Applied Digital Corporation (APLD) Earnings Call Transcript & Summary

Welcome to Applied's first ever Investor Day. We're so thrilled you're all here. Applied Digital is not just a name or a corporation, it's a vision. It's a commitment to redefining how first movers, innovators and digital leaders scale into high-performance compute. It's about collaboration, innovation and reshaping the digital infrastructure landscape. Today's Investor Day is a testament to the immense progress that's been made, and it's a tribute to your belief in our mission. It's a celebration of the brilliant minds behind our success, our dedicated team, our visionary leadership, and, of course, you, our valued investors. My name is Erin Kraxberger. I spearhead the Customer and Investor Relations efforts here at Applied, and I'm thrilled to be your host today. Over the next few hours, you're going to hear about the transformative projects, partnerships and technologies that are propelling Applied Digital to new heights. We have some powerful updates from our next-generation data centers to our commitment to sustainable energy solutions. First, we're going to kick it all off with our Applied Digital's Founder and CEO, Wes Cummins. He is excited to share an update on the state of the market. And please help me welcome him to the stage.

I'm going to sit, if that's okay. Thanks, everybody, for joining us here today. I'm going to kick off by giving just a brief history for those of you who don't know of the company and kind of where we've been and how we've got here and then what it looks like for us going forward. And today, you're going to hear from much more detail from some of our other execs around our AI cloud business and specifically our HPC data center business and a little bit on our blockchain data centers as well. So the history of Applied. We -- I founded the firm in early '21. It was -- it's had a couple of, I would call it, iterations since then. So in early '21, we were going to be industrial scale, Ethereum mining and we formed a partnership with a company called SparkPool, at the time was the largest Ethereum miner in the world. They controlled about 25% of the -- they were -- 25% of the Ethereum hash rate in their mining pool. So we partnered with SparkPool and I think that partnership happened in March. We raised money in April. By the end of May, and this was all expected to be deployed in China, by the way. By the end of May, the Chinese government crackdown on crypto. And so at the time, we had about 70% of crypto mining going on in China, I think, less than 5% in the U.S. And so we saw an opportunity of a big migration from Bitcoin mining, specifically from China to the U.S. We'd already made these relationships with these partners, they came to us to see if we could go build out capacity. We had already found a guy who finds power in the U.S. He's been doing it for about 5 years, one of the -- I still think the best in the business. We found a gentleman who had brought developed and brought online one of the first, I think, the first mining facility in the U.S. It was for a company called MineCo at the time, which you may know is Core Scientific. So we were able to hire those 2 plus others to go and develop these. We signed our first contract for power in July of '21. We ended up breaking ground for our first facility in September of '21 and then between September of '21 and now, so roughly 24 months, we've procured, done all of the development to bring -- to build and [indiscernible] mining almost 500 megawatts of Bitcoin data centers effectively. So that's -- by any measure, that's a massive amount of power to go and build out and do all that procurement, and we did it really quickly, and we've built a great team around that. And so we have that business that's moving forward nicely and we expect to energize our Texas facility, we said by the 23rd of this month. So we're close. And then in around April, May of last year of '22, we started thinking about what else can we do with these power assets. And we decided that designing an HPC facility, a high-performance compute facility, new design, complete new ultra-efficient design that we could co-locate with our Bitcoin facilities. But at the time, you have to remember, this is a niche market. And so it was -- the idea was -- we'll do 5 or 10 megawatts of this style of capacity at each one of our Bitcoin sites, and this will be a nice diversification and kind of market expansion for the company. And then in -- about this time, October of last year, we put some software partnerships in place and pieces of software to run a cloud service. We did that specifically to -- for one purpose. We're going to run a cloud service out of our own data centers. Because we thought we'd have to be our own first customer and show people that these data centers work. So it was again around selling the data center capacity. We started our first customers in December of last year. We're running mostly universities doing small, which we call at the time machine learning or deep learning. And so we started that in December of last year. And then as this year progressed, ChatGPT hits the market in December. It wasn't completely obvious to us at that point kind of what the ramifications were about. And then NVIDIA introduced the H100 in March and then it became really apparent to us in April, what was going on. So kind of mid- to late April, we were out marketing our data center capacity and we ran into these cloud customers, people who wanted a cloud service. And we ended up signing that first customer in May. I think it was May 15. And then we leaned in on cloud business from there. And so we've signed up additional customers. And so now when I look at our business, the other piece that we had to do was we had to -- I went back to my team and said, we're not going to do 5 and 10 megawatts on the sites, guys, we got to go back to building hundreds of megawatts of capacity. And luckily, we signed up a significant amount of capacity to go and do that in both North Dakota. We've been working in Utah to sign that up. And so we had that ready to go. And so it brings us to where we are now. So we -- at this point, we've said publicly that we've signed up $378 million of annual contract value for AI customers. Brad will go through later the groundbreaking of our Ellendale site for our new data center. I don't want to steal that from him, but he'll be able to talk about that. But I wanted to set the groundwork for what we're doing and then turn it over to our CFO and then some people who are doing these specific segments to give you a much better idea of what we're doing. And then I'll come back and talk about the market and what we see for demand afterwards. So let me give that intro and then I'll turn it over to David.

Good morning, everyone. Thanks for joining. As we mentioned, I'm David Rench, CFO for Applied Digital. Yes. APLD is an amazing company, and it's really the team that is the magic behind of how we've accomplished so much in such a little time. I really would like to thank everyone on our team that's made this possible. Legal disclaimers. So APLD when you think about it, we have 3 very distinct business units within the company. The first is our newest line of business which is an accelerated compute or supercomputing as a service. We've ordered 34,000-plus GPUs from NVIDIA through our partners, Dell, HP and Super Micro. Our customers are some of the most cutting edge and leading players in the space. We're very excited to be growing this business and excited to where that's going to lead. The second vertical is our -- and our CTO will dive into a lot deeper of what this entails in the morning, but it's -- I'm sorry, the next-generation data centers really solve the issue of where accelerated compute will be hosted. NVIDIA has really changed the game of what compute looks like today and tomorrow. And so the vast majority of the existing data centers cannot house this type of compute. And we're answering that question of where it can go and what that design looks like. Finally, our existing business model of colocation for hosting for Bitcoin miners. Although we're not expanding this business any further, it's a great cash flow business for us and runs very smoothly at this point. Marathon Digital is one of our largest customers there and continues to be a great business for us. Again, amazing what the people have been able to do in a short period of time. We started with 2.5 people and now we're over 170 employees today and really built out a team that answers and brings the resources to us. We continue to attract the best talent and be able to develop a great team to continue to execute. Over the last few quarters, we continue to accelerate our growth, and that's mainly because the contracted revenue continues to come online. You can see it through the history where we had Jamestown, we had that little hiccup where the transformer on the power company went out for a month or so, but we were able to get that back and continue to ramp. This shows Ellendale in the final quarter, beginning to recognize some accelerated compute revenue, with the energization of Garden City this month as well as us standing up additional accelerated compute clusters that trend should continue in the direction you've seen. So we're very excited about that. Again, as a reminder, the vast majority of our revenue is contracted take-or-pay and long-term reserve. So we'll dive into, again, the 3 segments. The blockchain colocation data centers. We have 480 megawatts of capacity, 280 are online today. We've announced October 23 is when Garden City will start energizing. When fully ramped, the segment EBITDA should be about $100 million and we'll use the cash flow to continue funding developments for the company. The Sai Computing vertical, again, 34,000 NVIDIA H100 GPUs on order. We have over 30 megawatts of capacity ready to deploy those. So you'll see a fast ramp there between now and the end of our fiscal year. One cluster, we talk in clusters is 1024 GPUs uses about 1.5 megawatts of energy. We have a target an estimated 2-year payback for each cluster and a 6-year useful life and 1 cluster produces about $18 million of anticipated revenue on our reserve contract. On the market demand, it would be much higher than that, but we've chosen to go out and get reserve capacity. HPC hosting. We're building out 300 megawatts of projects. We'll continue to energize that. We have our first generation at Jamestown 9 megawatts, and we've got projects in Utah and North Dakota that we're beginning to push dirt and get excited about where we're going with that. The question always comes out how we're going to finance that. We believe that we're targeting 70% to 80% of construction financing with an equity partner to come in for the remaining balance and $6 million per megawatt for the CapEx there. So really just wanted to break this down into very simple building blocks so that you can work on your models and understand very specific easy metrics here. For Bitcoin 1megawatt, we expect $625,000 in revenue and $208,000 on segment EBITDA. For HPC, revenue of $2 million per megawatt and a segment EBITDA of $900,000 to $1 million. And then AI Cloud, again, it's per cluster, the 1024 GPUs. A 1.5 megawatts, $18 million of annual revenue. And then we don't want to talk about EBITDA because of the depreciation is such a large amount, but on a segment operating margin, 40% when you get to scale. That really kind of runs through the basic financials for you. We'll have a Q&A question where you can ask questions later.

Thank you, David. As we're putting this day together, it was important to us that you obviously hear from our management team, but we also thought it would be great if you could hear from some of our partners and customers too. So throughout our time together today, we're going to share a number of videos, sharing some information and content from those people. We're going to start first with Jarred Appleby. Jarred has been digging into our company over the last 2 months. He runs a digital infrastructure advisory practice and has many insights to share. Although Jarred couldn't be with us in person today, he did spend some time with me last week walking through his market perspective, and now we'll play that for you. [Presentation]

Jarred, thank you so much for joining us today. We're really excited to get your feedback on some important aspects of what's going on in this industry. First, would you mind describing your background and experience for us.

