This discussion examines artificial intelligence factories and next-generation GPU cooling solutions for data centers. Pamit Surana of Akash Systems, co-founder and chief commercial officer, participates in theCUBE Research interview at theCUBE Studio, New York Stock Exchange, hosted by Gemma Allen and Dave Vellante. Surana frames the conversation around AI infrastructure, data center cooling and server efficiency.
Surana explains Akash Systems' deep‑tech approach to GPU thermal management using lab-grown diamond cooling developed from space‑grade origins. They demonstrate cooling with an on-stage ice experiment and cite AMD testing that shows approximately 10°C GPU temperature reduction. Surana highlights recent server launches from Dell with NVIDIA H200, Supermicro and MiTAC and describes how diamond cooling enables higher server density in air-cooled facilities.
Surana outlines the commercial impact and go-to-market strategy. They report customer analyses estimating a $2 million cash impact over four years per server and up to a 50% increase in token throughput. The discussion identifies target segments such as neoclouds, enterprise and hyperscalers, scalability to hundreds of thousands of servers, a diversified global supply chain with planned U.S. diamond manufacturing, and the implications for air versus liquid cooling strategies.
This conversation provides insights for professionals focused on data center design, GPU cooling, AI infrastructure and sustainability, and for decision makers evaluating server efficiency and token economics.
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Pamit Surana, Akash Systems
This discussion examines artificial intelligence factories and next-generation
GPU cooling solutions for data centers. Pamit Surana of Akash Systems,
co-founder and chief commercial officer, participates in theCUBE Research
interview at theCUBE Studio, New York Stock Exchange, hosted by Gemma Allen and
Dave Vellante. Surana frames the conversation around AI infrastructure, data
center cooling and server efficiency. Surana explains Akash Systems' deep‑tech
approach to GPU thermal management using lab-grown diamond cooling developed
from space‑grade origins. They demonstrate cooling with an on-stage ice
experiment and cite AMD testing that shows approximately 10°C GPU temperature
reduction. Surana highlights recent server launches from Dell with NVIDIA H200,
Supermicro and MiTAC and describes how diamond cooling enables higher server
density in air-cooled facilities. Surana outlines the commercial impact and
go-to-market strategy. They report customer analyses estimating a $2 million
cash impact over four years per server and up to a 50% increase in token
throughput. The discussion identifies target segments such as neoclouds,
enterprise and hyperscalers, scalability to hundreds of thousands of servers, a
diversified global supply chain with planned U.S. diamond manufacturing, and the
implications for air versus liquid cooling strategies. This conversation
provides insights for professionals focused on data center design, GPU cooling,
AI infrastructure and sustainability, and for decision makers evaluating server
efficiency and token economics.
>> Palo Alto studio connections, Silicon Valley, and Wall Street. here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen and we are talking AI factories, one of our programs with NYSC Wired. And joining me now is Pamit Surana, Chief Commercial Officer and co-founder at Akash Systems. Welcome, Pamit.
Gemma Allen
>> Great to be here.
Gemma Allen
>> So this is some deep physics we're going to talk right now.
Gemma Allen
>> Yes. We're going to go deep today, yes.
Gemma Allen
>> We are going to really think about how we get the maximum energy from these GPUs in the most cost-efficient way possible.
Gemma Allen
>> Yes.
Gemma Allen
>> Break it down for me. What exactly is Akash systems?>> Sure. We're a deep tech company backed ... Our seed investors include Vinod Khosla, Peter Thiel. We fundamentally, our mission every day is to solve the heat problem on GPUs. And we do that by using lab grown diamonds.
Gemma Allen
>> Wow.>> So this is a lab grown diamond. It's obviously not exactly what we use, but that is really special. So when you talk about physics, the physics of heat is what we deal with every day and it's what the ecosystem's dealing with every day. That piece of diamond is the most thermally conductive material on earth. That'll move heat faster from one end to the other than any other material. In fact, it's five times faster than the second most thermally conducted development, which is copper.
Gemma Allen
>> Wow.>> And that's what everyone uses today. So our firm, we take the innovation of mother nature's carbon and interestingly, we use it to decarbonize because that makes heat move quicker. And when you move heat quicker off a GPU, it lowers the wattage and power that you need. So you can get more out of one watt by using that. And if people are wondering why we have ice, this is really neat.
