the foot in the door, land and expand strategy where we may not have the entire project, but we start with the software. And then as they realize the benefits of the software, we can offer other products and other services. And in terms of the alternatives, that's an interesting question because there isn't an equivalent of cluster ware because most of the alternative software available they are very vendor specific. And the challenge in the vendor-specific software is, again, you have to piecemeal different pieces of infrastructure when you are managing the fact you're building the factory over managing the factory. So the challenge becomes increased time, manual configuration and may not result into the best utilization of the infrastructure.

Very helpful. Let's talk more about the memory AI compute platform or memory AI platform as well as the offering you have within the broader integrated memory portfolio. What exactly are you selling with an integrated memory? And how is memory AI an extension of that? And what are your core competencies that are starting to drive differentiation in the way that you serve customers as inference demand increases?

So in our integrated memory business, we build memory modules for OEMs -- these are large OEMs, you can think of large networking companies, large compute companies. And our Board there is essentially at the high level engineering expertise to design those memory modules because this is just not just memory. It is a memory card that goes plugs into as an interface within the OEM products. So we have engineering capabilities. We have validation capabilities, we build them, we validate them. And then we have manufacturing capabilities. We have factories where we manufacture those cards. And then we have the supply chain capabilities because we manage this business at scale. What we have done recently with this business at the high level, first of all, we are primarily prioritizing data center customers because the memory demand is high everywhere, but a lot of this demand, in some cases, is a function of just supply and demand. We believe the demand in the data center is driven by AI. And we believe the demand in the data center will be more durable than the cyclical memory cycles we have seen, which is why we are very disciplined and focused on the data center. So that's 1 of the things we are changing and it is helping us both in the short term and we believe it will help us with the durability of the business. The second thing is focusing on new AI infrastructure opportunities. Looking at the use cases such as the inference I mentioned with the adoption of agentic AI requires more memory in terms of having the capability to store the context so that the GPUs don't have to compute a dollar again as the LLM are doing the influence for an application. And CXL is 1 of the standard compute express line. So we have been the early adopter of CXL. And using CXL, we create 2 products. One of the product is our memory expansion cards. So these are just the CXL-based memory expansion cards based on that standard that we provide for the OEMs as a part of our OEM go-to-market with the integrated memory business. Then we took that CXL capability and created an appliance, which is memory AI KV cash appliance. So you can think of an appliance, which has both the hardware and the software to be able to provide the memory capabilities for the LLM to store the context. So let's say, if the LLM is writing a book. One of the option is every time it is writing a new sentence it's doing the whole computation again. The challenge becomes inefficient use of GPUs and high bandwidth memory or you're going to have a memory bank like our MeikVcash appliance. -- where you can store the context of the book you have written so far. So next time, when you are writing the next centers, you don't have to compute all over again as an example. That's really the benefit. So what it really does is providing you the faster response for the LLM and it is also saving the temptation power for the GPUs. So what is a net effect. You may not need for all the conversation, so your spend can go down. And then you will have faster responses for the LLM, which means better economics and faster responses. That product is part of our AI infrastructure, data center design, build and manage because it is an appliance that is connected to the GPUs versus the CXL cards are the part of the go-to-market for the integrated memory business.