Thanks, Erin, for inviting me. Sorry, I couldn't be there live. I'm a senior adviser. Now I run an advisory business for the digital infrastructure space, started out in the network world, 30 years ago, got into data centers about 15 years ago. Most recently, I was Chief Operating Officer for Digital Realty. I left there about 5 years ago after 15 years in the industry. And I joined up now, I'm a senior adviser to the Blackstone Group, and I work closely with emerging companies like Applied Digital. So excited to be here today.

Awesome. If you wouldn't mind sharing, how did you first learn about Applied?

Well, I've been working with the NVIDIA ecosystem for the last 4 or 5 years with some of my clients, and I was really excited to see the emergence of digital infrastructure players who had GPUs and we're doing new products and solutions like a bare metal offering and such in the market. I actually support Blackstone on their diligence on CoreWeave. And I looked around and said, he's got a pretty interesting model. So I reached out to Wes, the CEO and really had a discussion with him and what your strategy was and what you might be doing?

Perfect. And as a very obvious expert in the space, you alluded to a little bit, maybe could you expand a little bit more about what was it about Applied specifically that made you want to connect.

Well, I think there's a couple of key trends in our ecosystem right now. One is the impact of AI machine learning on data center campus designs and a new product. So our whole new structural change in how buildings are being built to support the AI machine learning workload. So I thought that was super interesting, critically starting with the North Dakota campus and what's going on up there. Second, this emerging services, particularly bare metal and GPUs and getting control of that pipeline, it's a very scarce offering capability. And I think Applied has some great capabilities and services they can offer. And then finally, the level of investment in the partnerships are very intriguing. I've been working with the hyperscalers for 20 years and they have a heavy reliance on these types of services and it's super interesting to see the partnerships, including the NVIDIA elite partnership that you have in place.

Well, maybe let's take a step back a little bit and first talk about the overall data center marketplace. What is high-performance computing from an equipment and technical requirement standpoint?

Well, I think we've seen an evolution of workloads from enterprise solutions that really had a high dependency on networking and power densities were in the 3 to 4 kilowatts of cabinet range. We saw this evolution of cloud availability zones and -- which are large deployments typically in the 18 to 36-megawatt deployments, power densities, tripled or quadrupled to 8 to 10 kilowatts a cap. But in today's world, it's about cooling and delivering high-power density solutions that could easily be in the 40 to 50 kilowatts a cap or in some cases, even 120 or more, we see. So the HPC AI world is the next generation. This is one of the biggest structural shifts that I've seen in my 30 years in the industry. And I think the folks who are in the leading edge, like Applied Digital of creating a product and buildings and cooling solutions that can support this generation are going to be winners in the marketplace.

And who is it that needs GPUs? And what are most of the high-end GPUs being used for today?

Well, the ones we read about them in the press all the time from the hyperscalers who've quickly pivoted in the last year. You can have seen some announcements where they had to stop their data center development programs for some time to retool the design, architecture and supply chains to support it. So clearly, the hyperscalers all need these types of solutions and we'll partner up. But enterprises need it as well. It's -- this is a disruptor, and it can really support everything from financial services, health care, pharma, pretty much every industry will have some type of AI machine learning dependencies. I think the first generation is we're seeing a lot of the training workloads, which can be further away from city centers, but the real value, I think, is dual purpose, where you're closer in. You can support businesses nearby an enterprise and channel partners, managed service and channel partners.

Got it. And what are some of the ways the data center requirements differ from these hyperscalers we are speaking of to enterprise data centers?

Well, I think one is scale. I mean the enormous scale of hyperscalers you can actually see campuses from a hyperscaler. I was at a conference recently, and they go, "I don't get out of bed for under 100 megawatts anymore". That's not what an enterprise would look -- they want a room or a cabinet type of solution, but now you're seeing whole buildings and you're even seeing campuses that are 500 megawatts or 1 gigawatt even in today's market. And the real estate is important, fiber is important from a site selection standpoint but power costs and total cost of ownership and the ability to cool is really a differentiator in today's market.

And what would you say is the pace of change in the equipment? And like where are all these technical requirements going?

I think we're early days. We're in early innings of the call hype curve, and you see a lot going on, but I don't think everyone's -- at least the clients and -- that we're working with around the world don't have their final solution yet, that's why this transformation is so important but they're experimenting, they're executing and delivering solutions to see what's going to happen, and it will take years to optimize the supply chain, the buildings that are built, the products and solutions and how to maximize it. So it's an exciting opportunity in the industry and yes, I didn't see the scale a year ago. I just was again another conference in a report, I think there's like 7 gigawatts of data center development going on right now to be delivered by 2026. So that type of scale and -- we've not seen ever really, it's just a very structural change.

So Applied is building and designing one of the world's largest GPU work clusters with keeping latency at the forefront. Can you provide some feedback on this new design?

Yes. What's intriguing to me is the importance of latency and the network -- neural network in the middle of all this, we saw campuses start out in the availability zones. They were single story spread out pretty significantly in very large buildings to go to market quickly and then people started stacking that design. In today's world, that -- it's good potentially to be vertical. And so seeing applied solution that it's vertically stacked to reduce latency, to improve information transfer. And the other piece of it is it has to be always on in terms of you're running these GPU chips continuously and try to be efficient until you've maintained. And that's a really different -- it's an all-out type of deployment, which is very heavily dependent on the network in the system, the performance of the GPU chips and the cooling technology that all have to play together. I'm finding this -- the purpose-built applied design super interesting in this first campus. And I think that's one of the new things we're going to see early movers kind of use the existing data center and colocation space, but you're going to need purpose-built AI machine learning data centers kind of going forward. We don't know all the answers, but it's good to be in front of it and testing these in the market.

And as we've been talking about, the industry is moving and changing really fast. So that's obviously creating many hurdles to meeting this demand that is ever flowing, the first that everyone's generally aware of is obviously GPU supplies. A second bottleneck is likely much more a longer-term hurdle and that's power availability. Can you provide some commentary on where Applied fits into this?

I think, number one, we don't know the importance of latency and distance away from where the Internet is. So the Internet lives in key peering hubs around the world and so when the cloud kind of started out, there were distance limitations how far are you away? So for certain AI workloads, we can really test that. That's where North Dakota comes into play because it has cheap power, because it's a cooler environment and you can build very, very large scale, we're going to see people go there. And I worked on a project 1.5 years ago, in the middle of Pennsylvania, and it was because it was near nuclear power. And when you get power costs that are -- that could be $0.04 type rate, it gets people's attention and at the scale. And so we do, as a country and globally have limitations on the power side and AI machine learning require much more. So I think that's really going to be a shift in thinking how far away from centers and you can definitely use it for their training workloads. It's just a matter of dual purpose, how far they can be away.

And how do you see these major cloud companies and AI players, hunting power and managing site selection? And how do you think Applied is situated to compete for these contracts?

I think, the hyperscalers we see are great at this in terms of -- but the scale that's needed and the pipeline of new capacity needed, I think, caught all of us a little off guard in terms of solving for it at least in the near term. So I think they've stated, again, some recent industry reports, they were hopeful to do 2 out of every 3 campuses would be self-build, but frankly, I think what we're seeing is they're only able to do 1 out of 3. So that means partners at least at this point, who can provide power and build the right product and solutions for them are available 2/3 of the market roughly can be new providers or emerging players who can deliver for them.

And what would you say are some of the biggest risk factors facing HPC infrastructure providers? And with that in mind, how would you say that Applied is set up to tackle some of these issues or risks?

I think the cooling solutions are probably the technical things that we're seeing. It's undetermined what will come out on top, I think using a combination of air cooled and liquid cooling is the way to go. I think AI, if you're using water cooled, that's a real issue in some parts of the country, where you're dealing. So water utilization efficiency is really big. And so by the Applied team is really thinking through those, how to minimize water, how to take advantage of environments, where they can provide a combination of air cooled and liquid cooling. And liquid cooling over time, I think, closed-loop systems are coming. So I think the Applied team is looking at all those ideas and figure out the best solution with their customers.

So maybe one last question. I would love to know if you have any comments on where we are in this hype cycle and how trends demands are expected to evolve in the next, call it, 2 to 3 years?

I mean I think we have line of sight. If you talk to hyperscale clients and enterprise clients, we have a pretty good line of sight at least into '26, '27 this cycle. It's undetermined after that. That's -- the evolution of these type of offerings is going to be super interesting. And we don't know how long the GPU limitations are going to last as well. So I think in this window, though, you said 2 to 3 years, it's about taking advantage and fully utilizing the GPUs that are available, and that requires different models and different product solutions kind of test the market and I think Applied is well positioned there, among a few others, especially with the NVIDIA ecosystem. I think they're particularly strong in this phase, and the next phase that we want to focus on with the executive team and the leadership team here is building that next generation of partners. It's going to be about partnering in a flexible model to support their growth and in turn Applied's growth.

It's incredibly insightful. Thank you for taking your time today. We really appreciate it.

Thank you, Erin.

When trying to do something new, he must move fast at Applied. It seems we don't really have a speed limit. We have a speed to market that we adhere to and if can't already tell its moving at work speed. So how are we doing that? We're so glad you asked. Here to catch us up on the Applied development philosophy is Brad Barton. Brad has an impressive construction and design background, and we're glad to have him as our EVP of Real Estate Development. It's all yours, Brad?

