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

Good morning, everyone. Welcome to our 2024 Analyst Day. As we begin the day, I'd like to remind you that during today's presentation, we may make forward-looking statements and refer to certain non-GAAP financial measures. Forward-looking statements include predictions and expectations about future events and our financial and operational performance and are subject to risks and uncertainties, many of which are beyond our control and could cause actual results to differ materially from our expectations. For more information on such risks and uncertainties and other factors that could affect these expectations, please see our SEC filings. Non-GAAP financial measures are supplemental and should not be considered as a substitute for or superior to GAAP financial measures. You can find reconciliations of the non-GAAP financial measures referred to today to the most recent directly comparable GAAP measures in the slide deck accompanying today's presentation. Thank you, and please welcome Mark Adams, CEO.

Good morning. And thank you all for coming this morning, both in person here in the room at our NASDAQ marketplace as well as those online. We couldn't be more excited to present our company to you today, not just what we've achieved, but really where we're going as a company. Today's agenda is pretty targeted, and we have a whole set of team members from our company to take part of the agenda and tell our story to you. After I give a brief set of opening comments around our strategy going forward, we're going to have Mark Seamans, who's our VP of Global Marketing, talk to the market opportunities we have as well as starting to define some of the complexity that the market has with deploying AI. From there, our President of Intelligent Platform Solutions, Pete Manca is really going to talk about the engagement with our customers and how we're building those relationships and scaling that part of the company. We're also going to talk a little bit about the strategic nature and the role of memory in AI. And today, we have Andy Mills, who's our Vice President of Product and Technology for Memory to articulate how memory plays such a critical role in the performance of compute and more broadly, AI. After that, Jack Pacheco, who all of you know, is our Chief Operating Officer and served as a transition role while we're having Nate joined us. Jack is going to really kind of present the financial numbers as it relates to our business and our long-term model that we're presenting today. I'll get up and have a couple of closing comments. And then post that, we'll certainly open it up for a session of Q&A and be able to answer any questions you might have. So again, thank you very much for coming. When I started back in the end of August 2020, that was really the end of the fiscal year. And I got to know the company. Jack was kind of a key executive sponsor for me coming into the company and getting me oriented to the company. And one of the things you might imagine is like, how do you get as much information here. The classic CEO is the first 90 days. So I talk to customers, I talked to suppliers, had a lot of internal meetings. This was actually during COVID. I don't think we had our first real in-person meeting for 7 or 8 months, if you can imagine that. One of the things we also did in after about 4 or 5 months is I wanted to go out and see what folks like yourselves thought, both existing shareholders as well as the targeted shareholders that might be interested in the company at that time. And it was really -- I tell this to people because it's not a very expensive thing to go do. And yet we did this study, and it was like a playbook for a new CEO and obviously the team. And what I heard, again, wasn't shocking, but it was a great template for me. And what I heard upfront was you guys are close to 80% memory. It's not going to attract a lot of investors here, some people who like it, but a lot of people don't want to play the cyclicality in memory it's just too tough to call. The risk of not only Brazil, but consumer memory, which is the most volatile part of memory in Brazil, and quite frankly, at the time, they're very consolidated, there's 5 or 6 major customers. So a lot of risk, a lot of uncertainty in that business. Also, remember this, the direct quote I heard was we don't need another holding company. We can diversify ourselves. That was the quote that I heard at time, and it made a lot of sense, we're a small cap company, you do what you do, we do what we do and diversifying isn't something you're calling on us to do. And so I heard that loud and clear. And finally, between memory and then a couple of acquisitions Artesyn at the time, if some of you've been around the story for a long time, they bought Penguin, of course. The company also bought a smaller company called Inforce but it was kind of cloudy where the company was -- what was the real strategy of the company. And if you think about all that, I left that -- I think this is amazing. This is exactly what we should be thinking about as a group. And that's really the road map we use. And so if I flash forward a little bit to where we sit today, we have a business today that's now 30% memory, not 80%. I think it was like 77%, 76%, but we have a business that's dramatically lower exposure to broad cyclicality of memory, not 0, but a lot less. Of course, we were able to divest of the Brazil business, and it was a great outcome for the shareholders. Just incredible at the time, I think we sold that for a multiple that was actually higher than the multiple of our company at the time at a time that the business went split in half, gross margins and operating income negative. It was -- we found the right strategic buyer. I want to also say one other thing. Not only was it great for the shareholders and great for our company, it was great for the employees in Brazil. And as a CEO, that's super important for me. We have a responsibility to all of our team members and they found the right partner to go scale their business. As you all know, we have about just slightly less than 20% ownership in that business because we believe in the business, it's just not where we were going as a company. But again, a good outcome for us there. Quite frankly, we've stopped the whole holding company mentality on how we think about M&A, how we think about operating the company. And we're on a path to be more of a traditional operating company model which looks exciting for us because it gets us more focused on where we're going on this great opportunity you're about to hear about today. And then finally, I would say why we're here today is we're here today to articulate our go-forward strategy. And you can see it around the room, solving the complexity of AI. Before we get into the glitz and excitement around AI, and there's a lot of that, I really want to hype on one thing. We're an operating company first. You're going to see a lot of exciting innovation today, you're going to see a lot of customer engagement activity, you're going to see a lot of good stuff going on today. But as a management team, we're focused on execution. One of the best lines I've ever heard about strategy is strategy without execution is a bad strategy. And I want you all as shareholders to know this is what drives us every day, execution. And so when I stood up here in April of 2021, a little bit over 3 years ago today, we were baselined at $730 million. Now that was normalized because we took the Brazil piece out of the business just to be able to compare apples and apples. Gross margin around a little bit below 21%, 20.7% and operating income about 4.6%. And so we came with those results, and we set a long-term model at the time. And we talked about high single digits growth in top line. We talked about a gross margin target from 20.7% to 26%. And we talked about an operating income target at 10% from 4.6%. That's what I -- if you go back and look at the presentation or for those of you who have notes from the meeting that day, this is what we presented. And again, from an execution mindset, I just wanted to show you how we've done against that. Top line growth in the 12% to 13% range, gross margin slightly above 32%, call it, 32%, and operating income at 10.4%. Again, execution mindset. Before all the excitement around AI and scaling and all that good stuff, we have been a responsible and prudent management team, and that's the way we think about the business. Our job is to return to our shareholders and to our stakeholders. And that's the highest priority we operate the company with. And if you look at our balance sheet, if you look at our operating results, that should be loud and clear as we think about where we're going as a company. We're going to operate this company in the same way with a prudent mindset to run financially fiscal and responsible. So maybe I'll inject my first attempt of humor today. For the last 4 years, I've gone into meetings with all of you. And we only had hour meetings, right, 55 minutes or what have you, and this thing comes up and then like, hey, what is SGH, tell me what that means? And clearly, it was a company in transformation evolving. And one of the big highlights today to start our communications is that today, we want to announce that we intend to rebrand the company, Penguin Solutions. And there is a process as a public company, that we will go through. And I think we're talking mid- to late fall that this will be formalized. We have to go to our shareholders for approval. We don't anticipate, but we don't know. But so I can't say with 100% certainty, but I'm pretty confident that we will becoming Penguin Solutions and a company that solves the complexity of AI. For those of you who are aggressively thinking, okay, what the symbol is going to be. We've proactively taking the pen on securing PENG for our company's NASDAQ ticker if we get the shareholder approval. So this is a major symbolic milestone for us because it really does highlight this transformation. Now when you think about some of you who've been with this company a long time, it was public ones under the symbol SMAD, SMART Modular. And then it went private, and then it went public again under a memory restart. And then they bought a couple of these assets I mentioned. This is the Silver Lake management team and Ajay, who is the founder of the company. And they put the company on a path to diversify away from memory. What we're really talking about today is the next step in the transformation. And the next step in the transformation is Penguin Solutions positioned as an AI innovator. And that is the message today as we position this, solving the complexity of AI as an innovator, as a solutions provider, a much different company than we were less than 4 years ago today. Here's a staggering fact or statistic, I should say. All the noise about GPU sales, incredible hardware sales and the likes. Back in 2023, only 5% of global enterprises were deploying AI. By 2026, that number is supposed to be closer to 80%. And so not a news to you AI is pretty exciting. Maybe slightly news to you is that the world of deploying AI is in front of us. This is early innings and that's the game we're in. What's this complexity we talk of? Well, that's going to be a large part of our message today, showing you what it's like. I can raise my hand to the question that I've never worked in a data center. But if you go to a data center and you take a look at what's going on in terms of the AI infrastructure environment, it's quite different. And of course, using someone else's quotes a little bit easier, but it's radically different to implement and deploy AI than it is a traditional compute environment data center. Why Penguin? Why us? Today, I can say that we don't know of many other solutions providers that have been in the business over 2.5 decades. Penguin's history that started out primarily in hardware because that's where the game is being played at the time, laboratories, government, education, 25 years of deploying these types of systems in a data center environment. You can't bottle that up, you can't just show up as a crypto company or something like that and say, hey, I want to go to AI tonight. That's just not possible. That know-how from a systems and architecture standpoint is very powerful. Beyond that, the software layer to manage the infrastructure, what does management mean? Well, on a multiprocessor environment, there are systems resources that need to be allocated and scheduled. Then there's a diagnostics component that allows you to see what's breaking down. One of the most hidden fact of data centers today is the failure rate of GPUs. And being able to know that ahead of time proactively through your software platform to be able to diagnose defaults ahead of the time, predictive, it's all about uptime, uptime is money in AI. And so we have this software platform that we use to drive our infrastructure, and we're continuing to invest more and more in doing so that enables us to proactively grow our solutions capabilities and be more valuable to our customers. Managed services. These data centers, as I said, are complex, and you're going to hear about the complexity shortly. Again, 25 years of people going on site, working with customers, knowing what's going to go wrong. By the way, you talk to anyone who runs these data centers, things go wrong. It's not that things aren't going to go wrong. It's what you do when they go wrong to get back up time and being the highest performance you can be. And that's a capability, again, that we have that's very unique to the marketplace. Innovative solutions. You're going to hear bits and pieces today, but we are leading in terms of innovation inside the data center. Cooling systems. Advanced processors beyond today's traditional, what we know GPUs. Memory solutions. Now if you think about this, without memory, there is no such thing as compute. The biggest bottleneck today in artificial intelligence compute solutions is memory. There's not enough bandwidth to allow the compute workloads to enable the memory to respond quick enough. It's all about direct link. You're going to hear about technology today that allows us to improve on that over time and some really radical new technology we've invested in. So slightly different than we've probably talked about in 2021, the investments in innovation we're making are substantial. And finally, as we think about characteristics of all of our products and solutions and services, high availability, high performance has to be available. It has to be peak performance. And that's how we design all of our products and solutions as we go to our customers. So in short, we're positioned as a trusted adviser for our customers, allowing them to harness the power of AI. We're technology agnostic. We don't come to you and tell you as a customer, you have to do this or this. There are sometimes we use vendor A, sometimes we use vendor B. We're great partners, but it's really the customer environment that's going to dictate how we design our solutions. And so being a trusted advisor allows us to really be more sticky in terms of our long-term relationships with the customers. And we've had a lot of customers that stayed with us, not just through HPC, but now in the AI generation. Before I hand it over to Mark, we had an announcement yesterday afternoon, and there's many components to it. But what I'm most proud of separate from the financial piece and separate from the strategic value, which is amazing for us. What I'm most proud of after that is the endorsement of a massive conglomerate company global in nature, and value they see in doing business with Penguin. So Jack will talk a little bit about the structure of the announcement yesterday. What I just want to share with you today is sometimes in agreements and announcements like yesterday, it's kind of like this one aha moment that, that's going to happen. We're very excited about the potential areas of collaboration that we've been in discussions with our partner. On the compute side, the AI infrastructure data center side. On the software side, by the way, they're very advanced and investing more heavily in AI, both in their own organic way, but also in the terms of the investments they've made, including Bloom Energy, Anthropic, Lambda Labs and today, SGH. And so when we think about these type of assets, it's just an enabler, continuing down this list, the global reach, most of you who follow the company know that majority of our business is U.S.-based for Penguin Solutions. We have the aspiration to expand beyond that over time. Having a partner like SKT will be invaluable to that objective. We've been talking a lot as it relates to the future of AI and the role that it will be playing at the Edge with potential inferencing platforms and the likes. Again, another opportunity for us to collaborate with SKT and build winning products to put us ahead of our competition. And finally, as they think about processing and memory together, working together with them to enable the highest performance and high-availability platforms. So there's a lot here to unpack for the optionality on what we can do with SK. There's a lot of potential areas for collaboration that we're discussing with them. And again, I can't be more excited. It's just a great endorsement for our company and opens up many avenues for us to explore. So with that, what I'd like to do is I'd like to hand it over to Mark Seamans, who is our Vice President of Global Marketing. Mark?