Gemma Allen
>> Yes. I love this.>> I'd love to show you how, and I want you to experience just how fast that moves heat. So this is an ice and pretend this is an air cooling system. This is the heat sink. This is what we're sending the heat to. Your hand is the GPU and your 98.6 Fahrenheit. And we're going to take the heat from your hand across the diamond and into the air cooled ceiling. And I want you to just hold it like you're cutting butter and take a look how fast that goes.
Gemma Allen
>> Wow.>> And there you go. You cut right through the ice.
Gemma Allen
>> Wow. And it got so cold.>> And your hand got cold, right?
Gemma Allen
>> Yeah.>> That's us cooling the GPU.
Gemma Allen
>> That is crazy.>> Exactly. In tests by AMD and our customers, the GPU got cooler by 10 degrees Celsius. Your hand is still cold.
Gemma Allen
>> How long does it take roughly for this to return to the heat it was before I cut the ice?>> Seconds. It should be right down.
Gemma Allen
>> Wow. Yeah, I can feel it.>> There you go.
Gemma Allen
>> That is incredible.>> Yeah, you can take the room's temperature.
Gemma Allen
>> Okay.>> It'll take whatever temperature. If you put this on a cup of coffee and dipped it, you'd burn your hand. And you felt how fast that was.
Gemma Allen
>> Yeah, that's insane.>> Right. So that's the speed removing heat.
Gemma Allen
>> So you guys are competing against copper essentially, right?>> Correct. Yes.
Gemma Allen
>> When I see technology like this and developments like this, my first thought is why did this not happen sooner? Sure. Why did it take so long and for you guys to come together and think, how can we solve for this to realize these innovations?>> Yeah. There's two parts. And all good stories start with beer.
Gemma Allen
>> I love it.>> It was a couple of years ago our PhDs ... So the four co-founders on the non-PhD, I just bought in to lower the average IQ.
Gemma Allen
>> I doubt that.>> But what these guys did was think about how they were doing this in space. So the name Akash is the Sanskrit word for space for the sky. So we solved the heat problem on satellites. And if you think about space, it's really the harshest environment because when you're up there in the atmosphere, it's 2,000 Celsius heat from the sun. You have solar radiation bombarding electronics. And when your satellite rotates, it goes to minus 200. So you're going from plus 2,000, minus 200 and it's rotating. So whatever you put up in space has to handle that. We complain going in and out of a building, and imagine the difference. So if you can solve it in space, you can solve it on anything less than that. And so two and a half years ago when the ChatGPT and all of this started to come out, Felix, Ty, and Dan, the PhDs were saying, "You know what? We did it in space. A GP is only a hundred Celsius. We can do it." And six months later we did it. And since then we've launched a Dell NVIDIA H200. We launched a Supermicro SMC-300 with AMD, a MiTAC AMD350. And we're off and racing now.
Gemma Allen
>> The rest is history, as they say.>> Yeah. So it started in space.
Gemma Allen
>> I love that. I mean, it's crazy though to think how these innovations come to life here on earth, but you guys just had some news funding announcements of late.>> Yeah. So really exciting. When you think about the broader aspect of what's happening with all the capital being deployed, everyone's looking at efficiency. That's capital efficiency, heat efficiency, GPU efficiency, token efficiency and everyone looks at two metrics, the cost per token and then the revenue per watt that you're getting from the data center. We help both. So we got funding from a major Wall Street bank, two to $5 billion at really subsidized rates because we're solving the densification issue at a data center and we're solving increased tokens at the server level. And so we're really excited about having that amount of capital to loan to our clients who buy diamond cooled servers.
Gemma Allen
>> Wow.>> Yeah.
Gemma Allen
>> So talk me through the total addressable market here. Let's break that down a little bit, right?>> Okay.
Gemma Allen
>> Because I'm sure this major bank saw a lot of opportunity. We know there are huge constraints, huge bottlenecks, and it feels as though suddenly every company is a technology, AI led company. A lot has shifted very quickly.>> Yes. Yes. Sneaker companies are AI companies. Yes.