Good morning. Excited to be here and I'm going to take a little bit different of a speed. I'm a design and construction person in the real estate department. My favorite part of real estate is the physical asset. So we're going to talk a little bit about that, but we're going to stick a little high in theory, if we can. That's the clicker. So probably the best titles I do hold husband and father of 3. I think they're watching today, so I'm excited to hear what they think. But I have had some extensive background in design and construction of critical facilities. I may not look very old, but my entire career has been built in building data centers from multibillion dollar government data centers that I can't talk about to privately held data centers in the Texas region. So I want to focus on one of the most iconic buildings here in the great city of New York, The Empire State Building. Most of you know it, most of you have been to it. Maybe some of you work in it. The crazy thing about this building is it was designed in weeks and it was built in 12 months. I don't know about you, but that's absolutely insane. A building like this today would take 1.5 years to 2 years to design and then 5 years to build. So what's happened? Why are we slower today? Why does it take us so much time to build? Well, the reason why this project was a success, there's a lot of them, and I'm going to focus on a few of them. If you don't know the history of them. It was right before the great depression. There was an abundance of labor availability and skilled work in at that. There's a repeatable design. It's copy pasted up every single floor. They did use prefabrication, there were less building codes. Safety, unfortunately, wasn't even a factor. There were quite a bit of fatalities on the job and material was readily available. Today, I want to talk about the lessons that I'm personally learning from this build and how we can kind of apply it at Applied Digital. So why this -- where we're at today is we're a segmented market. We've pulled a part design and we've pulled a part construction. And what that's created is a massive gap. We're all specialized. There used to be 1 design company. I think I've hired like 15 for my latest build. And what's in between that gap are labor shortages. New tech and design that hasn't even been invented yet that we're designing for thousands and thousands of building codes. Thankfully, Ocean Safety is involved, but it doesn't make things go faster and our good friends at Toyota taught us the beauty of just-in-time inventory. That's a wonderful thing that it was awesome, but it also makes construction so hard. If you want a generator, you're waiting almost 2 years for a generator right now. That's a long time. So what this gap has caused us to do is design kind of looks like this chart over here, it's pretty squiggly and pretty gnarly and ugly in the beginning. And honestly, this is kind of what it looks like on our first purpose-built data center. We're talking to all the OEMs. We're talking to manufacturers. We're talking to vendors, Supermicro, NVIDIA, lot of tenants and clients out there. What do you want? What are you seeing? What does the market look like? You just -- you just heard from Jarred, but this is a brand-new almost industrial revolution. We don't know what we don't know yet. So it did kind of look like this. But then what the industry does is they want you to deliver a lot of these standard packages, schematic design, development design and hopefully, it coincides with your construction schedule. Most of the time, it doesn't. There's a standard set of specs and everything that's issued. It doesn't really line up and help you go quicker. If any of you are architecture engineers, I do not mean to offend. So what are we doing in the digital to bridge that gap? Can we change the industry? No, we can't. But there are small little things that we can do that can help us pivot to go a little bit faster without sacrificing quality, safety, schedule and cost. I'm going to talk about a few of those. So we designed to the actual delivery method. Instead of calling it an SD package or a DD package, sometimes those make out of the drawings, but we call them package. This right here is a sample of how we're scheduling an underground foundation that we want to pour. Well, if you want to pour your foundation, you don't need just a schematic design. You need your schematic design that includes the following definitions and programs. Does it cause you to make some guesses? It does. Does it cause you to pull some things in forward? It absolutely does. You want to pour a slide, you got to know your underground. You've got to know what chillers are going there. You got to know all the things in front. So how do we do that? This is an actual slide of some chillers out there. The one in red is a very reputable, large chiller manufacturer, probably cooling this building. And the one in green at 27 weeks is probably cooling the building next door. It's just as reputable but we happen to find a size and a quality manufacturer that can meet our lead time. So we designed to that piece of equipment. At Applied Digital we're too young, we're too nimble to be married to 1 specific vendor. Do we need the quality and expect to be met? Absolutely. But these 3 can meet it. It just changes our spec a little bit. So can we design all around that? Absolutely. So how do we learn about this? As much as we want to claim that we're the all-seeing eye and we're the leading market. We are in some regards, and we're not in others. When we go work in North Dakota, we go work in other municipalities. We don't know what we don't know. So the early involvement of contractors and trade partners try to get them involved even earlier than this is key. They let us know what the labor market is like. They let us know, don't buy those lugs because it won't come in 52 weeks. That gear is available, but the connections aren't. That's great insight for us to get at the very beginning of design, not a hard bid scenario where we go out and say, "Tell me what it costs and make it cheaper". We bring them in the front. So another way is going back to what we talked about with the Empire State Building. This is the traditional design process for steel. SEOR is the structural engineer of record. They spend all this time delivering these standard packages and working under an architect typically. Then we hire a general contractor and then they hire a fabricator. Fabricator is a third-party designer that takes the drawings and then draw his own fabrication details of how it can go to a mill to be processed and built for the steel building we're standing in today. That's a lot of people in going back and forth, and all those arrows represent changes, design iterations. Someone wants a bathroom on floor 5, someone wants their office to be a certain way, and it's got to have certain weights. So things change, and there's a lot of room for errors. This leads to a lot of time. Now this next graph I'm going to show you, it's going to promise 10 weeks of saving. That's pretty aggressive. I haven't seen it yet, but we have seen some weeks of savings where one of our contractors, we hired the structural engineer under the architect to draw, he then has a separate contract underneath the fabricator to design. So he's taking his singular model to bring it all the way through to design, construction, fabrication in almost direction. It seems to have made a really easy step. Can other people do this? Absolutely. Will they? No. People like the way that we've traditionally done it, because it's comfortable, yes, it cost more and yes, it's longer, but sometimes it's funner to complain about things that actually fix it. We're willing to go and challenge this. Will we get 10 weeks? Maybe. Can we get more, could we get less? Yes. But any savings and efficiency in construction time solved in design is the best way to go. So another thing that the Empire State Building used was prefabrication. They didn't use these high technological things that we're using today. They use Velum and blue paper and people drew them by hand. We have BIN capabilities, which is building information technology. We're able to draw things and fabricate it in a shop offsite and bring it to a rural North Dakota site. Our aim for this next build is to remove 30% to 50% of labor off-site. So that one, it can be safer, it can be cleaner, it can be better quality and then put into place later. If we can accomplish that our goals are very attainable. We're already setting up some partnerships with some fabrication shops to help us do things like our underground peer caps. Our peer caps are quite big on building this size. Our multi-trade racks that we're looking at building only makes sense. Have your trades work in a heated warehouse during the winter, ship them on a truck, put them into place. It's kind of plug and play. There's a little more work put to it. That's about it. So going to pivot here and not go into the data center construction. That's kind of all been theory. Data centers have been around for a very long time, and they're very complex. If you were to look at a set of plans, they look like a big white open space with some rooms that surround and it doesn't look very complicated. But it is. For those of you who've been in the data center space, you know that cooling and electrical are paramount. Really to put its simplicity, a data center is lots of power in and a lot of heat out. How do you guarantee that if I'm going to host your machines. Well, there's something that have been called a service lease agreement, an SLA, and they've been improving over the past 20, 30 years of what it guarantees the people. And one of the biggest things we're talking about it at dinner last night, one of the biggest things is those 4 or 5 9s of uptime. I cannot go down. People cannot miss their Instagram feed. We have to stay up. Well, at Applied Digital, we're challenging that. The AI world is challenging that. Do you really need to be up that long? The answer still remains to be seen, but we're hedging our bet unknown. This new AI work cluster doesn't require full uptime, just like it doesn't require the massive amounts of latency, which we are actually able to capture up in North Dakota. We'll talk about that later. So it kind of goes into the history of data centers and I'm going to go really high level through here. Some of you are my senior, so please correct me if I'm wrong, but in the early '90s, data centers have been around since 70s and 80s. They've been supercomputers. But really, we're going to start in the early '90s. This is when the IP closets were in the 4 walls of a building and a great company called Digital Realty and GI Partners figured out, and we could probably grab those IT closets and put them in a co-located space and charge people money for it. They were small per KW leases, really easy, really quick start, and they did great. They went public in the mid-2000s at $12 a share. And right now, they're in -- someone may watch the market better than me, but they're a big company. They're one of the biggest REITs in the country. So had we bought shares in 2002, 2003 when they went public, we'd be doing just fine. And I often wonder why didn't I do that? Well, because I was like 10. I was scoring touchdowns or maybe lack thereof. Just I was too young. Right now, we have the opportunity, and I'm obviously very bullish on our company. We have the opportunity to invest in a new industrial revolution. IP closets are in these old legacy data centers. And Applied Digital is building a new asset, a new jump. I think we went public at a similar price. I think this is a great opportunity to jump in. So pivoting next to what we're doing. We've talked about this Ellendale data center. This is our next build. We are breaking ground very soon. Just up in North Dakota on Monday with our general contractor, we're mobilizing within the next few weeks. It is the winter and we're making provisions to build during the winter. But this is a purpose-built AI brain in the building, just like Jarred was talking about. We're going to have 100 megawatts of building load all delivered within 1 singular building. We are targeting a rack density at a minimum of 45 all the way up to 150. Your cooling mediums do change at those, so we're designing for 1 to 2 cooling mediums. It is purpose built for AI workloads. And we're targeting a PUE that's lower than 1.2. But right now, we're willing to say it's going to be at least 1.2. Harvesting that cold air up in North Dakota is a wonderful thing for us. We want to emphasize though that we're not going to be a one-trick pony. My real estate design and construction team, procurement, site selection have been very busy, and we have a pipeline that is very busy, enough to keep us busy through that season, like Jarred said, all the way out probably into 2030. There's so much power we've been able to locate. We've been very fortunate to have, like Wes mentioned, some -- the right individuals on the team to find some power for us. We want to develop that building and future iterations of it on campuses for now and years to come. Would love for you guys to join us along the ride and thank you for your time today.