Thanks very much, Mark, and good morning. I appreciate the opportunity to have a few minutes to be able to share with you today information about our market opportunity and also about customer challenges. You saw that in the agenda slide. And the reason I bring both of those topics up is because they're really inextricably linked as we talk about this wave that we're seeing with AI. It is, in fact, as Mark started to mention, is we'll get into some more depth on here. It's the challenges of these AI solutions, which does create a tremendous opportunity for Penguin because of the expertise that we're able to bring in helping customers get to their objectives of deploying the technology. At a really fundamental level and our Chief Technology Officer, you'll have a chance to meet in a few minutes, Phil Pokorny has talked about. The very nature of the workloads of AI is fundamentally different than what customers have been doing for decades in enterprise computing. With the advent of virtualization technologies over the last few decades, right, we've seen where customers have tried to take compute nodes and to be able to slice them to run multiple workloads to get more utilization out of those nodes. What we see in AI is that the size of these jobs is so big and it's so complex that customers want to link together hundreds or thousands of nodes to work on a single job, the training and the creation of these large language models. And so doing that is a different set of skills, it's a different set of technologies and processes, and we'll get into the details here on that today. Now obviously, as we all know, AI is expanding across a wide array of industries. And this is a nice laundry list of industries, but it's really as you get down to the next level of the use cases and the stories. And by the way, this has been something at Penguin for the last 2 decades. I've been fascinated as I worked in high-performance computing and looking at people, how they're using the power of compute to drive competitive advantage. And AI is in a completely new wave of that. We hear stories about people using AI. I was in a meeting the other day where someone in the insurance industry was saying, we're going to take drone footage and we're going to use AI to assess the health of rooftops within a community in order to drive a more precise insurance quote. Likewise, even within our own business at SMART Modular, we use AI in the business to be able to automate and drive up the quality in manufacturing by using AI to automatically inspect the quality of parts moving along the line. So it's going to be rather than one or two big bang innovations. It's going to be a whole series, hundreds and thousands of small innovations that AI is going to drive to move the needle on customer business. And what we really see is AI happening in waves. Mark talked about this. We're in the early innings. So as we think about what's happened and really we've been engaged, although it was prior to this whole wave coming along and the development of large language model solutions and large-scale AI systems going back all the way to 2017 with some of our customers. But this early wave that we've seen has been the deployment of systems targeted the creation of these massive models, things like GPT, things like the Llama model that we've heard about. But what we believe is coming is this next wave of enterprise deployment, which is going to stand on the shoulders of this foundational work that's been done, right? We hear about this all the time, people doing fine-tuning, right? And retrieval augmented generation and pruning of these models for specific use cases that relate to those industries that we talk about. And we believe fundamentally it's our ability to help these enterprises to accelerate their time through this challenging process of getting these solutions deployed that creates this opportunity for us. And for both wave 1 and wave 2, the wave beyond that is this wave of inference. It's actually the reason you're creating the models is to deploy these models to do whatever it was that you were trying to solve. And so it's this inference, whether it's in the data center, using today's GPUs or as Mark mentioned earlier, a whole host of innovative GPU and accelerator technology that we're doing early work on that creates an opportunity. And then beyond that, it's our ability to start to push this processing to the Edge with some of the Edge technologies you'll hear about today, where we can move the compute out to the point of where the data is actually occurring in order to speed processing, reduce the requirement to move the data by actually taking the compute to where the point of interaction is occurring. But as we think about deploying these systems, if you think about what a customer is trying to accomplish, they've kind of got two high-level things they're trying to solve, right? We distill the whole world down. The first thing they need to do is to get a system to production readiness and they want to do that on a timely basis and under their established budget. When they get to the conclusion of this, they hit this magical day that we work with towards with a lot of our clients, which is day 1 production. We're ready to take the system confidently into use. But that's just the beginning. As you've got these large systems in place, there's this whole process you need to manage in terms of smooth operations. And the way we do this is actually through a combination of technology, right, which is hardware, software, and also our professional services organization that gets heavily involved with these clients. And third, through a set of very refined processes we've developed just by having done it and kind of had the reps to understand. And so when you think about getting to production, it's all the way back at the beginning. It's processes around designing, building these systems and deploying them and build is interesting for us in that the process of building an AI solution starting in the factory and working its way all the way to the customer is a very specialized thing that we've got a specialized factory that this occurs in and specialized teams who work on this with the customer to make that happen very efficiently. And then when you get to operations, it's about managing that system. But beyond that, it's about anticipating the fact that we're going to need to expand the system and eventually evolve it as new technologies, new processors and new software technologies come into play. So when we think about getting down to the pieces that have to happen, it really runs the gamut of what needs to occur from design to rapid deployment, to working with your customers on their workloads to maintaining the performance and then at the end of this process, to being able to put processes in place that allow that system to evolve over time. Well, what's the downside if you don't do this correctly? And this is interesting as we engage with customers, right? We talk about this concept of achieving your percentage of potential kind of an interesting concept. And in fact, we have some patent-pending technology around software that we could run in order to quantify a value for a customer's AI system to figure out how well are you doing in terms of the potential of the system that you have, and what it's supposed to deliver, and what are you really achieving. But if we simplify it, really what it comes down to is they bought a lot of hardware. And by the way, this is the most expensive hardware most of these customers will ever have purchased to perform IT-related activities. So at the worst case, none of your nodes work. And at the best case, every single node you bought is ready for full production use. Then it's how you interconnect them because remember, every one of these nodes, as we talked about, is working on a common job. So it's not -- it doesn't work if one node works well and another doesn't, the whole system is dragged down to the speed of the slowest runner. So we've got cluster performance across the network. So if you get it just right, you've got 100% of your nodes working at 100% perfect configuration, you're achieving optimum by definition, AI infrastructure value. For the money you spent, whether it was $20 million or $50 million, you're able to achieve that output from the system. But that's not always the case. And customers often that we're getting involved with midstream on their deployments are seeing, "You know what we're seeing, we've got a bunch of nodes that are not able to go to production." So maybe customer has 70% of their nodes that are ready to run in production. And maybe the configuration is such that for the configuration that they've got working, it's only running at 70% of the speed it could deliver. And while you think about, "oh, well, I'm going to get 70% of the value of the $30 million I spent", it's actually a multiplicative effect. So when you think about it, what you're really going to get is 70% of 70% which means I'm going to get less than half of the value of this system that I spent all this money on is going to be actually dripping out the end in terms of value. So what we do with this process designing, building, deploying and managing in combination with our services, with our software that we put in place is to help customers either from day 1 if we start working with them from the get-go or whether we start working with them midstream is to overcome this lost value and to essentially reclaim that, right? And to be able to demonstrate for what you've put in place, you're getting the full value of that. And the cost that it takes in order to achieve that value relative to how much money you're spending on this infrastructure turns out to be a fantastic ROI for customers as they do this, especially given how unfamiliar this technology is to most people. So as we think about these topics, I brought up earlier, design, deployment, workload execution, performance, what you're going to find is that for every one of these, and this list is actually much longer than this, there's challenges that sit behind everyone. You've got data center power limitations that are going to govern what you can do in terms of your system design. You've got an ability to really know, am I ready to run to production? Or am I going to hit some fall starts? From a workload execution perspective, one of the systems, for example, that we put in place with a large customer for that one network had 40,000 network connections, 40,000. The whole thing is interconnected. And if you have a handful of those not working correctly, the entire performance of the system comes down, but think about that something that spans 3 data center halls and it's running slowly. And you're trying to figure out, well, where is the problem? It's this idea of being able to drill in and find the needle in the haystack to fix that and get the whole system back up to production which is extremely challenging. We've got a lot going on, as I mentioned, with network, and in many cases, it is the fabric that holds the whole solution together and then managing this change process. So a lot to take care of. And so we talked a little bit here generically about some of these pieces. But what we wanted to do to kind of bring this to life for the group here would be, I'm going to invite a couple of industry experts up to the stage and they can come on up and grab their chairs. I'm going to invite Phil Pokorny, who is the Chief Technology Officer for SGH and I'm also inviting up Fadi Gebara, who is an adviser and an AI industry executive. But the reason we have Fadi as well is that he's lived this in practicality in real life, putting these systems together and delivering them. And so before we dive into some of the Q&A to kind of hopefully illustrate what's going on, why don't I take a second and just let them introduce themselves. You can get to know them a little better. Phil?

