Operator: Hello, everyone. Thank you for joining us, and welcome to the QumulusAI second quarter 2026 earnings call. At this time, all participants are in listen-only mode. After today’s prepared remarks, we will host a question and answer session. If you would like to ask a question, please press star one to raise your hand. To withdraw your question, please press star one again. Today’s call is being recorded. I will now hand the conference over to Zachary Graeve, Head of Investor Relations. Zachary, please go ahead.

Zachary Graeve, Head of Investor Relations, QumulusAI: Good afternoon, and welcome to QumulusAI’s second quarter 2026 earnings call. I’m Zachary Graeve, Head of Investor Relations, and with me today I have Mike Maniscalco, our Chief Executive Officer, and Scott Krosnowski, our Chief Financial Officer. This being our first earnings call as a public company, I’d like to spend a moment on a few items before we begin. Our earnings release crossed the wire this afternoon, and that release, along with supplemental materials, are posted on our investor relations website at investors.qumulusai.com. This call is being webcast live and a replay will be available on that site. I’d like to remind everyone that certain statements made on this call may constitute forward-looking statements. These statements include those about our strategy, capacity, and deployment plans, customer agreements, and future performance.

Please be advised that these forward-looking statements are covered under the safe harbor provisions of the Private Securities Litigation Reform Act of 1995, and that QumulusAI avails itself of the protections of the safe harbor for these statements. Forward-looking statements are based on management’s current expectations and assumptions as of today and are subject to risks and uncertainties that could cause actual results to differ materially from those stated or implied. Those risks are described in our Risk Factors section of the registration statement on Form S-1 as amended, file number 333292514, filed with the Securities and Exchange Commission. Those factors may be updated in our subsequent filings, so please read these reports and future filings that QumulusAI will make with the SEC. QumulusAI disclaims any obligation to update or revise any forward-looking statement except as required by law.

Please note that we may refer to non-GAAP measures in today’s call. Any non-GAAP measure will be paired with the most directly comparable GAAP measure, and reconciliations are included in today’s release. Now with that, I’ll turn the call over to Mike.

Mike Maniscalco, Chief Executive Officer, QumulusAI: Thanks, Zachary, and thanks to all of you for joining us. This is our first call as a public company, so I will start the way I did on the Nasdaq stage in July with an expression of gratitude to our customers, employees, shareholders, board members, and partners. A number of them committed to this company well before there was much evidence to go on. We are thankful for that and excited about the future, which is the reason the rest of this call will focus on executing on the opportunity ahead. Let me tell you how I got here, because it explains a lot about what this company is. My background is software. Computer science by training, technology entrepreneur by trade, and much of that has been up the stack.

When I stepped out of the AI Infrastructure business in January of last year, I went back to what I knew. I started building things, leveraging the power of the emerging agentic coding tools, and I watched those tools get very good, very fast. What I experienced changed my mind. If software was going to become this easy to build, then software was not the constraint anymore. Whatever sat underneath it was. What sits underneath the AI tools are the models and the compute to power them. The heavy AI Infrastructure, power, data centers, and GPUs were the enabler of every good thing I was watching happen. Less than eight months after I left the infrastructure space, I came back, and I came back through Atlanta. I was in town visiting Georgia Tech, my alma mater, where I met the QumulusAI team.

QumulusAI’s own story runs on a parallel track. Two companies, both founded in 2019. One built data center assets and power. The other built blockchain managed services. In December of 2022, they merged into what is now QumulusAI. In April of last year, we acquired The Cloud Minders, which brought GPU-as-a-service as a foundation. Two halves of an answer put together. Through the early buildup of our Bitcoin business, we also ended up holding power in our own right. We have long-term land leases on two sites, one in Oklahoma and one in Texas, totaling roughly 39 megawatts available today. That is in addition to our existing HPC data center lease footprint. We have 8 megawatts of compute completely sold today, with the final GPU deployments actively in progress. Last week, we announced a co-location agreement in Metro Atlanta, our home market, for up to an initial 3.75 megawatts.