Gemma Allen
>> When you guys thought, okay, we know this technology works. We know this science has huge value in data centers across the world. We're going to now begin thinking about how we can connect that to converted opportunities to bring cashflow in. Where do you begin? Are we talking neoclouds? Are we talking hyperscalers, enterprise players? Where do you start?>> Great question. So from a customer segmentation standpoint, we're focused on neoclouds and enterprise and also working with hyperscalers. The neoclouds have a lot of economic and capital pressure to be really efficient and they're moving really quickly. So we think our product gives them a great solution to get maximum tokens out of their server and getting the most out of the data center they own or are renting from. On the enterprise side, these are companies whose servers have been sitting in data centers that were built 10, five, 20 years ago. They need air cooling options. And so our total addressable market today is on the air cooled data center market and we think there's absolute large capacity to unlock there with enterprise and with neo clouds. Geographically, your question, we're going to start in the United States, but we are a global firm. We did ship our first server over to India and now today we just feel like there's so much opportunity here in the US with existing data centers and then the new ones coming online.
Gemma Allen
>> So you win a new deal, you talk to this company, you understand there is some reverse engineering possibly that needs to hop in here. There are racks that could potentially be optimized. Or you're looking at kind of incremental new opportunities or both. What do these kind of engagements look like? And talk to me through what it would take if you did have a number of racks that you want to basically reverse engineer using Akash systems. How quickly is this deployed? How quickly do you realize value from this?>> Yeah, great practical question. So there's two customer segments we look, two opportunities we look at. One is clients who are looking to upgrade their existing servers and are already looking to buy the newest, latest and greatest. So for that, we call that the upgrade server refresh cycle and they can buy a new diamond cooled server for that. The other one are ones who maybe just bought a server a year or two ago and still want to run the life of that. We can do an upgrade, some call it a retrofit where what we do is we go to their data center, we put our diamond solution there and they turn it right back on and they've now got better performance. So we have two segments, both.
Gemma Allen
>> Wow. Talk me through the ROI. What is the immediate ROI on this?>> It's material. So we had another major Wall Street bank do an ROI four years, cash impact. You're looking at $2 million of cash impact with our servers that generate 50% token increase versus a stock and you can run our servers instead of a 75 Fahrenheit data center environment, like we're sitting in this nice comfortable air, you can turn off the power to air cool data center, let it run to 95 Fahrenheit and now you've unlocked more energy to put more servers in.
Gemma Allen
>> Wow.>> So for us, we think the ROI is at the server. So if you're getting 50%, they normally cost a half million. We just saved you a quarter million dollars just on that. Then I saved you the construction cost of building a new data center, which is 15 million per megawatt. So that's like another 300,000. So that's a half million of just avoiding cost. And when you get 50% more tokens, they're whatever, 30 cents, 80 cents, millions of tokens over four years, that's another million and a half. So it's $2 million of pure incremental benefit. Absolutely material. But again, for companies like hyperscalers or neoclouds, they may put aside that entire ROI because you can get capacity online today. And in this race that they're all in, we think that is in and of itself a great reason to buy this. And then all of that other is incremental ROI.
Gemma Allen
>> We're back at the time in tech where money is almost secondary to the race.>> Yeah. Correct.
Gemma Allen
>> It's true. We hear and say this all the time. How scalable is this?>> Sure.
Gemma Allen
>> So it sounds like it's a fascinating business. Sounds CapEx heavy though from your perspective too, I'm sure, right? How scalable is this midterm? Like over five years, what sort of market traction are you hoping for here?>> Sure. In the midterm, I think we're quite scalable to significant volume. Hundreds of thousands of servers, a couple million of GPUs in the midterm is easy to scale to.
Gemma Allen
>> Wow.>> And so today we're starting with the enterprise and neoclouds and then parallel tracking with the hyperscalers.
Gemma Allen
>> I'm interested because you mentioned enterprise and we hear a lot about enterprise and other shows. Not so much in our factories. I will say that we hear about it, but we don't have a lot of folks actually bringing their case studies, their unique story to the show yet, right? Because I feel like it's still a part of the industry that are trying to understand where they converge. Enterprise meets full end to end management. What are you actually seeing? Like what sorts of workloads are you seeing enterprises truly own end to end? And where is this intermediary vendor ecosystem still fully locked in?>> Sure. We're seeing enterprise coming off of the sidelines. I think you have your natural progression of the innovators, the leaders and some fast followers and I think they're trying to figure out how do they operate in this fast moving environment? How do they get their enterprise agentic AI progress working? Again, we've seen the impact that has on SaaS companies. So I think they're trying to figure out, and then within the stack they're also trying to figure out. I think you're seeing them go in a traditional enterprise approach, which is dual or multi-vendor strategy and I think we'll continue to see that. And as far as having a bias towards open source, I think they're going to keep that lane wide open for them and go after that because they're going to need that going forward.