Hello. My name is Phil Pokorny. I'm the Chief Technical Officer for Penguin Solutions. And I've been with Penguin for 23 years and have been a significant contributor to many of these large clusters that we've deployed that we're talking about today.

Hello, everyone. I'm Fadi Gebara. I'm one of the advisers that they brought up here to talk about Penguin. And I've got a bit of an interesting background in supercomputing and a little bit about Penguin that I'll tell you about. My background in supercomputing as I started off actually at IBM, working on a number of the top 500 systems, many of them that hit #1. I spent some time at Meta, which is probably the experience that's most interesting specific to Penguin. I was actually part of a team that was challenging Penguin internally within Meta. So it's pretty common for Meta to do this. They would hire multiple vendors. They would have multiple teams within Meta working on similar projects to see which one would pan out to be the best. I was actually on one of the competitive teams, and that's how I got to know the Penguin Solutions and some of their technologies. One of the things that I did want to highlight, if you don't mind, just right off the bat, because Mark talked about this, that I think it's so important is this concept of redesigning and I think it was actually mentioned in the quote from Meta is redesigning the systems. It's not the same as traditional IT. And I think the part there that you want to call out is that GPUs are really expensive. Typically, 1 GPU can be more expensive than the cost of the entire computer. And where the fundamental shift is coming in is that when you think about previously when you were designing IT infrastructure, you're trying to optimize for the memory, you are trying to optimize for the CPU because those are the bulk of the costs. Well, just 1 GPU can cost more than the entire computer, and you're putting 16 to 32 of these GPUs per node. And that's creating such a cost concentration around those GPUs. If you can't extract the value, you're really going to have a hard time. So leading into what Mark was saying around that 70% by 70%, I think you're being generous, it could be potentially much worse than that.

Thanks, Fadi. So what we wanted to do is just to touch on a handful of topic areas. We'll introduce them, and we'll really just invite Phil and Fadi to kind of comment. And the first one we wanted to talk about is this topic of power within the data center. And within the industry, you probably hear some of these discussions about people trying to locate data center capacity to be able to deploy. It's a big deal right now, right, because the power consumption characteristics of these systems is so fundamentally different. As you kind of see in this chart going from the left, which is the simplest or most traditional enterprise deployments on the left there, where we have these CPU racks with 40 nodes per rack and you can kind of see what's going on as we move to the right with the power requirements for a single rack of compute for these systems. And so what I wanted to do is kind of -- people that have kind of lived through it a little bit, invite them to comment. So Fadi, maybe you can give us a little bit of info.

Sure. This is probably one of the most critical and foundational decisions that you've got to make when you're thinking about your AI cluster. It's really how the power works in your data center that you've got or potentially one that you're going to end up purchasing or getting into your renting and hosting it. And I want to give a little context to this with respect to traditional computing. It's very common to have a 10-kilowatt rack, that's very common in most data centers. In there, you can fit roughly 40 computers, that's not uncommon, you'll see that. Other places can get as high as 96, but 40 is a very common number. So you've got 40 computing nodes that sit within a rack. And most of the infrastructure around that, the networking above it, the power density that comes to it, everything kind of is based on that one decision of having roughly 40 computers and the power consumption of about 10 kilowatts. When you think about a GPU, a single GPU could be 500 watts, 500 watts or more actually, 500 watts quick math, if you do that. If you put 16 GPUs in a single computer, you're going to end up with just 1 computer in a rack. Once you've done that, now you've got this 1 rack that's got 1 computer in it. It starts to become very inefficient in the way that you're using your data center space, becomes very inefficient in terms of how you're delivering your power. So the obvious answer might be, well, just increase the power to a rack. It sounds like a great idea, but there are some challenges that come with that. First, obviously, the power and cooling that come with that are significantly more challenging to deal with. And this is probably the life that Phil lives every day, it's really difficult to deal with that. And it also turns out that if you keep increasing the power of the rack, the cost of it doesn't grow linearly with it. Two 20-kilowatt racks, for example, are much cheaper than running one 40-kilowatt rack as an example. So you really want to make this design decision in terms of where you put your power and how you emphasize it per rack, per the GPUs in a way that's really going to optimize that cost extraction of the GPU. So I talked about earlier, getting the value of the GPUs. You really want to pick that decision carefully. It's a challenging decision and a lot of companies have to deal with it. And if you're shoehorning into your existing data center, you really need some ability to have some expertise to help you think about to make that best decision.

That's great. Phil, do you want to touch on that?

Yes, exactly right. That a typical data center in the past might have only been 3 to 5 megawatts, it was kind of a typical unit you could rent from the data center providers. But now we're seeing 15-megawatt up to 20-megawatts are starting to be table stakes. And that's been driven by these ever-increasing power levels per rack. But working with Penguin, if you have an existing data center space, even though it may be slightly less efficient, it might be significantly cheaper to you for us to co-design your new AI cluster to fit in some existing data center space that you have rather than having to go rent some new data center or outfit some new data center or upgrade your existing data center. And that's where the engineering expertise and the design that we do in our design, build, deploy, manage sort of model can be a real benefit to customers. It's sometimes said too, that every tech has to have an auto analogy, I don't know if any of you have Teslas and installed a home charger and maybe had to upgrade the electricity in your garage so that you could actually power the Tesla charger because the Tesla draws so much electricity, so much more than your dryer, than your stove, then your range, your air conditioner, any of those home appliances. That's a good analogy to what we're describing here.