That agreement also carries a right of first offer for up to 7 megawatts of additional capacity, which the provider may pursue at the same site and could bring the Atlanta location to 10.75 total megawatts. Looking back, the guidance we issued in July 14, 2026, contemplated 18 megawatts of HPC capacity by year-end. The 8 we had plus 10 more to develop. Atlanta is the first 3.75 of that 10. If you add the 39 megawatts under lease, that is roughly 57 megawatts in our combined footprint going into 2027. That is real opportunity ahead of us. To the quarter. On July 16, we began trading on the Nasdaq Global Market under the ticker QMLS. We did it as a direct listing.

Our registration statement was declared effective on July 14, and we rang the opening bell at the Nasdaq Global Market site on July 29. On July 17, the day after we began trading, we were approved as an NVIDIA Cloud Partner. Two things in the same week, and both are about access, one to the capital markets, the other to the supply chain that the capital buys from. However, neither one is the goal. They are both tools for the same job, turning signed demand into deployed GPUs. One funds it, the other sources it, and we do that faster than most companies in this market. For our customers, speed is everything. It is also why we have built our model around hyper-speed rather than hyper-scale. This was the quarter our model started proving itself. Our GPU fleet went from 952 to 3,088, a 224% increase from Q1.

It is worth highlighting that most of this was compute we had already sold coming online. Revenue more than doubled year-over-year. Additionally, gross margin expanded to 66% from 55.1%. Scott Krosnowski will talk about that later in today’s call. Those three numbers tell one story. AI compute we had already sold came online, and whenever compute comes online, that is when the economics of this business show up. On the commercial side, we signed 21 new direct customer contracts totaling $169.7 million in the second quarter. That brings our total signed contract value to $282.5 million across 40 contracts with a weighted average term of 2.2 years, shorter than what you will see from the largest players, but that is deliberately so. I will come back to why later. Everything we sold this quarter was contracted before the hardware was energized, meaning customers are committing and paying ahead of delivery.

Scott Krosnowski will talk about that more a little later in this call. I also want to say something about that $282.5 million because it is the real number I would watch. It is not revenue. It is signed multi-year demand sitting in front of a company that recognized $6.7 million in the quarter. The distance between those two figures is the entire operating challenge of the business, and it is our job to close it. The only way you close it is by putting compute on the floor at hyper-speed. The customer base has changed too. Direct customer relationships now account for more than 96% of our recurring revenue, up from less than 10% a year ago. We have completed the transition away from dependence on a single marketplace to direct multi-year agreements with customers themselves.

Marketplaces used for what they are generally good at, tailored user experiences and filling short-term gaps between term customers as they roll on and off. Customer contracts are getting bigger. If you look at our recent announcements, the trend is clear. What we are signing now is materially larger than what we signed six months ago. As we secure more power, we expect that trend to continue. Now, I want to take a step back for a minute and talk about why we are built the way we are, because there is a lot of noise in this category. We are not a powered shell developer building sites to lease to the next hyper-scaler. We are not brokering powered land to data center developments, and we are not brokering GPU clusters. We deploy and operate GPU clusters for our customers at scale, and we made a deliberate choice about how.

The traditional way to build AI Infrastructure goes like this, and I think this is important to understand. You do an initial data center design and seek data center development approval for about a year. Then you engineer it, then you finance it. You spend 24 to 36 months in construction. You roll in the GPUs, you spend a few more months commissioning, and 3 to 4 years later, you have delivered a token-generating cluster. Look at the pace that AI is moving, then look at NVIDIA’s chip roadmap. Neither of those paces line up with a 4-year build, so we do not lead with 4-year builds. We look for pockets of power, call it 2 to 50 megawatts, where the power or shell already exists or can be ready for service soon, and we deploy quickly.

Most of that is co-location, and it means we bring capacity online in months, not years. That is hyper-speed. There is a trade in that, and I will name it. It is gigawatt-scale campuses. Gigawatt-scale monolithic builds are important for AI, but they are not for everybody, and we are not chasing those at the moment. We are currently focused on customers who need capacity today, not those planning for 2030. That market is large and is underserved. What we get in exchange is speed, and in this market, speed is a core differentiator. But speed alone does not win. There are five things customers actually buy. We call it our FACTS framework. First, flexibility, whether it is around location, vendors, or architectures, such as bare metal versus Kubernetes or RoCE versus InfiniBand. We provide customers that flexibility. Second is access. As I mentioned, our customers struggle to find capacity.