Gemma Allen
>> Interesting.>> Because how the sector is changing across all the different stack layers, I think enterprise, they're starting to just keep that open.
Gemma Allen
>> When we think about AI factories, we hear a lot about supply chain constraints, right? From every angle, everything, right? Due to engagements between the socioeconomic, geopolitical, and then here on the ground, right?>> Sure.
Gemma Allen
>> What does your own supply chain model look like? I assume there is some sort of global footprint here.>> Yes.
Gemma Allen
>> And in terms of scaling that out, how do you see that trajectory going? You said you're very US focused right now, but we all know why tech went global, right? It was for costs.
Gemma Allen
>> Everyone needs it. Yes, that's right.
Gemma Allen
>> So what's the reality here?
Gemma Allen
>> For us, we've got a great diversified supply chain. It's global. We have Asia, we have Europe and we have the United States and part of our capital deployment is to also build our own reactors to create the diamonds here in the United States as well.
Gemma Allen
>> Wow.
Gemma Allen
>> And we have significant support for that as well.
Gemma Allen
>> You guys started as academics, what a great idea.>> Yeah.
Gemma Allen
>> What's the R&D like here? How intensive, regular? And what does this R&D relationship look like with companies like NVIDIA and AMD and Broadcom? How connected are these conversations?
Gemma Allen
>> I can't get into specifics, but I can assure you that we are talking to several chip companies because we are solving the number one problem that they have with the number one thermally conductive item and the R&D process is intense, but again, we have that unique advantage of that we've learned to do it at 2000 Celsius with solar radiation and that amount of heat we continue to innovate and we innovate on every GPU, whether it's a Blackwell, a Vera Rubin, whether it's the Helios going to the future lines at AMD. So we're constantly in innovation. You could almost think of us as no different than our pharmaceutical company where we have multiple tracks solving multiple problems because each of these have different powers, it's air cooling, liquid cooling, it's a lot of different items that go into it. So that is a lot that we focus on.
Gemma Allen
>> Talk me through what you are seeing and hearing and believing in the air cooling, liquid cooling space. We met at GTC this year, lots of conversations around liquid cooling, what's ahead. I mean, that's been a promise for a long time. It hasn't really met reality if we're being frank.>> Absolutely.
Gemma Allen
>> What do you think?
Gemma Allen
>> I categorically believe that air-cooled data centers, legacy air-cooled data centers is the untapped market to satisfy this unbelievable appetite for power. Because when you look at the installed capacity in the United States, the dozens of gigawatts that we have, the best place to leverage that with the publicly traded pure play data center companies, the Equinix, the Digital Realtys and others, they have installed capacity. Most of them cannot handle liquid cooling that they're building in the future. So what do you do with that existing data center? You have to learn or find ways to operate it to squeeze more power and dedicate it to the servers. And I think that you were talking about the socioeconomic. The more and more communities decline the permits for these liquid cooling data centers-
Gemma Allen
>> They're happening.>> Yeah. That slope of the online capacity coming online, it's going to go this way, but the demand is going to keep going up. And that gap, they're going to have to come back to those legacy air-cooled data centers. And for us, there is massive opportunities in that and that was just a domestic bias. If I think of Europe where the old European cities and the data centers that have been built, they don't have space for liquid, so they have to do their best with air cooled. So we think we'll see a big opportunity there.
Gemma Allen
>> We're seeing a lot in Europe in the data center space in those much colder territories, right?>> Yeah.
Gemma Allen
>> In Finland and close to the border of Russia.>> Yes. Iceland.
Gemma Allen
>> Yeah. Again, which is very interesting. Ireland at one point had interesting data center model because of the rain, right? So a lot is changing. But again, this is all essentially about economics. It's about maximum output from input, right?>> Yes.
Gemma Allen
>> What are your thoughts? What else do you see? Are there any kind of longer tail opportunities that we haven't quite uncovered yet, do you think?
Gemma Allen
>> I think beyond the air cool data center, I think the longer tail will start to be how the business model changes for pure play companies in the neocloud space or if you're just selling GPUs, I think you're going to see a switch from GPU by the hour to token based output models because you have to go revenue and when you're borrowing this kind of money to get efficiency, you're going to have to be at the GPU level at the data center level, then at the GPU level, the rack level, the data center, the VLLMs, the models. That was going to take time, but I think that's where the industry has to go to because as we've seen with the hyperscalers, the capital deployment is really important because they went from cash that they had, cash on the balance sheet, now they're moving to debt. And as we all know, if you want the canary in the gold mine, look at the credit and the debt covenants and the expectations, it's going to have to be on tokens for cost and the revenue per tokens. So I think the long tail is us watching the industry move from GP by the hour, which is like an occupancy based model versus revenue per token or revenue per watt because the WAT encompasses everything and when you're financing, you're financing the WAT, so you know the measure has to be the revenue. I think that's what we're going to see.