Yes, it's interesting, Phil. And so in our Penguin business, one of the things we've seen, as Phil mentioned, is advising customers on the actual data center design but also an area where we're getting increasingly involved is just helping customers, even if they're moving to a co-location facility is to have our finger on the pulse of what's available and to be able to assist them in that process of locating an appropriate data center to place the system that will work on co-designing. But we talked a few moments ago, as I talked about criticality and networking. And the next area really is networking within these systems. It becomes the thing which governs the speed at which your entire system is going to run. And that's why we put up here. The network is the platform. And so what I wanted to do maybe is, and I'll ask you Fadi to touch on again, what's your experience been in terms of some of the network deployments and the criticality of that and the challenges?

Yes. I think power might be the one of the more primary solutions the network is very close second or if not just equally as important, as you mentioned there. And I think when you talk to a lot of people, they say, "Hey, we've got a data center, it's full of computers, why isn't it a supercomputer." And I think the thing that most people don't really comprehend completely, unless you're really in the nuts and bolts of it is supercomputer really is the network. That's what makes it a supercomputer. And to give you an analogy of how that works, and I think this can maybe best describe it, we've all used calculators. If you punch into your calculator, it gives you the answer immediately, right? So how fast can your calculator go? Well, really, it's a function of how fast your fingers are. It's not the compute engine that's the limitation. It's how fast you can input. And really in this analogy, your fingers are the network and the compute engine is the GPU, the calculator is the GPU. So unless you can type in faster, the GPU will always be able to compute more than you can put into it. So the network there really does matter is the driving force of it. And these networks are very complicated, right? They're not the common kinds of networks that you might see in a traditional IT rack where you have one networking cable going to one computer going up to the top of rack switch and then on, you'll see very complex networks, topologies that are associated with it. The HPC guys have been developing these for years, and it turns out these AI workloads are even yet more complicated because they're more chatty than some of the typical nearest neighbor kind of style networks that you might get from an HPC market. So I think the expertise that you've gained from HPC is immensely helpful and then it's one step up yet more complex on the AI side.

Great, Phil?

Yes. No, it's a great point that the computation is the limit, the last machine to provide an answer to the others because they're all coordinating together, they all need to share results. And the last machine to answer is dictating the pace at which the whole cluster can operate. You mentioned virtualization from the last 10 years where those workloads are not correlated, right? Those are client server kinds of workloads. But instead of communicating externally to a client, these are now clusters that are designed to communicate inside between the nodes in the cluster, and that's a dramatically different communication pattern.

Yes. It's interesting to see again how the complexity of this network plays its way in. But as we move beyond kind of the complexity of power and networking, you start to get now to the actual physical process of getting these systems into play. And in some cases, we work with clients from the beginning all the way back to the design. And one of the things we do is we work through that is to put in place before we ever get going, a definition of what does it mean to be ready to go to production. And then we work through to that so that when we turn the system on in production, it's ready to go. And so we're trying to avoid this idea of false starts. And I wanted maybe, Phil, we'll kind of ask you to chime in on the challenges involved in this and some of the things you've seen customers work through.

Yes. This is one of the really exciting things about Penguin is that we are kind of the full service provider, right? So it starts with design, build, and now we're sort of talking about deployment. And in deployment, it's important that you've got those design documents. You've got those build documents so that as you go to deploy and you're starting to connect things together, everything is labeled appropriately so that you know where to plug the ends in. But then also, once it does come up, you can query the network electronically, extract the connectivity information and be able to verify that what you actually deployed is what you designed. That then also serves as the basis for now ongoing benchmarking of the system before you turn it over to users, finding a select set of users that you can use to work out all of your other business processes in terms of how do you authorize users on to the cluster? How do they get their files on the cluster? How do they get their applications on the cluster? How do they view their results? And so having a few key willing guinea pigs, if you will, to help you debug the cluster is an important part of avoiding a false start, where when you go to turn it over to your wider user community, they don't start -- they're not asking you uncomfortable questions like, well, how do I get my files on? How do I get -- and they're not frustrated by you saying the cluster is ready, but they can't actually get in and use it.

That's a great point. Fadi you've probably seen some of that as you've worked on system deployment.

For sure. And this is going to feel completely ridiculous making the statement to this audience, but time is money. I think you all know that. And if you can't get these GPUs up and running quickly, you're losing so much of your value, right? Because these GPUs, every couple of years, new ones coming out. And for the energy that they take, as we talked about power earlier for the energy that they take, you want to get the latest generation in so you can do more computation. So having the ability to get these systems up and running as quickly as possible is super critical. It's surprising, often a traditional system might take maybe 1 month, you can have it up and running, it's pretty common. I've seen these AI systems like the last one I deployed just was plagued with bugs, just couldn't get the thing up and running. It was 4, 5, 6 months before we finally got that thing to accepted testing. So it's very, very challenging. Having things that Phil talked about, just starting off, it seems simple with really good documentation but then having the tooling that can help you get to that level of consistency to know that everything is connected correctly and configured correctly. It sounds simple, but this is the blocking and tackling that you have to have the discipline in order to drive these systems up and get them up and running quickly.

Yes, I'm going to come back to you, Phil, on this next topic because it kind of follows on to this idea of users coming and either they've identified or maybe we've identified Penguin in our management of the system that this is not operating at full speed, but I've got literally tens of thousands potentially of network connections, a host of interconnected GPUs. And top of rack switches a lot going on, and it's this idea of being able to find that needle in a haystack many times, right? And maybe you can kind of touch on some of the work that we've done and also the experience customers have had.

Yes. And this goes back to the percent of potential that we talked about earlier, right? As you're shaking down your cluster, you're going to be trying to isolate why am I not getting the potential that I expect to get. And that's part of that debug process. It also then factors in with when users get on and they say, "Well, I expected my job to run this fast, why isn't it running that fast?" Having a set of benchmarks that you've run on that hardware that you know you're getting 100% of the potential so that then you can start to work back to what is the customer's job doing differently? Why is the customer's expectation wrong? And how can we improve the performance of their application on the available hardware. There's also -- you talked about 40,000 network connections in a large cluster. When one of those is wrong, how do you find it? And part of that is having electronic documentation that you can instantly compare. Is the system actually wired the way? Or did someone in the act of servicing something, get something connected back incorrectly. It's also understanding the interconnectedness of the various parts of the cluster so that you can say, hey, okay, we can isolate this problem to this set of machines, what's common about that set of machines? Or what has changed in that set of machines. And the information about the cluster over time but also the way that it's built is really the key to finding and isolating those problems.

Great. Fadi?

Yes. Phil kind of covered 1/2 of the story on the hardware side, but you've got equal problems on the software side as well. 40,000 cables, you have 40,000 operating system settings that you've also got to deal with. It feels like there's just an endless number of variables that you have to deal with. Performance could be slow. Network performance could be slow. Could be the hardware, it could be the software. And this kind of analysis is quite tricky to do, right? It requires a level of expertise that is beyond your traditional standard operations team or your standard, maybe your SRE team as you might have in a normal business, you need real expertise. You really need HPC expertise to drive these answers. And even the best can't do it without the tools. So often, what you'll see is people are investing heavily in tooling upfront, writing custom tools themselves, working with partners to get the tooling and maybe leapfrogging ahead a little bit to take the advantage of the tooling that their vendors have. But at the end of the day, it's just a sea of choices that you have to make, a sea of settings you have to make. And then you need to make sure every computer in the node has got the exact same settings. Otherwise, it's impossible to track down these bugs.

Yes. I'd like to highlight that, too. Pete will talk about this a little more later, our ClusterWare software and patent pending AI module, which actually monitors the cluster on an ongoing basis and the consistency, making sure that all the hardware in the cluster is consistent is one of the key benefits of running something like ClusterWare.

Yes. Thanks for bringing that up. I was going to go to that point as well. It's that software that we've developed to hit the needle in the haystack, which is also a powerful piece of how we help these customers maintain that high level of operating efficiency. Maybe the last topic we'll touch on really is after all of this, after you get into stable production, you're really at the beginning of the journey, not at the end. As part of our services engagement with customers on these systems increasingly, the proposal we put to customers is for multiyear services engagements with them beyond the day 1 production, but to help them all along the journey of operating and so that they can use AI versus struggling trying to run AI infrastructure. And so what I wanted to kind of ask as we kind of close, maybe I'll start with you, Fadi is, what are some of the other things people think about in terms of operating the system at scale as the thing is a fully living, breathing thing as you have to manage it in a production world?