Thanks so much, Brad. So you've already heard some high-level overviews about the different business units that make up Applied. Each unit will a valuable part of the expansive ecosystem here at Applied, each has its own story and opportunities. So that's why we want to let each of our unit leaders take a few minutes to walk through their units and give some expertise on what they've been seeing. We're going to begin first with the area that got it all started, our hosting unit. But before we do so, we want to give you a little customer perspective. [Presentation]

There are so many incredible things happening in the space. But I'm going to let you here, it's straight from the source. Nick Phillips, our EVP of Hosting Operations and Public Affairs.

I'm Nick Phillips. I'm the EVP of Hosting Operations and Public Affairs at Applied Digital. I get to work on the strategic long-term and midterm operations. I've got a team of 110 people who support me on a day-to-day basis at all of our facilities. The public affairs part of my job is I work with local, state, county and federal government officials, whether it's legislators, regulators or other folks to help carry the mission about what Applied Digital is doing and educating them on topics as well as working through any issues that come up with in regards to permitting or other things that might hold off our business. If you take what we built, right, we've constructed almost 500 megawatts of facilities in the last 2 years, 2 years and change. We have turned on 300 megawatts of those facilities in the last 2 years and change. We've grown from -- again, I was the fifth person to join the company. We're now about 170 almost in that period of time. We have all sorts of great systems. We have all sorts of great operations. We have all sorts of ways that we watch things. We have customers. Our customers are really interested in what we're doing but they're not that interested in micro managing the day-to-day basis of their items because we do it for them. I had a customer at some point, call saying, "Hey, can you check all of our settings on all of our miners for us. So don't need to because I don't want to alert that pops up every 15 minutes, if anything is not exactly the way that it's supposed to be. And it just tells me I'm 100% confident knowing that the things are set up the way that they're supposed to be and we're managing them properly because we have systems that we've built and put into place to do so". So North Dakota, where we have 2 of our facilities representing about 300 megawatts as well as West Texas, where we have a 200-megawatt facility are really great locations for wind-generated power. The challenge that there is in these areas, there's not a lot of load in those locations. So there's not a lot of people who are looking to take that power and utilize it. The second challenge that exists with those remote areas that were located in is that there's no transmission lines to pull that power away from all that generation. By us building our facilities right where the power is being generated by all these wind turbines and everything that's going on, it really helps out the grid in a lot of waste. When there's too much wind, which means there's too much wind-generated power going on. We're able to absorb a lot of that and when there isn't too much, we're also able to pull from these transmission lines and be able to power our facilities at the same time. So it does a lot of things for the low communities as well as it does a lot of things for us. From a financial standpoint, for us, if the wind mills were to be curtailed, meaning they were unable to operate during a time where there's too much wind, then the wind turbines have to be shut down. And effectively, they're just not generating any revenue for those companies or for the communities in terms of tax or other items that they get paid on. Because we have our load in those areas, we're able to take advantage of very low pricing at times when there's a lot of wind being generated, and we're able to help the communities with revenue based on those turbines continuing to be able to spin, they don't have the sophistication of our systems that we've put into place. We have very clean facilities. We spend a lot of time and energy on safety. We spent a lot of time and energy on optimizing energy usage. We have all sorts of systems for monitoring and managing all of the miners to make sure that they're mining to the right place all the time, which is security risk that we manage very, very tightly, that they're up and operational as much as possible. We have very unique and creative ways of managing hash rate in the way that grid operators and utilities that we work with were able to very well function inside of the systems and what they need, which helps balance out of the grid, which gets us this power pricing that we need to be able to be extra profitable. In terms of managing the mining business, we do it with a really small headcount, right? So we try to have a bunch of folks who are in these small rural towns that we operate inside of. We bring folks in who have experiences that are either mechanical or farming or electrical or other types of experiences. And we set up systems, processes, trainings and other resources for them to be able to manage these highly technical, highly sophisticated facilities in a very effective manner. We've managed to do that inside of very small towns, ranging from 500 to 17,000 people, which is where we operate. And we're able to find local workforce that's right there and to be able to work with them and be able to run these facilities very efficiently without having highly trained, multiyear experience type workers that come in and do things with us. While we're doing that, we're managing our SLAs, we're making sure that we're meeting our customers' needs. We're making sure that from a security standpoint, our customer equipment is doing what it's supposed to be doing all of the time. There's not a case at any time where I've ever worried about that. About 2 years ago, when we started doing this, it was just an idea, right? We went from, hey, we're going to go build a 100-megawatt facility in rural North Dakota to now today, we have got one of the world's best, most well-run facilities. We've got almost 500 megawatts of facilities constructed. 300 megawatts of those facilities are online today, the rest coming online soon. We have systems in place that manage and monitor everything. We take care of our customers' equipment very tightly. We're able to see what's going on all the time and respond very quickly to any issues that are coming up. And when you walk into our facilities, they're clean, they're well organized, they're properly cabled. We've taken what's been kind of the wild west of blockchain and really try to professionalize it and make it run like a professional data center does. That means cable management, that means monitoring systems, that means having line of sight to every little thing that's going on and being able to manage it very tightly.

It's time for a quick break. So we've got more snacks and more coffee right outside the doors. But please be back in about 15 minutes. Thank you. [Break]

Welcome back, everyone. So you've heard a lot about the opportunity and work this building inside our HPC business unit. Here to give an update on what's happening there is Erik Grundstrom. He's our Vice President of HPC infrastructure. At the end of this update, you'll also hear from a customer who's already seen a ton of benefit from their work with Applied. Now I'll let Erik speak.

My name is Erik Grundstrom, VP of HPC at Applied Digital. I initially met the applied digital team working at an OEM at a compute manufacturer. And getting to know them over time was a great experience. Going meeting at shows and discussing architecture, deploying initial POC, which is now leased in Jamestown, was all fun, we -- Applied garnered a great amount of support from the OEM, that I always work for both from myself, from an engineering perspective and then from executive leadership as well. So it didn't take long. We started to build the first supercomputer together as soon as the AI explosion began and engaged with the end-customer heavily. And from then, I made the jump. I had to. It was a once-in-a-lifetime experience. Applied is doing things that nobody else has ever done. It's very exciting, and I'm very happy to be here. So over the last 2 months, I've continued to harness and work with an amazing team and leading this HPC and supercomputing effort continue to build and to bring on new talent and everybody kind of mission-focused and so excited for what the future brings at Applied. So the first thing I want to talk about is, something that we're all very keen and aware to, which is market growth and demand. With demand currently exceeding supply, the expanding applicability of AI, as well as enterprise converging automation, analytics and code, Applied is in a really unique position to harness and expand on our existing resources to meet the computing needs of today. Whether that means VC-backed AI-focused companies or the enterprise. So we are expanding on both fronts. The fact that Applied is not a colo provider. We are not an AI as a service provider. We're not a platform as a service provider. We combine construction facilities, architecture, design and maintenance involved in all of the physical infrastructure. But we also deploy world-class bleeding edge computing infrastructure. So for one company to manage both worlds together, results in something where a customer cannot only realize a certain degree of value that's not -- that doesn't exist elsewhere in the market. But it allows a great degree of flexibility of ability to evolve over time, to change, to accommodate different workloads, different hardware as it comes in, whether that means we need to scale from 45 kilowatts to 100 kilowatts per rec, which we're planning on doing, or if we need to go beyond that. We need -- we'll have facilities to expand into any sort of cooling schema that is expected moving forward. Hardware is consuming more and more power as time goes on, that's apparent. That is one thing that is -- we're on a linear curve. And all of the manufacturers will tell you the same thing. It is a major factor in the performance gains of the future. And Applied is designing new facilities and acquiring new power leases from state and -- from state and local governments, that's going to allow us to do more than anybody else can do. There's no disconnect, right? Like we all work together as this organism -- they're in our meetings, we're in their meetings. We're continually discussing optimization and the best way to deliver on this idea that is really -- that is a unique position. We've had great success with customers, signing on to what we do that see the value and what we have in terms of operating both the physical environment that the servers operate in, as well as hands on, operate the compute infrastructure itself. I think the highest complement that you could be paid is a referral from one customer to another. And we thankfully have had increasing demand as a result. So a lot of these products that are end customer, a lot of the things that they are doing are -- anybody who is very familiar with what's happening in AI can tell you, are very exciting. And expand on human creativity, expand on our ability to articulate certain messages, expand our capabilities as people, especially in the digital world that most of us spend a lot of our time in. So yes, the demand, the onboarding of new awesome customers and the continued expansion, are all very exciting and are all remarkable achievements for a company that is this age. The second thing I want to talk about is technology differentiation. Applied's approach as a service provider to the industry is unique. We utilize power, real estate, construction and compute capabilities to provide world-class architecture, both in terms of facilities and HPC infrastructure. Our flexible supercomputer-as-a-Service and Baremetal-as-a-service offering, our cater to accommodate the world's most demanding workloads and are designed to offer precision performance, it can be easily configured and redeployed through a combination of AI-powered, automated and hands-on processes. Another area to highlight, Applied is our talent and expertise. Applied is building some of the world's most powerful supercomputers. I have spent decades deploying compute all around the world. I met CTO, Mike as well as HPC systems engineer, Rahim, during initial OEM engagements and the thought of what they're doing really touch my inner peak. The thought of deploying supercomputing at scale, the thought of the workloads that science that applied mathematics that artificial intelligence, the ways that they will change our lives in the short term has accelerated deployment and development of so many things, so many things that we all will come to realize, that will come to shape our lives and our children's lives. But like I think something even more low level that a lot of us who are into this type of stuff perform -- at a computing performance are into. It's just the raw scale, the power and the ability to do the math at like nothing, the big math like nothing that has ever been done before. Building out computing architecture that can tackle the biggest, toughest most complex problems that we are able to conceive of as a species is really exciting. So I think that not only for myself, but for my colleagues and for my teammates, that is a big reason why we came on board at Applied. It's one thing to work with a supercomputer or if you're -- if your company has a supercomputer, that's really fun. That allows a lot of creativity and thought and coding and tweaking workloads and tweaking compute performance to change things. But what about when you have 7 of them or 9 or 21 of them. That's the direction that we're heading in. So that is something that I think that is kind of like a -- it's been a bucket list thing for myself and I think for many of us, really to like be hands-on, to build, to maintain, to operate our own infrastructure like this. And it's not an opportunity many people have. So it really has been a pretty wonderful experience engaging with all of the hardware and the capabilities that we have at Applied. We're very lucky to have that. So we have exceptional talent working within our group that includes HPC engineers, the systems engineers from Apple, Meta, Gray, Oracle and others. And our team shares that same passion about supercomputing and how HPC is helping change the world in so many ways. We continue to actively recruit the best and we have excellent internal support to do so. So the next thing I'd like to talk about is our innovation road map. So technology standards are constantly changing. exoscale, computing and exabytes of data will soon be a thing of the past. Your iPhone, a decade from now will have an exabyte of storage. Applied maintains deep relationships with the OEMs and the vendors that are changing the landscape today. And I think possibly, more importantly, Applied is developing and growing deep relationships with the OEMs and the vendors that are going to bear that standard in the industry tomorrow. Our facilities in computing infrastructure are designed with upgradability, expansion and accommodation of next-gen hardware in mind. From power and cooling delivery to automation, oversight and compliance, Applied is focused on evolving at the pace of Silicon Valley, at the scale of Texas. So the big message would be this. There are a lot of companies out there that you can lease or purchase cloud computing from. There are some companies that you can lease supercomputer from. There are some companies that you can lease data center space from and even a smaller subset of companies that you can lease data center space from where you can accommodate anything dense enough to be considered high-performance computing or supercomputing. What Applied does, is all of those things together. There is nobody else in the world doing this today. That gives us several distinct advantages, from the value prop to the fact that we can go ahead and manipulate change on the fly, computing, the way that it works for you, manage this as a service on the back end. We are completely 100% unique in that position and makes us definitely future market leaders, but the only company that can top to bottom, accommodate HPC, AI, supercomputing-as-a-service, for AI-based customers and for enterprise alike. [Presentation]