What we do, we provide access and capacity that is available when a customer needs it, and we do that with a fair and clear price that customers can grow into. That is our cost structure. Next is trust. We are building long-term partnerships through reliability and SLAs or isolation and data privacy. We are here to support our customers’ needs. Lastly, it is speed, and all five matter, but one of them decides most deals right now. Customers just are not opening with price. They are opening with, "When can I be running?" What we see is an industry that is compute constrained top to bottom, from the largest providers down to the startups that cannot get enough compute to keep pace with their customer demand. When supply cannot keep pace with customer demand, speed stops being a feature. It becomes the decision. That is the near-term reason we go after small pockets of power.

It is the fastest way to get a customer running. There is a second thing that comes from building this way. Because we typically deploy into pockets of existing power rather than financing for your builds, we do not need to lock in every megawatt into longer-term 5-year contracts. Most of our contract value is multi-year, but part of the book comes up for renewal each year. GPU prices have been rising, not falling, which means in a market like this, we would rather have some capacity repricing than all of it locked to rates we set years ago, and that is deliberate. But we are also making a longer-term bet on that same footprint. Today, distribution is a means to an end. Over time, we think it becomes the end itself. As workloads shift from training to inference, they get more particular about where they run. For example, budget starts to matter more.

Latency begins to matter. Proximity to the end users starts to matter. So does the security and compliance posture of a specific location. When customers start choosing on those terms, a network of sites in different places stops being an operational complexity and becomes a differentiated product. We will see how quickly that develops. In the meantime, the same footprint is doing exactly what we need it to do today, delivering supply at the highest speed. That is why we built this company inference first. Tokens are the output, and getting tokens to customers faster than anybody else in our business is winning today. Getting them to the right place is the business we are building towards tomorrow. Let me put it together. In the second quarter alone, we more than tripled our GPU fleet. We signed 21 new contracts.

We gained access to the capital markets and a place inside NVIDIA’s partner ecosystem. Our go-to-market motion is working. We activate capacity, sign demand, and deliver the supply. Those are the three dials, and all three moved this quarter. Thank you. Now I will turn it over to Scott to walk you through our financial reports in more detail. Over to you, Scott.

Scott Krosnowski, Chief Financial Officer, QumulusAI: Thanks, Mike. Good afternoon, everyone. I will take revenue first, then walk down the income statement, then finish with cash and the balance sheet. First, revenue. Revenue was $6.7 million in the second quarter, up 118%, compared to $3.1 million from the same period last year. The driver is compute power, which grew to $5.6 million from $1.3 million a year ago, up 328%, or $4.3 million, as GPUs activated primarily on term compute contracts, representing 84% of total revenue in the second quarter, compared to 43% from the same period last year. Our legacy lines moved as expected. Together, mining hosting and cryptocurrency mining were about 16% of revenue this quarter, down from roughly 39% in the first quarter and 57% a year ago. We expect this downward trend to continue as we activate more GPUs. Now margin. Gross profit was $4.5 million, compared to $1.7 million a year ago.

Gross margin expanded to 66.6% from 55.1%. Two things drove the improvement, and both are clear sequentially. Margin moved 29 percentage points from 37.5% in the first quarter, GPU activations grew faster than the associated HPC colocation costs, and we recognized approximately $0.4 million in curtailment credits at our Oklahoma site, credits for returning power to the grid during peak demand periods that reduce power costs. Down to the operating line. Operating loss for the quarter was $7.7 million, compared to $2.2 million a year ago. The loss widened, and I want to be specific about why, because the reason is favorable. Depreciation and amortization were $6.9 million in the quarter against $1.1 million a year ago, and up $4.2 million quarter-over-quarter. That sequential step is the direct consequence of the HPC assets Mike described coming online and driving top-line and margin growth.