Yes. This is really interesting, I think this is where the hard part starts to be honest, right? You can get a cluster up and running, you're happy. You've got it configured well, and you think, hey, I've got all this value sitting in front of me, how do I extract it and then you quickly find out stuff breaks, when you've got hundreds of machines like that at any point in time, stuff is broken. There's -- you're never 100% healthy, right? So the strategies that you have to manage and deal with broken systems. This is kind of where the expertise of running these things for a while really matters. Previous systems that I've built, we've had replacement strategies where you've had hotswaps sitting on the side. It can get very expensive. As I mentioned, those GPUs are very expensive. These could be $100,000 nodes easy. Having a few of them sitting on the side doing nothing is not necessarily great, understanding how to manage your system from a making sure that these long-lived jobs are running well. And if they do fail, how to pick up where you left off. So having that understanding of how to do the checkpointing, that kind of thing really matters. And I think at the end of the day, when you operate these again it's not your traditional SRE or operations teams that are going to do it. The teams that are really going to do well here are the ones that have the expertise that come from an HPC background because it's the most similar to it. and then shifting their thoughts over to what AI brings and how it's a little bit different to get that consistency and the configuration and to make sure that you've got good strategies for when failures do happen because it is inevitable, they will happen. Having the good strategies in place is really what matters here.

That's great. Phil?

Yes, highlighting again the issue of consistency and making sure that as machines maybe get pulled out for maintenance, when they go back into production, that they've been upgraded to whatever is then the current version of firmware of software, all the rest of it, so that you don't get a mismatch. That's another one of those sort of difficult problems to solve, that's important that your management tools are making sure things are consistent. But also speaking to the future, right, as the CTO, I'm thinking forward to what can we do to bend the curve, so we don't need a 40-megawatt data center right? Are there technologies and we are working with a number of companies that are trying to create more efficient, more flops per watt types of accelerators that could potentially replace GPUs in some of these applications or targeting specifically AI? For Edge, for inference, for training. And what are the technologies that are going to, as you say, evolve and expand these clusters with over time because this is just going to be your first cluster. We want to be there, as Mark has said, we want to be with you for the long term. We want to help you deploy your second and third and fourth cluster.

That's fantastic. So we'll wrap up this section. I'm going to thank you, guys, and I really appreciate the input that you shared. You can see, right, that operating these systems today in a production environment introduces a myriad of issues for customers. But what we're also trying to do at Penguin is work at the tip of the spear in the next-generation technologies that are coming that are going to help customers meet the challenges of AI as they move into the future. So as we wrap up the section, what I wanted to leave you with is a little bit of a case study that was developed with both a customer, a great customer Shell of ours and a great partner of ours, AMD. And it kind of goes to something Phil talked about, this idea of advanced cooling technologies and some of the innovative technologies that are coming that we're going to start to see, I think, on a more repeat basis as AI evolves into the future. So before we proceed to the next section, we'll go ahead and take a minute to hear the story. [Presentation]

My name is Pete Manca, President of IPS. I've been with the company all of 3 months now. Prior to coming to SGH Penguin, I was SVP and GM at Dell for Dell's Apex business as well as their hyperconverged inferred businesses, that was running large P&Ls there. I saw the opportunity at Penguin, I saw what was going on in the market, and I saw what the people and the technology and the process that Penguin had and thought this is just a tremendous opportunity and one that I wanted to be part of. I'm here to talk to you about how we drive the success for our customers and how we overcome the challenges that these guys have talked about. And they did a great job of talking about all the different challenges that face customers today in this AI environment. Penguin has the processes, the people and solutions to overcome these challenges. And this is what we do. We live it day to day, 25 years of experience of overcoming these challenges. I'm going to come back to this slide real quick. Mark described the loss of value from not having the correct node availability to not having the correct cluster performance. This is really poignant, I think this is the most important slide we put up here of how much customers losing time to value and actual revenue opportunities for these customers. If you think about these systems, as Fadi described, there are hundreds of thousands of dollars per server, per environment, and you've got 4,000 of them, 16,000 of them. You're talking millions, tens of millions, hundreds of millions, in some cases, of equipment. If you're losing half of that value, you're losing a lot of money, number one. Number two, you're losing the opportunity to take advantage of what you bought those systems for in the first place. So this is very, very important. How do we go about solving this? First thing we do is we sit down with the customer and we talk about what's the best technology choices for the outcome that you want. We design it right from the start. After we get that design down, we do a very predictable deployment process. We take the software that Phil mentioned. We leverage that software to help deploy the systems, manage and monitor them. And then we wrap it with expert services, again, 25 years of experience developing and servicing these types of systems. That's fundamentally how we provide time to value for the customers. In short order, we deliver assured AI infrastructure, so our customers can have accelerated AI outcomes. That's really what we're about, right, getting our customers up and running, getting them to value faster so they can start leveraging these AI systems for what their business needs are. So how do we do that? I'm going to talk about two fundamental approaches that we have here. First, for customers who want an easy button, we have a ready to run AI infrastructure called OriginAI. OriginAI is a prepackaged predefined set of resources that allows our customers to get up and run very, very quickly. For customers that want a more custom option, we'll work with them and tailor the solution with specific technologies and different partners to enable them to have a more custom solution for their environment. And we'll talk about a little bit of that of each of these in a bit. Underpinning all of this is our unified process. design, build, deploy and manage. And you're going to hear a lot about that. I'm going to repeat that a lot, right? Design, build, deploy, manage, that's fundamentally what we're about. We take the customer from start to finish with their AI implementations through this process. Here we go. Let's talk about ready to run OriginAI first. Prepackaged solutions come packaged with all the hardware, software, services you need, sparing, the warranties, all the stuff you need prepackaged, customers can start from a very small 256 node implementation, they can scale up to a 4K node implementation. They can increment along the way, start small, grow the business, go to larger, it's faster time to value, it's prepackaged, it's standardized. It allows us to actually have a much simpler deployment and environment provides a good margin solution for us as well. So think of these as prepackaged solutions that our customers can purchase, get up and running quickly. We announced our first one with NVIDIA recently about a month ago, and we'll roll these out with additional technology vendors as well in the future. For customers who want a more bespoke option, who want more tailored, we'll go in there, we'll work with them on what the configuration requirements are, what their application needs are. We'll work with multiple partners, and there's a sample of partners here that we work with. It's not a complete list. But we work with multiple partners to bring together these very complex systems, integrate them together and get them up and running quickly for our customer. I'm going to come back to this again. I think I'm going to spend more time on this slide. So this is really fundamentally what we do at Penguin, design, build, deploy, manage. First thing we do is sit down with the customer, figure out their requirements, help them design the correct solution to solve their business need. We use that experience. We've had 25 years of experience to help design the correct system. Once we've designed it, it goes into build. You think, okay, build, that's pretty simple, just building these things up. It's not simple at all. This is one of the more complex parts of this. We're taking thousands and thousands of nodes, we're pre-building them in our factory, prewiring them, pre-configuring them and testing them in the factory. That takes the complexity out from having to do that on the customer side. Traditionally or typically, you might drop shift your equipment at the customer site and you might send your service people in, you try to wire rack and stack that at the customer site. That's nearly impossible. So we take that complexity out, reduces the risk significantly. Build it in our factory, test it, ship it in rack to our customers and then deploy it, right, get them up and running much, much quicker. That's the short AI capability I talked about, the time to value is much faster. We'll project manage the installation, we'll get them up and running. Think of that as getting up to day 0 or day 1 operations. And then after that is the ongoing management, day 2 and beyond. And that's where our managed services capability comes in. We manage the systems for them. We monitor them. All the stuff that the team talked about with regards to complexity around root cause analysis, determining what the issues are, where your performance might be a challenge, our software can monitor that for you, pinpoint the failure for you, and our managed services will help correct it for you. So again, take one last look at this design build, deploy and manage. Fundamentally, this is what Penguin Solutions does. So you heard Mark talk about the waves of AI, and I'll just skip right to the slide. If you had a you are here thing, it would be right around the middle there. I think we're starting to see the enterprise customers start to adopt AI and start fine-tuning those models. The next massive wave coming up is going to be infrastructure or inferencing at the Edge. And you look in terms of hundreds of solutions for large language models, maybe thousands or tens of thousands for enterprise, you're looking at hundreds of thousands or more at the Edge. And think about these systems at the Edge, let's skip to the next slide. Is they're just as critical just as important as those that run in your data center. In fact, if you look at this IDC quote here that 77% of organizations define their Edge workloads as either highly critical or critical to their business needs, right? So that means your infrastructure at the Edge has to run as pristinely as what it would inside of a data center. It's going to have mission-critical capabilities but at the Edge. So think about how hard that is. You're at the Edge, when you're talking Edge, it could be as simple as retail for point of sales, and I'll talk about -- I'll give you a case study in that in a bit. It could be oil and gas. It could be military operations where you need much more ruggedized environment. And you don't have IT staff out there, right? You don't have IT staff out everywhere at the Edge. So your systems better run, they better be highly available, right? They better be easily repairable, they better be remotely monitored. Those are the important things you need at the Edge. And Penguin can help you there as well. They can help our customers there as well. So we have multiple options at the Edge. First, I want to talk about our ztC Endurance platform. It's our fault-tolerant platform. 99.99999% of availability. This is targeted at customers who can't tolerate downtime, right? For customers who need more ruggedized or more hot swappable type, we have ztC Edge components, ztC Edge products. These products are really built for remote ruggedized type environments, all managed remotely through our software, which I'll touch on a little bit and with our services team. So again, we have an entire portfolio of Edge products, and we're going to continue to build this out because as these waves continue to progress, we really feel like this is going to be an important one. We have -- we're really well positioned to take advantage of that. Let's talk about a case study. This is an Edge case study. Dis-Chem is a pharmaceutical and retail-based company in South Africa. And they needed always on capabilities for their point of sales and the inventory tracking systems. They couldn't tolerate downtime. They had a mismatch of systems, networks storage systems, doing that today, they consolidated down to 1 ztC endurance box per location, 250 of these across their locations in South Africa. And you can see the quote here from the head of infrastructure and no downtime in the stratus provided hardware. That was critical for them. These stores have to be up and running all the time. You need Edge devices that act like your mission-critical components inside your data center. And that's what the ztC Edge and Endurance products do for you. Let's talk software a bit now. You can't do all this without really efficient enterprise class software. You can throw 1 million people at it and go try to solve the problem, but that's not really efficient. What you need is software that helps you monitor and manage your systems. So Phil touched on cluster where we'll start there. ClusterWare is our software that helps you design, test, burn in your platforms, get them up and running, get the correct software installed and then manage it ongoing in the data center, right? So ClusterWare is really the foundational piece of the software suite. We have this patent pending technology called the Assured Infrastructure Module or AIM, think about that as using AI to manage our AI installations, right? So it's doing GPU utilization analysis, workload analysis, does predictive failure analysis. So it's really monitoring the environment and making sure that it's up and running and trying to predict where the failures might be. So again, using AI inside to manage AI. Go around the circle here, Penguin Cloud Central. Our customers leverage Penguin Cloud Central for bursting to the cloud. So if they have capacity they use to burst to a public cloud, for example, they can leverage Penguin Cloud Central software to burst those workloads for excess capacity when they need it. And then finally, fault-tolerant active service network. This is our remote management software. This allows us to monitor and manage the systems that are out there on the Edge. It's a very comprehensive software suite, something that we're going to really lean into going forward as well. It's very important that we continue to innovate in this area. It really sets us apart. All this is wrapped by a very comprehensive end-to-end services portfolio. Professional services to help the customers design their systems, customer care to work on the customers if there's any issues. And then finally, managed services to manage these environments going on. So full end-to-end suite of services capabilities that we can leverage our other customers. We're also a certified NVIDIA DGX ready managed service provider. So that's important. We're actually one of the few in the world that are NVIDIA certified today. So that's a feather in the cap to the Penguin Solutions teams who have been working on these installations and working with these customers for years. Let's do one last case study because this kind of brings it all together. Voltage Park, an AI cloud service provider, they needed to deploy 24,000 GPU at scale, right, 24,000 GPUs, they need help managing this. They came to Penguin. So we need your software, we need your services, we need your capability to manage this environment so we can sell these services to our customers, can't do it without you. And so we went in with an H-100 solution here across 4 data centers, leveraged ClusterWare software to help design and build the clusters and then manage them ongoing and then our professional and managed services to help them manage the cluster ongoing. And I'm going to show you just a quick video here from the CEO of Voltage Park. [Presentation]