Gemma Allen
>> Wow. A fascinating theory.>> Yes.
Gemma Allen
>> So for me, last question, what's ahead? I mean, you guys have an interesting couple of months even since we met in San Jose. What does the next year look like for you and the team?
Gemma Allen
>> Yeah. The next year looks like we're doing a series C round. We've got great interest and success with what we're doing. We'll be announcing several new liquid cooled products as well. And in July we'll be at the AMD AAI event with our showcase product announcing the new 1,400 watt server that we're coming out with.
Gemma Allen
>> And I believe we might be too, so I look forward to that.>> Oh, we should do this again. Yes.
Gemma Allen
>> Pamit Surana, thank you so much for coming on theCUBE..
Gemma Allen
>> Gemma, thank you so much.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the NYSC. This is AI Factories, one of our programs with NYSC Wired. Thanks for watching.
>> Palo Alto studio connections, Silicon Valley, and Wall Street. here with Dave Vellante, my co-host.
Gemma Allen
>> Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen and we are talking AI factories, one of our programs with NYSC Wired. And joining me now is Pamit Surana, Chief Commercial Officer and co-founder at Akash Systems. Welcome, Pamit.
Gemma Allen
>> Great to be here.
Gemma Allen
>> So this is some deep physics we're going to talk right now.
Gemma Allen
>> Yes. We're going to go deep today, yes.
Gemma Allen
>> We are going to really think about how we get the maximum energy from these GPUs in the most cost-efficient way possible.
Gemma Allen
>> Yes.
Gemma Allen
>> Break it down for me. What exactly is Akash systems?>> Sure. We're a deep tech company backed ... Our seed investors include Vinod Khosla, Peter Thiel. We fundamentally, our mission every day is to solve the heat problem on GPUs. And we do that by using lab grown diamonds.
Gemma Allen
>> Wow.>> So this is a lab grown diamond. It's obviously not exactly what we use, but that is really special. So when you talk about physics, the physics of heat is what we deal with every day and it's what the ecosystem's dealing with every day. That piece of diamond is the most thermally conductive material on earth. That'll move heat faster from one end to the other than any other material. In fact, it's five times faster than the second most thermally conducted development, which is copper.
Gemma Allen
>> Wow.>> And that's what everyone uses today. So our firm, we take the innovation of mother nature's carbon and interestingly, we use it to decarbonize because that makes heat move quicker. And when you move heat quicker off a GPU, it lowers the wattage and power that you need. So you can get more out of one watt by using that. And if people are wondering why we have ice, this is really neat.
Gemma Allen
>> Yes. I love this.>> I'd love to show you how, and I want you to experience just how fast that moves heat. So this is an ice and pretend this is an air cooling system. This is the heat sink. This is what we're sending the heat to. Your hand is the GPU and your 98.6 Fahrenheit. And we're going to take the heat from your hand across the diamond and into the air cooled ceiling. And I want you to just hold it like you're cutting butter and take a look how fast that goes.
Gemma Allen
>> Wow.>> And there you go. You cut right through the ice.
Gemma Allen
>> Wow. And it got so cold.>> And your hand got cold, right?
Gemma Allen
>> Yeah.>> That's us cooling the GPU.
Gemma Allen
>> That is crazy.>> Exactly. In tests by AMD and our customers, the GPU got cooler by 10 degrees Celsius. Your hand is still cold.
Gemma Allen
>> How long does it take roughly for this to return to the heat it was before I cut the ice?>> Seconds. It should be right down.
Gemma Allen
>> Wow. Yeah, I can feel it.>> There you go.
Gemma Allen
>> That is incredible.>> Yeah, you can take the room's temperature.
Gemma Allen
>> Okay.>> It'll take whatever temperature. If you put this on a cup of coffee and dipped it, you'd burn your hand. And you felt how fast that was.
Gemma Allen
>> Yeah, that's insane.>> Right. So that's the speed removing heat.
Gemma Allen
>> So you guys are competing against copper essentially, right?>> Correct. Yes.