Our next presenter has his hands on every aspect of the technology here at Applied. Here to talk about data center building specifications, security implications and all of the technical components is our CTO, Mike Maniscalco.

All right. Thanks, Erin, and thanks to all of you for being here this morning. I'm sitting in the back of the room all morning and listening to everybody speak. And I'm thinking to myself, what can I say that hasn't already been said. But in all honesty, I think it really speaks to the strength of our team and the quality of the team we've put together. So I'm thrilled to talk to you today about some of the work we're doing. One thing I think we've touched on a fair bit here and there is go through the disclaimer and myself, is how quickly things have moved here. I started with the company about -- started working with the team over 2 years ago. And I can tell you, I'm more excited than ever about the work and the things that we're doing today. It's just a ton going on, a lot of really great people and a problem that I believe, is important to solve. Because we haven't really talked about the why of this equation a lot today. For those of you in the AI world or following the AI world, we're living in a world where -- it's funny because this is a line of our manifesto. It feels like innovation is racing against time, and that's an absolute truth. We're living in the middle of this AI race to dominance, is the word I'd like to explain, where you've got the brightest minds in the world trying to deliver some amazing technologies. And I think the eyes have been open to what these technologies are capable of. This technology is not going to slow down. It's going to continue to move faster and faster. And the smartest minds in the world are working on these problems because they believe they have world-changing implications. And to work alongside of these people every single day to deliver a digital foundation for them to do their work on to me is extremely exciting. But, if you're a world-leading AI researcher, what you want to do is you want to train your models. However, to train a model in today's world is extremely complex. It's not just the PhDs at Stanford, working on the math and the science and writing the algorithms and the user experiences. That's the front side of it. On the back side of the house, there's an enormous requirement for digital infrastructure. And these are the things that an AI researcher doesn't want to think about. They want to be turned over the keys to a supercomputer, and they want to go fast. And that's what we're providing at Applied Digital. To do this, the digital infrastructure requires a combination of power, compute, facilities, networking, cooling, a lot of different specialties. And I think one thing that's really changed in this world is this AI explosion driven what -- I think we all would agree could be called the ChatGPT effect. Because if you rewind to what was it a year ago, when we were starting to build our James Town facility and really getting that up and running, there is a lot of excitement, but nothing like what we're seeing today. ChatGPT effects essentially approved -- has started to prove the world what the possibilities were, but also that the capabilities of GPUs. Until that point, large language models and going larger and larger with these parameters and larger and larger, which are training workloads wasn't really apparent what that was going to produce. But ChatGPT showed that adding more parameters, more compute to the equation, more GPUs to the equation, more power to the equation that you're going to produce better outcomes, and that changed everything. But it's not easy. And that's I think where really Applied Digital is coming in. We're looking at these complexities and we're welcoming them with open arms. I think Erik was a perfect tee up to this conversation, Erik's on my team. Erik and the team he's built, they wouldn't be here who wasn't for that complexity. That's what excites them. That's what allows us to really deliver these services. I don't think many people in the world are capable of doing. We've built up a fantastic team. We're tackling power. We're tackling scale, we are tackling construction, as Brad alluded to, networking challenges. We're talking about ultra-low latency networking. We're talking about building some of the largest supercomputers in the world. That's factual. The top 500 list is published in the performance and capabilities. These large H-100 clusters we're developing to do that. And to kind of remind everybody how we do that. There are essentially 3 different services we're offering. Accelerate GPU compute as a Service is turning over a handful of GPUs to somebody who just needs to run it for a few hours. Where that's first for inference or a researcher -- team that's not ready to scale up to hundreds of millions of dollars of compute. There's plenty of those guys out there in the world. But there are also some of these leading AI researchers who are ready to scale up to hundreds of millions of dollars of compute. And they're looking for somebody who can deliver supercomputer-as-a-service. And that's one of the unique strengths of the services and the expertise that we've built to deliver to these customers. And then lastly, and I don't want to understate this because I think it's important, and I'll talk about the trend a little later. We're delivering these colo next-generation data centers, specifically designed for some of the largest AI supercomputers looking forward, and they're just getting bigger. We do this by leveraging a lot of NVIDIA's technology. We are one of, I think, the few people in the world who are really deploying these NVIDIA HGX reference design clusters to our customers. And turning that over to baremetal and then supporting them through the implementation of that. To get something like this in place isn't a one-man job. It's -- and to go back to the team comment earlier, this is where I think we shine. To get a cluster of this size between 256 and 5,000, H-100 cards in place, requires a number of specialties. It requires power as a specialty. It requires design and construction as a specialty. It requires networking, ultra-low latency as a specialty, compute as a specialty, storage as a specialty, and the list goes on and on and on of the team of people we have to put together to make this happen, and it really is impressive. I'd like to think about the work we're doing is turning pipes and air into threads of opportunity for our customers. A lot of people ask about what we're doing with our data center specifically in North Dakota. And why is that different? Well, we'll start with the network. When you think about AI training and workloads, there are basically 2 classifications. You've got your training clusters, which are meant to compute on large amounts of data and then spit on algorithm that can be delivered to customers to use for things like ChatGPT or character at. And then on the other side of the equation, once that model is trained, the way the customers interface that is through inference. Where you're now interacting in your consumer app with that compute. Now training and inference have 2 very, very different sets of requirements. And Jarred talked a lot about this in his interview, training, all that compute is happening locally. So you don't need ultra-low latency to the data center. You don't need massive pipes of the data center. You need to upload a workload. You want that workload to work effectively for some extended period of time, days, weeks, months in some cases. And then you want that workloads results to be step back out to you. During that time period, all the compute, all the communication is happening locally. So latencies from the network -- the Internet side is somewhat irrelevant. But what is highly relevant is the latency within the data centers. And that's where the complexity comes in. And that's where the NVIDIA reference architecture comes in. We do all this on InfiniBand networking, which is ultra-low latency networking, specifically for AI training applications. And these are not insignificant blips. Just in physical infrastructure alone. We have that a 5,000 H-100 cluster requires 250 kilometers of fiber. For a total of 15,000 fiber cable, InfiniBand fiber cables. Just plugging these things in, takes teams of people weeks. So it starts to highlight why an AI researcher doesn't want to go through cabling, doesn't want to think about power. They just want to run their training algorithms. So I think another point quickly to highlight here. And I guess moving on to the next slide, is power as simple as it seems is truly the primary ingredient of all this innovation. We're, as I think Brad did a great job of talking about this, we are essentially building an AI brain in the building. We are just like your brain, trying to connect an enormous amount of compute into a very, very small space. And the reason you do this with these clusters is you have physical distance limitations to meet the latency demand inside that cluster for the nodes to communicate with each other. And that constraint is in our world, 30 meters. All those nodes have to do within 30 meters of each other, which speaks to our proprietary design for the new data center for building. You go beyond that, and you also want to squeeze all that compute and the tightly dense cabinets or rack. To give you some perspective, if you were to go out and survey the market today and say, hey, data center in Metro Dallas. What's your idea of a high-density rack. They're going to come back and say, well, 5 to 12 kilowatts is what we really require. Well, one of our servers with H-100 is 12 kilowatts. And 1 kilowatt or one server per rack for hundreds of services isn't going to cut it. So we're looking at going to 45 kilowatts, or we are at 45 kilowatts, pushing the boundaries of air cooling, looking towards 150 kilowatts per cabinet for our buildings. Again, to condense the compute in tightly confined spaces, and that's extremely important. And then take another journey quickly. Right now, I think everybody in this room probably knows this, constraints in the market are compute, so access to GPUs and access to InfiniBand networking. Those are the constraints in the market today. That's what's slowing everybody down. And I'll tell you software developers aren't used to waiting. So they want it yesterday. I think that it's a very, very short-term limitation. The world can solve silicone problem, and there's going to be another problem that we're already seeing. There's not enough space in the world to put these clusters. There are not enough spaces in the world we say, "Hey, I want 45 kilowatts per rack. They're going to say, our HVAC system can't handle that". And we need that in one data hall, very, very tightly packed and say, well, we can put one here and one here and end up not going to work. So the next constraint is going to be space, which we've recognized. And then the next constraint is we're moving to a trend of gigawatt scale data centers. Finding gigawatts of power in Metro Dallas, that's hard, and that's expensive. And that takes a lot of time. Another great one I'd like to point out is, if I need a gigawatt of or let's say, 200 megawatts of power capacity in a metro area and a new substation has to be built and transmission lines may have to be run. That's going to take 5 years. And hundreds of millions of dollars -- or sorry, millions of dollars a mile to get that power to the data center. What you flip the script and said we're going to go to where the power is at. We're going to go where there it's stranded power. And there's 200 megawatts available in 6 months. What if we go there? The challenge for most of the operators say where is the network? Where is the connectivity? Well, we can build fiber to those data centers in 6 months for tens of thousands of dollars a mile. It seems like a no-brainer to me. So that's why I'm really excited about the work we're doing in North Dakota. Brad talked about SLAs. I will highlight that quickly. AI -- without getting to the detail. AI workloads are designed for failure. They're designed to have bugs, designed to checkpoint and they're designed to restart as soon as they fail, they'll restart. And I think we can really leverage that with our data center design by eliminating a lot of the SLAs by saving money to the customer, by getting these clusters in their hands as fast as possible because that is what they want. We don't have to wait for generators. We don't have to wait for UPS. Yes, there will be some experienced downtime. But when it's back up, the workload just picks up and start running again. That's the future of AI. Yes, I think that covers all of that. So to kind of wind up a little bit. Clearly, there are a lot of complexities. There are a lot of challenges. We are trying to bring super compute level systems to our customers overnight. And you just don't turn on a supercomputer overnight. It requires a strong team, requires a lot of planning, requires strong relationships. These are the things that Applied Digital brings to the table for our customers. We're solving the problems of power. We're solving the problems of scale. We're solving the problems of connectivity, we're solving the problems of doing your diligence on your OEMs and vendors. We're solving the problem of supporting that cluster once it's up and running. And we're doing this for AI innovators, large and small. And we're enabling to do really, really incredible things. So that really -- I started this off by how excited I am to be here. Hopefully, that came off in my presentation today because we're doing amazing things as a company. And we're offering is in a fantastic package of services for the future. With that, I will say thank you, and that's -- yes, there we go. [Presentation]