Another great customer testimonial. Look, in closing, Penguin's got the right people, the right processes, the right technology to really help customers through their AI transformation. And we've shown you a couple of customer clips today. I think they're great testimonials. Again, I'll go back to our design, build, deploy, manage philosophy. This is really what sets us apart. And again, Penguin Solutions has the right things. As you heard earlier, as I transition to Andy, as you heard earlier, memory bandwidth is an important part of AI systems. So here to talk about that and how we're going to help solve that problem is Andy Mills.

Thanks, Pete. So we're going to take a little bit of a deeper dive into memory and what it means in particular for AI. There's a lot of complex issues that arise that are driven by AI as we move forward into this new era. And one of the things that you may have seen plenty of times in this chart, some of you may have seen this before, I'm not sure, but let me just explain it a little bit. You've got a plot here of the number of parameters that models typically need in AI. We're going to relate that to memory here in a second. And then you've got from 2015 to modern day here. And then you've got this linear kind of growth here of what is actually being deployed in terms of GPUs and memory. So let's just take one of the data points just before. You can see there's kind of two areas here, the compute limited era and you've got the memory bandwidth limited era. And you can see we're well into that right-hand part of the curve now. Look at the growth. I mean, you've got in the early years here, models were easily able to be fit into a single GPU, you could run most of these models, multiply them out across multiple GPUs. As you cross that barrier or that line, if you look at GPT-2, probably GPT's the most famous, they've got 1.5 billion parameters in GPT-2. Just going to GPT-3, it rose to 175 billion parameters. Now those are pieces of data you've got to crunch in terms of GPU and memory. Once you cross that line, you now have to spill outside multiple GPUs. You go into the network. We've heard about the network before and you need a lot more GPUs to solve the problem. So they call this gap that's emerging here the memory wall. And that's one of the complexities here that we're actually working to solve and as you probably appreciate those of you who've worked with the company for a long time, we've been in memory a long time. We know how to do a lot of things with memory that really play well into this. And in fact, if I summarize it, and this is all data. I literally was reading something on the plane coming out. The newer data is over 2 years, you've seen a 750x growth now, not in the number of parameters versus only a 2x growth in memory capacity. That's just in the space of 2 years. So we're in this rapid, rapid growth cycle. And it's impacting memory in 3 different ways here. Reliability, I'll touch on it in a second, something we've been doing for a long period of time; scalability, how do you scale up your memory and get it to deal with this rapid growth in the number of parameters you got a process per GPU; and lastly, performance, we've got some really exciting stuff going on through some of the investments Mark referred to earlier as well in that area. So let me touch on those 3. First one, reliability. We've had a technology out there for a number of years now called Zefr, it means zero failure memory. The zero failure refers to the test period that we run in our factory, we look for no failures. If you have any failures whatsoever in the memory, we're testing these things under stress and under duress. If they experience one failure, they're out. And the reason we do that is because if you think of AI training models today, they can take weeks and months to get through the training period. The last thing you want is memory to drag that or make that fail process. So having reliable ultra-reliable memory is absolutely critical. And we literally take temperatures up to 70 degrees. We're raising it really high and then we run HPC workloads on these for 7 or 8 hours and again, a single failure means we kind of push that to one side. It's not a bad memory per se. It's just not meeting the minimum bar we think is necessary. And Penguin deployed most of their servers now this way in our Penguin service solutions group. And we have a number of external hyperscalers also using us now. Now this involves using off-the-shelf memory modules as well as our own modules that we make. The next one that's kind of -- is really new, and I think is exciting for us is we've just launched some new products this year, which are based on CXL. CXL means Compute Express Link. It's actually a special computer bus that allows you now to attach memory in a much more plug-and-play fashion. So we're now addressing that scalability issue and I'll show you an example here in a second, but we just literally launched and we're now shipping for revenue our first products based around CXL. This is an exciting growth area for us because we are literally having most customers who are building anything with AI or even just classic enterprise, big memory models are now looking to CXL as their primary method for expanding memory beyond what the CPU itself can provide. And we'll show you a specific example of that in a second. The last one, I think, is really, really where we start to get into the innovation area. We've been partnering with a number of people in the industry, but silicon photonics has been, in my view, something that's been in the R&D lab for a long time. It's really coming out to shine now in telecom and now in memory. What it allows us to do, if any of you are familiar with HBM, high-bandwidth memory, we've been -- the memory industry has been trying to scale up memory as best it can. It's been going to 3D stacking technology. For example, the die inside memory now, there's several stacks of die and they do this with high-bandwidth memory to try and increase the parallelism. How fast can you get data into a stack, well, rather than just going in through 1 door, you can go into 8 at once, for example, right? Get more data in and out. The limitations of that are, you have to put that HBM chip on the same substrate or the same -- right next to the die of the GPU, literally 1 to 2 millimeters. So from a scalability side, that's a real complex problem to take and say, I need to now scale that somehow. So what we're working with in the lab, this is kind of targeted for like a 2026 kind of time frame product, we're starting to work on solving the problem and decoupling that HBM and putting it in an external memory appliance. Now that's actually going to open up because you can now make that appliance backed up with DDR5. You can put a lot of other memory technologies behind it. You're taking the problem from 96 gigabytes up to terabytes of storage. It's no good just doubling the memory, you have to quadruple, you have to 10x it, you have to have 50x it. So a lot of exciting things going on there. So let me actually start with a case study here. We're actually actively working this with our customers, so we can't name who they are right now until they launch. But the industry is AI-based analytics. There's a lot of folks out there now trying to take basic service and just put more memory in them. This is just standard CPU memory expansion here. And what this product here, and actually, we've got it brought one with me, just you can see kind of physically what it looks like. This is our CXL add-in card that allows you to add up to 8 additional memory sticks or DIMMs inside a server, and you can put multiple of these inside the server. So in this particular case here, you can put up to 8 of these things or 4 typically, it depends what your power budget is inside that machine powering heat that we've seen that before. We got to keep balancing all those various pieces. The challenge the customer had here was without this. If you look at that memory, we won't be able to see it on this diagram here, but there's only about 24 of these little DIMM sockets. These sockets that take these individual memory sticks here. You only get 24 of those. So the industry today has been using these 3D scaled-up memories to put in those sockets. They're expensive, there's not a linear increase in cost. It's like a quadrupling in cost. Once you go to 3D stacked memory, you really want to use the cheapest and most common memory, which is what this targets here, which is 64 gigabyte or 96 going to 128 gigabyte sticks. Putting this inside that box are allowed us to take the whole DIMM count from 24 to 88 with a simple adding card, keeping the GPU power the same. That brought the cost down to about a 1/3 of what the original cost was, a 66% cost reduction because the only way you could do this in the past was to add a second server with more GPU, with more CPUs in it, with more memory, and that was the only way you could scale in the past. So this opens up a whole new exciting avenue for us in terms of scaling memory and eventually, as we showed before, scaling the HBM levels in the future. Okay? And with that, I will hand over to Jack, our COO.