Gemma Allen
>> When I see technology like this and developments like this, my first thought is why did this not happen sooner? Sure. Why did it take so long and for you guys to come together and think, how can we solve for this to realize these innovations?>> Yeah. There's two parts. And all good stories start with beer.
Gemma Allen
>> I love it.>> It was a couple of years ago our PhDs ... So the four co-founders on the non-PhD, I just bought in to lower the average IQ.
Gemma Allen
>> I doubt that.>> But what these guys did was think about how they were doing this in space. So the name Akash is the Sanskrit word for space for the sky. So we solved the heat problem on satellites. And if you think about space, it's really the harshest environment because when you're up there in the atmosphere, it's 2,000 Celsius heat from the sun. You have solar radiation bombarding electronics. And when your satellite rotates, it goes to minus 200. So you're going from plus 2,000, minus 200 and it's rotating. So whatever you put up in space has to handle that. We complain going in and out of a building, and imagine the difference. So if you can solve it in space, you can solve it on anything less than that. And so two and a half years ago when the ChatGPT and all of this started to come out, Felix, Ty, and Dan, the PhDs were saying, "You know what? We did it in space. A GP is only a hundred Celsius. We can do it." And six months later we did it. And since then we've launched a Dell NVIDIA H200. We launched a Supermicro SMC-300 with AMD, a MiTAC AMD350. And we're off and racing now.
Gemma Allen
>> The rest is history, as they say.>> Yeah. So it started in space.
Gemma Allen
>> I love that. I mean, it's crazy though to think how these innovations come to life here on earth, but you guys just had some news funding announcements of late.>> Yeah. So really exciting. When you think about the broader aspect of what's happening with all the capital being deployed, everyone's looking at efficiency. That's capital efficiency, heat efficiency, GPU efficiency, token efficiency and everyone looks at two metrics, the cost per token and then the revenue per watt that you're getting from the data center. We help both. So we got funding from a major Wall Street bank, two to $5 billion at really subsidized rates because we're solving the densification issue at a data center and we're solving increased tokens at the server level. And so we're really excited about having that amount of capital to loan to our clients who buy diamond cooled servers.
Gemma Allen
>> Wow.>> Yeah.
Gemma Allen
>> So talk me through the total addressable market here. Let's break that down a little bit, right?>> Okay.
Gemma Allen
>> Because I'm sure this major bank saw a lot of opportunity. We know there are huge constraints, huge bottlenecks, and it feels as though suddenly every company is a technology, AI led company. A lot has shifted very quickly.>> Yes. Yes. Sneaker companies are AI companies. Yes.
Gemma Allen
>> When you guys thought, okay, we know this technology works. We know this science has huge value in data centers across the world. We're going to now begin thinking about how we can connect that to converted opportunities to bring cashflow in. Where do you begin? Are we talking neoclouds? Are we talking hyperscalers, enterprise players? Where do you start?>> Great question. So from a customer segmentation standpoint, we're focused on neoclouds and enterprise and also working with hyperscalers. The neoclouds have a lot of economic and capital pressure to be really efficient and they're moving really quickly. So we think our product gives them a great solution to get maximum tokens out of their server and getting the most out of the data center they own or are renting from. On the enterprise side, these are companies whose servers have been sitting in data centers that were built 10, five, 20 years ago. They need air cooling options. And so our total addressable market today is on the air cooled data center market and we think there's absolute large capacity to unlock there with enterprise and with neo clouds. Geographically, your question, we're going to start in the United States, but we are a global firm. We did ship our first server over to India and now today we just feel like there's so much opportunity here in the US with existing data centers and then the new ones coming online.
Gemma Allen
>> So you win a new deal, you talk to this company, you understand there is some reverse engineering possibly that needs to hop in here. There are racks that could potentially be optimized. Or you're looking at kind of incremental new opportunities or both. What do these kind of engagements look like? And talk to me through what it would take if you did have a number of racks that you want to basically reverse engineer using Akash systems. How quickly is this deployed? How quickly do you realize value from this?>> Yeah, great practical question. So there's two customer segments we look, two opportunities we look at. One is clients who are looking to upgrade their existing servers and are already looking to buy the newest, latest and greatest. So for that, we call that the upgrade server refresh cycle and they can buy a new diamond cooled server for that. The other one are ones who maybe just bought a server a year or two ago and still want to run the life of that. We can do an upgrade, some call it a retrofit where what we do is we go to their data center, we put our diamond solution there and they turn it right back on and they've now got better performance. So we have two segments, both.