Okay. Next up, we have Applied Co-Founder, Jason Zhang, talk through site computing.

Hi, everyone. My name is Jason Zhang. I'm one of the co-founders of Applied Digital. I'll be sharing an update and an overview of Site Computing, which is our new cloud services business. We started this business, we started incubating and building the fundamental parts of this business in 2022 and officially launched this business model in May of 2023. So Site computing is a wholly owned subsidiary of Applied Digital, and we offer specialized computing around GPUs. And we offered GPU cloud computing to end markets in high-performance computing and artificial intelligence and a variety of other use cases that our end users are using the cloud computing for, but it's mainly focused on GPU compute resources and helping the ever-growing need in HPC. The cloud services offerings are focused on 3 major areas. So we have reserved compute, which is focused on much longer duration and much larger quantities of GPUs. And typically, what we do here is 6 months minimum contract length, all the way up to 5 to 6 years. And it's typically a large-sized training cluster that we deploy on behalf of our customers. And they're using these for large-scale language modeling training or other types of model training workloads. We also offer other types of compute contracts. We have first compute and also short-term compute again, you can think of these as much shorter duration length in terms of the contract and also some on-demand capacity or ecosystem partners, that will allow users to test and do shorter-term compute workloads using our hardware and using our infrastructure. There are a variety of GPUs that are part of our portfolio offerings. We have the typical older generation A40, A600 H-100s, which were kind of the potential GPUs in 2022. And preceding years, but 2023 has been really focused on H-100s and our deployment of H-100 have really ramped up since June of this year. For future offerings, we're now working closely with NVIDIA to explore the deployment of the Grace Hopper, which is the GH-200 and Hopper Next, which is the generation beyond GH 200. We're also currently in the midst of deploying L-40S, which is a redeployment or replatform of the L-40 and is going to be much more benchmarked to the H-100 performance, but specifically used for inference workloads. So we're working with our customers and NVIDIA to do test unit deployments of these right now. So that we can scale these out in large scale for inference workloads. So the reason why we have these different types of contract length and durations and different types of deployments is because there's never a one size fits all for all the end users. And there are some customers who much rather want to have a very large cost that they build out. And in order for us to commit to such a large CapEx expenditure on the equipment side, of course, it warrants a longer contract value and the longer contract duration. So those types of consumers of compute are, of course, going to be different than your on-demand type of needs, right, where sometimes you need first capacity for certain workloads that are a couple of hours or just a couple of days. Instead of committing to a reserve compute that is multiyear, sometimes you can just be in the market and absorb the first capacity that is available in the market. So the on-demand inverse and the shorter-term capacity is also very important because, one, it allows customers to get a glimpse of our offerings, but also allow them to have not as much upfront commitment where they might not be as pocketed as some of the large reserve customers. So again, it's a good mix so that we can better serve our end users. On the short-term contracts, we've partnered with a variety of different platform creators and software developers, who are developing platforms to better execute and increase the utilization of these GPUs, when they're in idle mode. So in these instances, we can better increase the utilization of existing equipment, but also offer attractive pricing and offer attractive deals for smaller customers who are just getting ramped up and also exposed to GPU compute. So in the GPU cloud revolution or this dynamic that has really taken off in the last 12 months, we've seen that location specific type of workloads are less and less so, because these types of very compute-intensive workloads tend to be location-agnostic. So it allows us as a company that was previously very focused on finding power and then building out computational resources really to play to our strengths, right? We can go out and find locations, where the power availability and the cost of delivering that compute is much more attractive than your typical computational epicenters like in the Bay Area or like on the East Coast around Virginia, right? So we have really put together a great offering from a geographical perspective, having locations around the Midwest and also Mountain U.S. regions where we can take advantage of ample amounts of power and being able to deliver that power into computational resources in a more effective and cost-effective way for our customers. And because of these workflows that are a lot less location specific, we can do that and take advantage of these opportunities. And as we are building out this cloud services offering, a very big component of the cost model is, of course, on the equipment and the facility, right? We've partnered very closely with the largest equipment manufacturers and of course, NVIDIA themselves, which provides the GPUs and the networking equipment to build out these clusters and build out these deployments. So we have a very close partnership with Supermicro. We also have very large orders in place with HP and Dell in addition to Supermicro. The key areas of differentiation for our GPU card services are in the following aspects. One, we've been one of the few cloud providers that have deployed H-100 with InfiniBand and networking at scale. We are deploying a couple of clusters that are ranging from 3,000 to even 8,000 H-100 GPUs in one location. And these clusters are some of the first clusters in the world that are being deployed by NVIDIA customers. So we're very fortunate to be one of the first to deploy these, but also working closely with our customers to work through a lot of the kinks that comes with deploying cutting-edge technology. We're also one of the only cloud providers that offer bare metal in an instance, we hand over access to the actual servers to our end users and allowed them to really control as much as access as they would like to have, when it comes to provisioning and using and utilizing the equipment. We also have a team of very experienced HPC engineers, storage and networking experts. That help support our end users in these deployments. Again, we're doing something that has been done by very few companies in the world and we need to be a leading edge and helping our customers figure out a lot of the early things that need to be figured out when you're deploying cutting-edge equipment. The last point is on vertical integration. I'd like to touch on the aspect of Applied Digital's core business, which is building data centers with the fact that site computing is now deploying and building one of the largest and fastest-growing GPU cloud-specific operators in the world where we can use applied digital to build GPU-specific facilities for site computing. This allows us to remedy a very important constraint in the market, which is data center capacity. As we grow site computing and we can deploy those GPUs in our own facilities, that allows us to, again, be a lot more flexible on how we deploy it, what size we deploy, what timelines we deploy, these types of clusters for our end users. And we're not beholden to a third party that we work with or a third party that we have to contract capacity with. On the product road map, we are working today, again, in the bare metal offerings where we provide the facility, provide the equipment and then hand over access to those machines to our end users. As we scale out our business offering and our services offering, we'll start to have a lot more virtualization and containerization, orchestration tools that we built -- that we will be building on top of the bare metal offerings. Again, these are additional offerings that continue to refine and improve the product offering, but it's not anything that's holding us back today. Again, a lot of our deployments today are bare metal deployment and our customers are very satisfied with that because we are deploying with end users that typically are a lot more sophisticated and also have the internal infrastructure piece. So bare metal access is what they prefer and what they work well with. So as I mentioned before, we started the business and started incubating the idea in 2022, but didn't really launch it until May of 2023. And that was the catalyst to that is, of course, our signing of our first large contract with Character.AI. And that relationship has blossomed and expanded from that initial contract that we signed for 5,000 H-100 GPUs. So Character.AI, right off the bat, they were backed by some of the largest companies, BCs in the world, such as Google and a16z, they raised $150 million at a $1 billion valuation pre-product and it has been absolutely amazing working with them to deploy one of the largest training clusters focused on H-100 NVIDIA technology in the last couple of months. Here is a recap of what has unfolded in the last couple of months since we started working with Character.AI. We initially signed the first compute contract with them at the end of May and we started deploying that first cluster for them in June. This is unheard of in terms of the turnaround and the speed. We worked very closely with Supermicro and NVIDIA to deploy as Character was a very key strategic account for NVIDIA and [ NOM ], of course, has connections all throughout NVIDIA's leadership. We were able to deploy that first 1,000 cluster for them, within the first month of us signing that contract. And since then, we've scaled up our commitment from Character all the way to 10,000 GPUs and now expanding to 16,000 GPUs and beyond for 2024. So again, a very good example of how we've landed a key account, deployed and executed for them and then expanded that relationship over time. So in the last 12 months, we've seen HPC really grow from a very niche offering to something that is on top of everyone's mind with this AI boom and generative AI taking over everything that we've seen. In business applications to consumer applications, we've really seen the demand for the fundamental layer that powers a lot of that really explode, right? Because all of these applications and all of these new models and new technology is based fundamentally on the equipment and the facilities and the computational resources that power all of these applications. We're lucky to be at the ground core of all of this and having built a GPU cloud business in a matter of a couple of months, where it usually just takes many years if not decades to build, has been quite humbling for me to see. And I've been super thrilled with the team that we've assembled to help pull these offerings together and deliver that offering to the market. And we've been overwhelmed by the amount of demand and interest from generative AI companies, large tech companies, research institutions and all types of different end users that we've seen our demand and forecast over to NVIDIA really skyrocket from a couple of thousand GPUs to now we're deploying 30,000-plus GPUs before mid-2024. We started Applied Digital 2.5 years ago, and we've seen that business really skyrocket and grow into something that is hardly resultant of where we started on day 1. Site computing is no different. We've only been at it for 4 or 5 months, but we've already seen a lot of traction in the market, and we basically built out a whole new business segment within Applied Digital in a matter of months. And we're super excited to see what the future holds for the business segment but also for Applied Digital broadly. [Presentation]