Great. Andy, thank you for that. Well, really good step here with CXL and the advanced memory products that Andy talked about. And we've heard a lot of great information today about the compute, our memory solutions for everybody before you. So my job today is to take all that now and turn it and how that translates into a long-term model that we'll show you towards the end of my presentation here. But really, we've got -- our value to create strategies, we've got these 3 pillars here that we talked about here. The first one is really profitable growth. As Mark has touched on, if you look at the company over the last 3 or 4 years, we've gone through one of the worst memory downturns ever, and we've been trying to transition back from a holding company to an operating company. But all that time, we've been profitable. Every single quarter, we've been in profits and we generated profits. So as we focus on this growth, one thing you'll see from us is that we always will focus on profitability. We're going to drive profitability, and we're going to do that as you're going to see some higher-margin recurring software and services kind of streams come in the future. That's what really going to focus on, we'll talk about that. So we are going to transform our business to a more software and services focused company. And we're going to do that by investing to expand our capabilities in these areas. But as we do that, we're still going to maintain our fiscal discipline and as Mark talked about, our execution. We are an operating company. We will execute as we go forward. So we're going to continue to focus on that as we're driving the growth of the business. And of course, we will deploy our capital in a very prudent manner, right? We want to make sure we maximize shareholder value with the funds that we have to maximize the investment but also to get the best returns for our shareholders. So if you look at the last 4 years, kind of Mark talked about, about our revenue, we grew from 2024, a 13% CAGR. But the really important thing here, if you look at what really grew, what really grew was a compute solutions memory -- I mean revenue, sorry about that. That thing almost doubled in the last 4 years. And what grew that revenue, if you look at services, services grew to 36% CAGR at the same time. So really the services revenue really started driving the growth in our overall revenue of the business. If you look at what that translated to, look at our non-GAAP gross margins. So in the same period of time, our margins grew from roughly 20% up in the 30s, so by 11 points, and that was really driven by this higher margin service type revenue that we added in this period of time. And also, that translates now into profitable growth, which means our operating margin also went up. So we kind of talked about our pillar of profitable growth. You'll see it here that our operating margin now is up almost 6 points to 10% as Mark showed before. So really, just to make sure we're maintaining our fiscal discipline, operating income also grew at the same period of time. So we will continue to grow. But as we grow, we will grow our margins. And at the same time, not only we grew margins, look at our cash. From 2024, our cash was up over $380 million of cash. We increased that. But at the same time, look at the debt, we knocked our debt down by 1.2x. So a little about 1.4x EBITDA right now, right? So I think this shows you the kind of fiscal discipline that we have as a company. We grew the revenue, we grew the margin, we grew our cash and our debt. So we will continue to grow in this manner as a company. Okay. One thing you've heard about in this whole presentation is how we're going to invest for growth, right? So I think at the 4 main things that we're going to look at is we're going to expand our software offerings, right? We want to offer better offering to our customers and provide them more value. It's key to us is we have to add value to our customers so they want to come back to us. We're also going to grow our service capabilities. As Pete mentioned before, we had a service not only at the cloud but on-prem and at the Edge, right? So we have to provide better services at all those places. And to get there, we're going to have to go invest and go to market, we're going to have to invest in sales, we have to invest in marketing, we have to invest in customer-focused engineering, people go talk to the customer. That's going to be big for us. So we can go out and win these new customers that we're trying to go get. And Pete mentioned the Edge offerings we have today, but we definitely have to expand these so we can meet the AI needs as it really proliferates out of the Edge here. So I thought right now, you talk about investing for growth and all that, I mean, we talked about the transaction yesterday, talking about $200 million, we're going to get here. 200,000 preferred shares, but this is going to help drive our growth here. So for the details, everyone who didn't see the press release, there's a conversion price of about $32.81. It's based on a 30% premium to the volume-weighted average closing price over the 15-day period. So if you look at the 15-day period ending July 12, 30% over that price. We will pay a 6% annual dividend. And we got all the little Ts and Cs at the lawyers here put together in the whole agreement. But we won't go through all that with you here. But really, why are we doing this? Because what we need, we want funds to be able to do what? We got a partner to help us, but also accelerate our software development as we've talked about, right? We need to build out a focused go-to-market organization AI here for the team. We have to scale our services more. And then we need to really expand these Edge products that Pete talked about earlier. So we're going to make a big investment in AI-driven technologies, and we'll also look at strategic M&A to drive this business going forward. Of course, this is subject to all your kind of closing conditions, and we expect, hopefully, by the end of calendar '24 to close this transaction here. Am I okay on that? Okay. So now let's go to the good stuff which you came for. What do we think we're going to do in the future here, right? So if you look at our non-GAAP gross margin, right now, we're roughly 32%. Our goal here in our long-term model to get that up to 38% to 40%. Our goal here is to grow the margins, how we're going to do that? Well, it's a higher-margin recurring revenue. It's software, it's services, right? That's what we're going to go drive get our margins up because one thing as we've talked about, we're not a hardware company. We don't just sell hardware. We sell a solution to a customer that has hardware, it has software, it has services. And those seem to be higher margin will drive that up. And that will then translate into higher non-GAAP operating income. We expect to grow that up in the 16% to 18% range over the same period of time. And doing that, we will, of course, keep our fiscal discipline that we have in place. We're also going to allocate our resources, right? We're going to put the resources where it matters. Where we need the resource, that's where the people are going to go. We're not going to invest in products or different things that don't add value to us and our customers. That's going to be a key as we go forward here. And then if you look at the long-term model over now, so our goal here is we think revenue will be a high single digit to low double-digit growth for us here. I talked about the non-GAAP gross margin to 38% to 40%. And then a non-GAAP operating margin in the 16% to 18% range. So I think we will continue to look at our costs. We'll continue to maintain them, but the whole goal here is to keep increasing shareholder value as we will execute here on our long-term plan. And then if you look at our capital allocation, what are we going to do with our capital? Well, the first thing we've talked about, and I'll probably talk about it some more, but we are going to invest. We're going to invest in our go-to-market. We're going to invest in our software offerings. We're going to create more software offerings. We have to make sure we have all the software our customers need for up the stack, whether it's on-prem, the cloud or at the Edge. We will invest in that. And then we need to accelerate our -- will help accelerate AI solutions as we do that as well. Balance sheet. We will maintain a strong balance sheet. If you look at our levels right now at 1.4x EBITDA, so we'll maintain conservative leverage. And we also will reduce leverage when warranted. So this year alone, we paid back our Term Loan A by $112 million already. We'll continue to make sure that we keep our debt and all that under control. And then we're going to continue to look for inorganic opportunities to grow the business, whether it be software products offerings as we go forward, services as well. But also if you look at Brazil, right, we've also shown that we will divest a business that doesn't make strategic sense for long-term value. So we'll keep looking at the company. And if we have things that we don't think makes value, we'll look to divest those as well to make sure we're on our strategy, and we're executing towards that. So I think if you look at it going forward here, I mean, we're in the really early stages of AI. We think we're very well positioned here to leverage solutions that we talked about today to drive profitable growth here as we get going, right? We're going to increase our operating margins, we'll generate cash, but we're also going to maximize shareholder value in doing this. So with that, I'll turn it back over to Mark for closing remarks.