Gemma Allen
>> Wow. Talk me through the ROI. What is the immediate ROI on this?>> It's material. So we had another major Wall Street bank do an ROI four years, cash impact. You're looking at $2 million of cash impact with our servers that generate 50% token increase versus a stock and you can run our servers instead of a 75 Fahrenheit data center environment, like we're sitting in this nice comfortable air, you can turn off the power to air cool data center, let it run to 95 Fahrenheit and now you've unlocked more energy to put more servers in.
Gemma Allen
>> Wow.>> So for us, we think the ROI is at the server. So if you're getting 50%, they normally cost a half million. We just saved you a quarter million dollars just on that. Then I saved you the construction cost of building a new data center, which is 15 million per megawatt. So that's like another 300,000. So that's a half million of just avoiding cost. And when you get 50% more tokens, they're whatever, 30 cents, 80 cents, millions of tokens over four years, that's another million and a half. So it's $2 million of pure incremental benefit. Absolutely material. But again, for companies like hyperscalers or neoclouds, they may put aside that entire ROI because you can get capacity online today. And in this race that they're all in, we think that is in and of itself a great reason to buy this. And then all of that other is incremental ROI.
Gemma Allen
>> We're back at the time in tech where money is almost secondary to the race.>> Yeah. Correct.
Gemma Allen
>> It's true. We hear and say this all the time. How scalable is this?>> Sure.
Gemma Allen
>> So it sounds like it's a fascinating business. Sounds CapEx heavy though from your perspective too, I'm sure, right? How scalable is this midterm? Like over five years, what sort of market traction are you hoping for here?>> Sure. In the midterm, I think we're quite scalable to significant volume. Hundreds of thousands of servers, a couple million of GPUs in the midterm is easy to scale to.
Gemma Allen
>> Wow.>> And so today we're starting with the enterprise and neoclouds and then parallel tracking with the hyperscalers.
Gemma Allen
>> I'm interested because you mentioned enterprise and we hear a lot about enterprise and other shows. Not so much in our factories. I will say that we hear about it, but we don't have a lot of folks actually bringing their case studies, their unique story to the show yet, right? Because I feel like it's still a part of the industry that are trying to understand where they converge. Enterprise meets full end to end management. What are you actually seeing? Like what sorts of workloads are you seeing enterprises truly own end to end? And where is this intermediary vendor ecosystem still fully locked in?>> Sure. We're seeing enterprise coming off of the sidelines. I think you have your natural progression of the innovators, the leaders and some fast followers and I think they're trying to figure out how do they operate in this fast moving environment? How do they get their enterprise agentic AI progress working? Again, we've seen the impact that has on SaaS companies. So I think they're trying to figure out, and then within the stack they're also trying to figure out. I think you're seeing them go in a traditional enterprise approach, which is dual or multi-vendor strategy and I think we'll continue to see that. And as far as having a bias towards open source, I think they're going to keep that lane wide open for them and go after that because they're going to need that going forward.
Gemma Allen
>> Interesting.>> Because how the sector is changing across all the different stack layers, I think enterprise, they're starting to just keep that open.
Gemma Allen
>> When we think about AI factories, we hear a lot about supply chain constraints, right? From every angle, everything, right? Due to engagements between the socioeconomic, geopolitical, and then here on the ground, right?>> Sure.
Gemma Allen
>> What does your own supply chain model look like? I assume there is some sort of global footprint here.>> Yes.
Gemma Allen
>> And in terms of scaling that out, how do you see that trajectory going? You said you're very US focused right now, but we all know why tech went global, right? It was for costs.
Gemma Allen
>> Everyone needs it. Yes, that's right.
Gemma Allen
>> So what's the reality here?
Gemma Allen
>> For us, we've got a great diversified supply chain. It's global. We have Asia, we have Europe and we have the United States and part of our capital deployment is to also build our own reactors to create the diamonds here in the United States as well.
Gemma Allen
>> Wow.
Gemma Allen
>> And we have significant support for that as well.
Gemma Allen
>> You guys started as academics, what a great idea.>> Yeah.
Gemma Allen
>> What's the R&D like here? How intensive, regular? And what does this R&D relationship look like with companies like NVIDIA and AMD and Broadcom? How connected are these conversations?