Nearly every new digital innovation is proving to be extremely power hungry and Applied has the power. We're turning to the stage to talk about what's next for Applied and how the team is powering the world's next innovations as West Cummins. But first, a quick video. [Presentation]

You guys can hear me? Perfect. So to wrap up, and then we'll do some Q&A. So first, I want to thank everyone, hopefully, a lot of people in the room and maybe some people are new to it, but everyone generally gets to see me and I speak a lot at a lot of different events. So it's great to have other people at our company, other employees, see the full team. I'm super proud of the team that we've assembled here at the company and what they've accomplished. We started, as Jason mentioned and I mentioned earlier, we started 2.5 years ago. Our August quarter, we just did a little over $36 million of revenue. So that was over 50% of what we did. That was our first quarter. That was over 50% of what we did in the entire last fiscal year. We've guided for approximately $400 million this year. And I want to make a couple of points for our company. I think what we have done extremely well since the start of our company is speed to market. That's been in the Bitcoin mining market, which I draw a lot of parallels from that market to what we're seeing now in AI. Just what we saw at that time was, in 2021, everyone was rushing to get the Bitmain S19j Pro miner, who is -- how many could you get? That was the big bottleneck. And then as you went into '22, there were warehouses full of S19j Pro miners looking for data center capacity to plug into. That's where we came in. And we built out our 500 megawatts of data center capacity. And the stat I'd like to look at for this market that I think is -- there's actually some parallel. I don't want to compare them too much, but there's some parallel here where -- the forecast is roughly 1.2 million H100 chips for NVIDIA this year, somewhere in that neighborhood, 2 million for next year. So let's say, 3.2. 3.2 million H100 to the end of 2024 needs about 4.8 gigawatts of IT power. So that's probably total data center power around 6.5 gigawatts. So 6.5 gigawatts on those fully loaded through the end of '24 compared to a data center market, it's roughly 22 or 23 gigawatts worldwide. It's a massive step up and then it should just keep going from there. So I think for us, we could land in a similar position. We're in the process of kicking off. You got a lot on our new kind of AI brain data center, the importance of the network density and being within the magic number of 30 meters and that entire building can be within that 30-meter radius of the network core, which, in theory, means that you could put roughly 60,000 H100 GPU same spine, same network core, same training cluster, there will be nothing like it in the world. So we're excited about that. But I think we're stepping into a market where we have 300-plus megawatts of power that's contracted that will come online next year. So I think we have a really special window here in the next 24 to 36 months where power is going to be a massive -- it's going to be a massive supply constraint and it's not there yet. So when this started to happen, everyone ran out into the market and us included, and contracted what was available, contracted everything through '24. But I think when we see next year, we're going to start to really run into the constraint of finding places to put these types of workloads. And by the way, that was just the NVIDIA math, right? It doesn't account what AMD or Intel are going to do or just standard kind of data center growth. So I think we're in a really unique position. And to put a point on this, I won't say which power provider, but one of the largest utility networks in the country. Our power guy was speaking to his friend there 2 days ago. And his friend mentioned that they had just received an inquiry for 900 megawatts of power. And that's a big number. And he said it's the eighth inquiry that they have received, it's between 500 megawatts and 1 gigawatt in the last 2 months. And it's all data center driven. And so I think we have a lot of exciting things going on. We're close to getting all of our blockchain data centers ramped up, hopefully, next 2 weeks. And then the AI cloud is growing quickly. We started -- now we have large language model customers. We have text image model customers and a copilot -- software co-pilot customer. So we have a good growing customer set there, really strong pipeline, as Jason mentioned. And I think that gets a lot of attention. Everyone's -- a lot of people for us have been concerned about the blockchain piece turning on. And so we have that, I think, solved at this point. But I think the piece that gets overlooked for our company is the value of this contracted power and the land to go with that power. And in Ellendale, we have in North Dakota, we have the building permits, and we've done the geotech and we're getting ready to mobilize as Brad said, and put the foundations in for that building. So I think that piece of our company, which I think is probably the most valuable piece and most important piece often gets overlooked, and it's going to become, in my opinion, extremely apparent of how valuable that is over the next 6 to 9 months. I think everyone is going to really see the power constraint that I don't think is completely recognized in the space right now. So with that, I think there's a lot of exciting things going on for our company. We've accomplished a lot, super proud of our team. Glad you were all able to see some of our team members and leaders of our groups here. And Erin, I think we open up to Q&A now. Yes. So we'll do Q&A. David, why don't you come up to in case anyone has financial questions?

Talk about power. Can you just beyond the 300 megawatts contracted, I'm sure you guys are doing a lot of site surveying other power sites. Just how constrained that beyond kind of...

David, what was the message? Was it our pipeline is a little over 1 gigawatt outside of what's already contracted? We were reviewing that a couple of days ago. So I think it's around 1.1, 1.2 gigawatts of things that are in what I could say, the pipeline of assessment. But I do like to mention this, I like it to back again to Bitcoin. I think it was aggressive power chasing when Bitcoin moved to the U.S., right? And I think it feels like 10x that right now, and we lived through that time as well. So it's definitely everyone is looking. I think we have -- our guy and the way we've gone about this for the last 3 years or I guess, 2.5 years, we have a pretty good understanding about how to find those, what the kind of elements that we need within that, which I won't name all of those up here, but we have a pretty good idea, and I think we have a lead in being able to go out and find that power a bit. But beyond what we have contracted, the pipeline that we were reviewing 2 days ago is about 1.1, 1.2 gigawatts.

You guys have a guidance of fair amount on the last couple of earnings calls in the last few presentations you've done. And I was hoping you could unpack it a little bit further for us because you just completed your first fiscal quarter of '24. The numbers you've talked about are significant. And there's obviously an implication is that there's an extraordinary ramp in the next couple of quarters. And so I'm hoping that you could maybe unpack it a little bit more for how we get from 10 of adjusted EBITDA, for example, or whichever metric you think is most relevant to the numbers you're talking about on a trailing basis by the middle of '24. And then maybe talk a little bit about your CapEx -- your outlook for CapEx and how that gets funded?