Thanks, Jack. So I think you can tell over the last 90 minutes, those of you who look at the vantage point of 2020, what a different company. And not only the strategy piece of it, but really the production of it, the development of the products and solutions, the customer engagement, the proof points that we're on a path to solve for complexity. So looking ahead, themes that you might take away from today. One, very few companies have what we have in terms of the cultural mindset to help our customers at the level Penguin can, both at a hardware design level, at a software level and a managed service level. Innovative solutions, oil-based immersion cooling. Pretty amazing. Expand software and services. Well, today, when you think of the platform we have coming from HPC that we modified and developed for an AI world, a platform that we have in our cluster management software is going to enable future growth for us and adding more value to our customers. Beyond cluster management, there's areas like ClusterWare that we can invest beyond ClusterWare like an orchestration, for example, or security. We have a road map of what we want to do for short or long term, that will make us even more sticky and more valuable to our customers. Scale new customer acquisition. Look, every earnings call I've been on since I joined the company, we've talked about high-performance compute and the lumpiness and cyclicality of that business. Our plan over the next 3 to 5 years is to tease out that lumpiness, add more customers, and we're doing that today. We will continue to invest in go-to-market and customer engagements to enable us to grow our base, grow our top line, but also diversify out the noise, hopefully, over time for what has traditionally have been a lumpy industry in general. And last, but not least, the theme of being a prudent managed business, profitability, operating efficiency, process orientation. Go back to the initial theme I mentioned before we got into the AI piece of the day was execution. As a CEO and with our leadership team here who have our commitment to manage the company well and to keep on delivering on profitability and cash flow generation for shareholder return. So again, thank you very much for taking the time and joining us today. We're now going to proceed into the Q&A portion of our agenda. And I'd ask my team members to come up and join me on stage, so we can take your questions. So it looks like we have microphones available. So if you have a question, please raise your hand, and I'll start navigating and then we'll get it to the right team member.

Thanks, Susan. Harsh Kumar, Piper Sandler. Maybe Pete or Mark, you guys could talk about the scale or scope of the Penguin, how many customers you guys have? What is a typical deployment? How long does it take? How long is the relationship typically from start to beginning? But just mostly about what kind of customers you work with over there?

Sure. Maybe I can start and then Pete, I'll ask you to join them?

Sure.

So when you think about -- let's start with the customer engagement model. Typically, that model is like a 12- to 18-month engagement, and a lot of it is upfront is to identify the customer need requirement. And then effectively, we go into a design part of our engagement to design the solution for a specific customer environment. After that, something that's kind of gotten a little healthier is the supply chain piece of it for a while, it was a little bit extended, but we're starting to see more normalized, not perfect, more normalized supply chain. And by the time we finish integrating testing and then deploying, we're probably out into the greater than 12-month time frame. And depending on the level of complexity, it could be as long as 15 to 18 months. And so that's kind of how the cycle breaks down. From a customer set, Mark showed a chart earlier about all those industries. It's really fascinating, it complements the chart I had about like 80% of global enterprises will be deploying GenAI in the next couple of years. It's just staggering. Where we've found a lot of our success today is in industries such as hyperscale or even Tier 2 cloud service providers, oil and gas, certainly education, financial markets are a big target for us. And so we're honed in on a couple of 4 or 5 sectors that are very big for us, and we're choosing them based on our capabilities, our relationship. As some of you may remember, when we acquired Stratus, they were in 50% of the Fortune 100. And that's something we continue to try to work on and try to scale our measure of importance to those type of customer relationships, and they were very heavy in the financial sector as well as trading and communications. So for us, it's been very targeted. Openly, HPC, for those of you who share the same hair disease that I have in terms of being gray and older, HPC was a world -- where it was primarily an education, government early on, research laboratory work, and it really wasn't so much vertical solutions. It's very focused and concentrated. The difference over time is that's evolved more so into commercial. As a matter of fact, we've gone from what used to be a very balanced 50:50 type model on go-to-market resources, federal versus commercial to, we're very focused on the commercial, and all of our resources ads are in this space because the enterprise demand is so significant. And so that's how we think about the customer opportunity. and very engaged in terms of scaling that. Now we're kind of a smaller company when you compare us to some of the bigger hardware-only players, and the opportunity for us right now is to go expand that way. I would say that at the beginning of our fiscal year, we talked a lot about expansion and growing customers, because these are like engagements. We're not just selling widgets through a channel. We're actually -- as you saw today, we're working with customers to solve the complexity of their AI deployment. These are not customers that you add hundreds of customers. We're talking about customer alliance partnerships. And I was very pleased so far with what we've done in our fiscal year '24 in terms of new ads. You heard from one of them today, 24,000 GPUs. That's a significant deployment. And we probably didn't do a good enough job highlighting that. Everything we're talking about in terms of solving complexity, they were trying to do already. It's not even our hardware. Pretty powerful stuff.

Yes, I think you covered it well. The one area that we are going to focus on more and this example Mark is talking about the Voltage Park video that I showed, that was a software and services deal. That's an example of a customer that we won in a quarter and actually took revenue in that same quarter. And so as we increase our software capabilities, increase our services capabilities, we'll look for more customers that need that kind of help, and we can come in and help deploy and manage their environments much quicker for them. So Mark covered, I think it really, really well. These 12- to 18-month processes can be lengthy, they need our expertise, but we think there's opportunity also to get some quicker wins here with software and services, and we'll target those as well.

I get asked a little bit about like the role of hardware. We have this portfolio to solve the complexity, and our customers are going to drive our behavior. And so we still are an innovator in hardware. I mean we didn't talk a lot about our hardware road map today, we've built some prototypes today on route 5 for different type of AI application environments. And we're going to continue to lead. One of the deals we won with the government, was with a third party called [ Nic Silicon ] and we're out developing solutions. So we're on the leading edge and hardware, we're not going to apologize. It's just we are a solutions vendor with that type of mindset. And if you think about the best data point I can give you is look at us relative to hardware vendors. Our gross margin is substantially higher, somewhere in the area of 2x, depending on the quarter and the data, but our gross margins are substantially higher than our hardware-only large-scale competitors and that we're going to have the discipline to stick to that because that's a good proxy for are we offering more value to our customers. Question here?

Kevin Cassidy, Rosenblatt Securities. A question about just trying to model your agreement, so say the software agreement with Voltage Park, it's 24,000 GPUs and you've got 75,000 GPUs under management currently. Does that scale exactly? And so is this like 1/3 more business? Or maybe can you give us a structure of this?

Yes. I think -- a couple of things I would comment on. I think it'd be a little bit aggressive to say it's going to scale one-for-one for GPU. So it's not a one-for-one basis. And also remember that the revenue that we got for the first 75,000 was primarily with our hardware. So there's a little bit of apples and oranges in terms of comparisons. I would say this, though, is a really attractive business for us on the software services. It's not -- again, I think the way to look at it, if you would dissect the anatomy of our customer engagements, there will be customer engagements that they're going to be one throat to choke. Penguin designs it, hardware, software and managed services, and it's all in one. And there may be some bigger implementations. We're talking to more and more customers that, hey, we have bought this hardware. We just can't get it up, we cannot get the hardware working. And so that will be an opportunity for this other sales motion, which we're talking about. Furthermore, we've actually had people say, "Look, we've already deployed. We've got it up, we just don't know how to maintain it and make it long-lasting sustainable. Then we can just plug in our service model." so the solution says a module solution set. And as I said earlier, the customers are really going to drive our directive. So it's really hard to say one for one. You guys are X revenue today at 75,000 because there's a different mix of products and solutions there.

Maybe just on a follow-up with that. When you mentioned your own hardware, would there be a case where you decide to use one of your competitors' hardware just to give the right solution?

It's interesting because I want to stop from naming competitors, but if you look at the landscape, one of our competitors actually got out of the business in their back end. Clearly, don't have the software expertise and the scale of it all as an example. Another one has been really a host of acquisitions and a little bit of a brain drain and they don't really have the host necessarily to do the capacity to do it all. And the third is just a very efficient operating company, but hasn't really invested that way. So the overwhelming answer to your question is absolutely. Because for us, as an example, the solution set is, the customer minds a deployment success. Whatever we can do to enable that, that's great, and we're having conversations to further accelerate our ability to do that and also scale our go-to-market more efficiently as well.

Can I jump in real quick?

Sure.

I think Kevin, remember too, this is all recurring revenue, right? So as we get more of the software services, it's revenue we take over time. This will help to kind of smooth out the lumpiness as well. We don't get it in one quarter. We'll take it 12 months, 24, 36 whatever the contract is. So for us, it's much better vision to the future and also the recurring revenue streams so that's why we like this kind of business a lot as well.

So that is going to be the model Jack talked about is going to how we're going to try to build this business over the next five years. Is building out more and more of that quality of revenue. Obviously, we're coming from what has traditionally been a hardware systems business. And we're starting to see some signs of success. I think over five years, you'll see more and more of this type of solution sale, revenue recognition approach that Jack said. We're starting from a hardware business today, obviously, and we've got more and more services. We'll continue to invest in scaling this.