Gemma Allen
>> I can't get into specifics, but I can assure you that we are talking to several chip companies because we are solving the number one problem that they have with the number one thermally conductive item and the R&D process is intense, but again, we have that unique advantage of that we've learned to do it at 2000 Celsius with solar radiation and that amount of heat we continue to innovate and we innovate on every GPU, whether it's a Blackwell, a Vera Rubin, whether it's the Helios going to the future lines at AMD. So we're constantly in innovation. You could almost think of us as no different than our pharmaceutical company where we have multiple tracks solving multiple problems because each of these have different powers, it's air cooling, liquid cooling, it's a lot of different items that go into it. So that is a lot that we focus on.
Gemma Allen
>> Talk me through what you are seeing and hearing and believing in the air cooling, liquid cooling space. We met at GTC this year, lots of conversations around liquid cooling, what's ahead. I mean, that's been a promise for a long time. It hasn't really met reality if we're being frank.>> Absolutely.
Gemma Allen
>> What do you think?
Gemma Allen
>> I categorically believe that air-cooled data centers, legacy air-cooled data centers is the untapped market to satisfy this unbelievable appetite for power. Because when you look at the installed capacity in the United States, the dozens of gigawatts that we have, the best place to leverage that with the publicly traded pure play data center companies, the Equinix, the Digital Realtys and others, they have installed capacity. Most of them cannot handle liquid cooling that they're building in the future. So what do you do with that existing data center? You have to learn or find ways to operate it to squeeze more power and dedicate it to the servers. And I think that you were talking about the socioeconomic. The more and more communities decline the permits for these liquid cooling data centers-
Gemma Allen
>> They're happening.>> Yeah. That slope of the online capacity coming online, it's going to go this way, but the demand is going to keep going up. And that gap, they're going to have to come back to those legacy air-cooled data centers. And for us, there is massive opportunities in that and that was just a domestic bias. If I think of Europe where the old European cities and the data centers that have been built, they don't have space for liquid, so they have to do their best with air cooled. So we think we'll see a big opportunity there.
Gemma Allen
>> We're seeing a lot in Europe in the data center space in those much colder territories, right?>> Yeah.
Gemma Allen
>> In Finland and close to the border of Russia.>> Yes. Iceland.
Gemma Allen
>> Yeah. Again, which is very interesting. Ireland at one point had interesting data center model because of the rain, right? So a lot is changing. But again, this is all essentially about economics. It's about maximum output from input, right?>> Yes.
Gemma Allen
>> What are your thoughts? What else do you see? Are there any kind of longer tail opportunities that we haven't quite uncovered yet, do you think?
Gemma Allen
>> I think beyond the air cool data center, I think the longer tail will start to be how the business model changes for pure play companies in the neocloud space or if you're just selling GPUs, I think you're going to see a switch from GPU by the hour to token based output models because you have to go revenue and when you're borrowing this kind of money to get efficiency, you're going to have to be at the GPU level at the data center level, then at the GPU level, the rack level, the data center, the VLLMs, the models. That was going to take time, but I think that's where the industry has to go to because as we've seen with the hyperscalers, the capital deployment is really important because they went from cash that they had, cash on the balance sheet, now they're moving to debt. And as we all know, if you want the canary in the gold mine, look at the credit and the debt covenants and the expectations, it's going to have to be on tokens for cost and the revenue per tokens. So I think the long tail is us watching the industry move from GP by the hour, which is like an occupancy based model versus revenue per token or revenue per watt because the WAT encompasses everything and when you're financing, you're financing the WAT, so you know the measure has to be the revenue. I think that's what we're going to see.
Gemma Allen
>> Wow. A fascinating theory.>> Yes.
Gemma Allen
>> So for me, last question, what's ahead? I mean, you guys have an interesting couple of months even since we met in San Jose. What does the next year look like for you and the team?
Gemma Allen
>> Yeah. The next year looks like we're doing a series C round. We've got great interest and success with what we're doing. We'll be announcing several new liquid cooled products as well. And in July we'll be at the AMD AAI event with our showcase product announcing the new 1,400 watt server that we're coming out with.
Gemma Allen
>> And I believe we might be too, so I look forward to that.>> Oh, we should do this again. Yes.
Gemma Allen
>> Pamit Surana, thank you so much for coming on theCUBE..
Gemma Allen
>> Gemma, thank you so much.
Gemma Allen
>> I'm Gemma Allen here at theCUBE Studio at the NYSC. This is AI Factories, one of our programs with NYSC Wired. Thanks for watching.
Gemma Allen