Sure. So when we talk about our fiscal '24 guidance that we reiterated on Monday, there's really 2 out of the 3 businesses primarily contributing to that. So you have the blockchain data centers that we've said many times when fully ramped, get to roughly $300 million of revenue and $100 million of EBITDA based on that business. And so when you think about that on quarterly progression, we had Ellendale for a part of the quarter in Q1 that we just reported. And we had all of Jamestown and we didn't have any of our Garden City, Texas facility. We've said now that we expect Garden City to turn on by the 23rd of October, and that will ramp through November. So you have those for November, and then you'll have some of the AI cloud business, several clusters running for the quarter there. And we've said -- I think David had the numbers up here. But on the AI cloud, we assume on the H100 per cluster, so the 1024, so think for 1,000 GPUs, about $1.5 million of revenue per month and EBITDA on the AI cloud business is, let's just call it, roughly 80% is the EBITDA. But that's again why David called out specifically earlier, he was calling for an EBIT margin or an op margin because we point to that because I think that depreciation is real. But when we're guiding for an EBITDA number for the year, that's what matters. And so then if you're looking out into February, you get essentially from December 1 to the end of February, sorry, we have weird quarters. But you have roughly $75 million of blockchain that $300 million divided by 4, and the same kind of for the May quarter. And then we've talked about -- I've talked about publicly 26,000 GPUs online in the April time frame. So if you step those up, but let's just go to what a full quarter of those combined look like. So 26,000 GPUs in a single quarter. So 1.5 million times -- 26x. So that's $39 million a month x3 is $117 million for a quarter plus $75 million from the blockchain hosting. That gets you to a little over 190 -- I think, 192, is that right? Trying to do the math in my head, but that's roughly where that is. And so then you get -- this is on a quarterly basis. Those would go to sort of $25 million of EBITDA from the blockchain data centers. And then the $117 million x0.8 would be your EBITDA from the AI cloud business. So you see those numbers get pretty large, pretty quickly as we roll that out. And so the CapEx portion. The CapEx portion you should think of for the clusters, every cluster running, let's call it, roughly $40 million per. We get significant prepayments from our customers. So our primary customer, we get a little over 60% prepayment. This has been disclosed in our public filings. So we get $22.5 million per cluster that goes out. And then we've been so far successful with vendor financing, and we're hopeful that can scale significantly. And then we've also engaged a bulge bracket investment bank that's working on a GPU debt structure, specifically for us, something similar to what we saw with CoreWeave. And so we -- right now, we feel comfortable about financing those. We've done pretty well so far. We'll see how far that scales. But the debt financing takes care of the vast majority of the financing needs and whenever the debt financing doesn't take care of, we get the prepayments from the customers to satisfy the rest of that. And so just to talk about the contracts that we're signing, I've talked about this before. This is -- I mean it's going to ramp really quickly, but I call it kind of trying to go into this market in the safest way that we can, which is take-or-pay contracts from our customers that generally pay for the expense of the GPUs over the life of the contract. So we're getting payback in kind of 24 to 28 months; in the character contract, it's 24 months. So maybe we don't get fully paid back, but we get 90-plus percent of that paid back to us. So we're trying to match all of these appropriately from a CapEx perspective and risk perspective to the company. So that's where we're matching the take-or-pay contracts. And then also the types of customers that do we really seek out our customers that I like them to have, obviously, good investors financing them. A product in the market already is always nice with a big user base. And so I'm getting kind of in the weeds here, but when you think about the race that people are running to try to win here, why do you want the people that have a lot of customers. So if you get the best model in the market, whether that's an LLM, whether it's image, no matter what it is, you get the most users -- with the most users, you get the most data and then you got to train the next version of the model, which is even better. So it's kind of this virtuous cycle if you can catch on the cycle. So that's why I prefer to have those types of customers, and that's mostly who we've attracted. So did I answer? I think I answered everything in more.

George. George Sutton, Craig-Hallum. So Jarred, I thought did a great job of answering one of the misnomers in the market, which is that the hyperscalers will just go out and build their own facilities that hopefully won't need. So basically, if I heard correctly, they were planning to build 2/3 themselves. They're now looking at only 1/3 themselves, 2/3 going to folks like you. So I ask that in the context of logic of a hyperscaler being your anchor customers in one or more of your near-term facilities and then I'll hold for a follow-up.

Yes. So Jarred is an expert in the industry, I'd defer to him on what the switch back to only doing 1/3 and 2/3. I think I will go back with my own opinion that power is the biggest constraint, near-term power availability is the biggest constraint in the industry. We have kicked off a formal process for the anchor tenant for our North Dakota facility. We kicked that off in mid-September, and hope to wrap that up fairly soon. But I think those types of customers you're talking about are the most likely for us to be our anchor tenant at that site. And we had a slide, Rich has a slide, I can -- remind me to give this to you later about you're seeing these hyperscale customers, if you see their data center locations, they already have been moving away from kind of these cloud regions to where they can find power. And so we're seeing a lot of interest in our North Dakota site and hopefully, we can wrap that up.

Okay. And one other question. Looking out, say, 24 to 36 months, what will this company look like from the perspective of you've got a Sai Computing business, which will look largely separate or separable from your data center business, which could become a REIT or obviously become part of a REIT. Can you just give us longer-term thoughts there?

Yes. My thought there is we made Sai a wholly owned subsidiary for a reason. The contracts for cloud go into Sai. And then the idea is that Sai Computing is an AI cloud services company and then Applied Digital is a data center company. And if I think through our timing, build out 300-plus megawatts over the next 24 or 36 months. I think at that point, Applied Digital has the scale to REIT on the data center side and then the Sai Computing side out, which is kind of why we've planned for that. But you're completely right on them being 2 separate businesses, one naturally being a customer of the other.

Rob Brown at Lake Street. I just want to get your opinion on pricing in the industry, how you see it changing? Are you pricing this demand environment into your contracts?

So pricing in just data center in general, is very strong and pricing continues to move up. The goal for us is to be building -- it would be fantastic if we could get Tier 3 pricing for the style of build we're doing in North Dakota, and I think this market maybe helps us get there. But that would, for me kind of be the dream, but we'll see when we go through this process, if that's what we get to. So if you think about kind of Tier 3 data center pricing, this is the way data center prices, which is important because it's different than kind of what we've done in Bitcoin or you're doing the hourly on GPUs. So you do a price somewhere in the neighborhood of, say, $120 to $150 of monthly rent per kilowatt. And so that means per megawatt, that's $120,000 to $150,000 of monthly rent and then you do a pass-through of power and some pass-through of data charges. You don't upcharge the power kind of like we do on Bitcoin mining. So that's how you should think about that in the model. So if our facility at 100 megawatts is say, we're at the low end of that $120,000 you can just do $120,000 x 100 for the monthly revenue. You'll have additional revenue on like the power pass-through that is lower margin or zero margin, but you still end up kind of shaking out to like that 50% EBITDA margin.

And with -- can you pivot a little bit to liquid cooling from the alternative prior? Is there just any increase in CapEx there, which should be aware of and how that get passed on to customers?

Yes. So there is some increase in CapEx, which we talked. So there's -- we talked last call about kind of the CapEx going from this 4.5 to 6, that's 2 pieces. That's building vertically and then it's added primarily adding the liquid cool component to it. And so the upside of that is we expect to be able to run much higher densities. And then also, it makes -- so if we can run higher densities, provide liquid cooling, it makes it even more of a scarce asset in the marketplace in my opinion. And then we also run at a lower PUE, Brad, correct me if I'm wrong, liquid should get us to a lower PUE?

Yes.

Are you seeing any kind of market exporters or service or cost of data centers [indiscernible].

So I think what we're seeing is the people are much more open to going to regions like North Dakota for multiple reasons, there's a capacity issue in general that I think is going to get much worse. But the workloads that we're doing is just -- it's wildly different, right? It's just -- it's not video streaming, it's not Zoom calls, it's not TikTok, it's not mission-critical apps. It's much more compute driven rather than comms driven, right? The last 25 years have been completely comms driven, in my opinion, from a data center perspective, right? It's been all these other apps, primarily video that drives it. So this is extremely compute driven and so you don't need that latency. So people are figuring that part out plus there's just -- there's already a very limited supply. And so people are going to be forced into getting supply where it's available, those 2 items. I will say one other thing about this, though, you have to be careful about that I've said this many times publicly, we can't nor do I think in other Bitcoin miners convert their facilities into HPC facilities. So back to the low cost, like we're down a lower cost. But I think there's a limit that people are willing to put $250,000 servers into. I know I would never put them into my Bitcoin facilities. I think we built pretty good ones, actually, but I still would never put them in there. So there's definitely a spread. And so think about the $6 million on the megawatt built, Brad, Tier 3 is $10 million to $12 million, typically, is that right? $8 million to $10 million? So it's still drop down. Big difference there backup generators, the diesel backup generators on sites, we're putting some in to run mechanical and a few kind of mission-critical things but not for the full 300 megawatts that we build out, those are fairly expensive. There will be the ability to add them later if we have a customer that absolutely requires it and is willing to pay for it, but we're not doing them initially.

Are you seeing a shift in customer expectations on that?

It's Mike from Northlands. Character.AI is obviously going to be a really big customer going from 1,000 GPUs right now at 16,000. Can you just talk about the pacing there and maybe when a second customer or customer #2 is going to begin to get some of these other GPUs?

Yes. Second customer in November on GPUs. Let me kind of split this when we talk about large customers and Character.AI. And this is important for what we're doing in North Dakota, too. So we have the things that we're ramping up and it will be Jamestown. We'll run 5,000 of these and then we have Denver, Salt Lake City, Las Vegas, Minnesota, right? So we've had these third-party colos that we've pulled up. And so they'll be spread out. And then the way we view the cloud business is when you're looking out into '24, as we get this capacity online in Ellendale in North Dakota, we have demand from several companies for very large training clusters. And the capacity -- the electricity capacity, the data center capacity doesn't exist really for these. So when you think about someone that wants to do 22,000 H100 GPUs in a single cluster, and they're going to need close to 40 megawatts of power around that 30-meter radius that we were talking about earlier, those are the types of things that are perfect for Ellendale. And that will be capacity specifically for that. And when we put those large training clusters in Ellendale, the smaller training clusters that are more in cloud regions around the country will be really kind of perfectly positioned for inferencing, if that makes sense. So I think inferencing, there's many different versions of what inferencing will look like, but I think those will be start to move towards the inferencing part of the market because they're based in those cloud regions. So I want on that kind of 16,000 comment, just bucket that into very large training model, specifically in the Ellendale location. But our second customer, we'll get capacity in November.

Roughly about how many thousand?