Adjusted EBITDA, which excludes interest, taxes, depreciation, and non-cash items, was a loss of $0.8 million, compared to a loss of $0.3 million a year ago. Sequentially, the loss narrowed from $2.8 million in the first quarter. That is a non-GAAP measure. The most directly comparable GAAP measure is net loss, and a full reconciliation is in today’s release. The year-over-year comparison reflects the public company and personal investments we have made. The sequential improvement reflects revenue beginning to scale against them. General and administrative expenses were $4.3 million, up $1.9 million from a year ago, driven by increased headcount, costs associated with becoming a public company, and scaling up infrastructure operations. Sales and marketing expenses were $0.9 million, up from $0.4 million a year ago, driven by increased headcount and costs associated with promoting and marketing our HPC services.

Below the bottom line, net loss was $22.8 million, compared to net income of $12.1 million a year ago. The year-over-year comparison is significantly affected by non-cash accounting in both periods. The prior year result included a $14.5 million non-cash gain related to the remeasurement of our investment in The Cloud Minders upon acquiring the remaining interest. In the current quarter, the largest single item is a $19.2 million non-cash loss associated with the issuance of convertible notes, which did not represent a cash outflow. The charge was partly offset by $6.2 million in non-cash fair value gains on the notes and related option. Net interest expense was $2 million, primarily related to loan draws and lease financing. Turning to cash. Operating cash flow for the first six months was positive at $22.3 million against $0.8 million used a year ago. The driver is customer prepayments.

Deferred revenue rose $30.5 million. On our major multi-year contracts, we see prepayments in the range of 10%-35%, and that money, in some instances, supports the deployment it is attached to. Investing activities used $36.3 million, primarily purchases and deposits of HPC and related equipment. Financing activities provided $42.2 million. Cash and restricted cash ended the period at $39.9 million, up from $11.7 million at year-end. That includes $19.9 million that had been restricted pending our public listing, which became available following the direct listing on July 16th. On the balance sheet, total assets were $215 million, up from $91.7 million on December 31st, driven by finance rate of use assets, property and equipment, and deposits on power equipment.

On the liability side, we carry $55.5 million of convertible notes payable, a $38.7 million additional convertible notes option liability, $18.9 million of protocol loans, and finance lease liabilities of $45.8 million. One note on how the term Compute contract value shows up in our statements. Mike gave you a total signed contract value of $282.5 million. In our financial statements, you will see a related but narrower figure. Remaining performance obligations of $173.1 million as of June 30th. That is the accounting measure. Direct customer Compute contracts only, and only what was signed as of quarter end. Two last points. First, on unit economics. Our most recent Blackwell contracts are generating between $18 million and $20 million of annualized revenue per megawatt. Across our installed base, the blended figure is closer to $16 million per megawatt. The gap is pricing power. Our deployments are currently focused on the Blackwell chips.

The price per GPU hour has been firming across the market, and customers will pay for GPUs and capacity that come online quickly. All three of these factors are working in our favor, and the revenue we earn per megawatt is widening as we scale. Moderator, we are ready to take questions.

Operator: We will now begin the question and answer session. Please limit yourself to one question and one follow-up. If you would like to ask a question, please press star 1 to raise your hand. To withdraw your question, press star 1 again. We ask that you pick up your handset when asking a question to allow for optimum sound quality. If you are muted locally, please remember to unmute your device. Please stand by while we compile the Q&A roster. Your first question comes from the line of Michael Donovan with Compass Point. Your line is open. Please go ahead.

Michael Donovan, Analyst, Compass Point: Thank you, operator. Good afternoon, guys. Congrats on the progress. I thought we could discuss the AI-XP opportunity. How is the economic structured across QumulusAI and site partners, particularly around CapEx contributions and revenue sharing? Can we get a sense of timing for bringing on initial sites of the 25 sites that you indicate in the presentation?

Mike Maniscalco, Chief Executive Officer, QumulusAI: Yeah, thanks, Michael. I appreciate it. I want to keep those somewhat separate because I think that if you reflect back on the statements I made a few minutes ago, where we are seeing a lot of success in deploying chips quickly is into third-party colocation capacity that is available RFS this year. A lot of the effort that our land powered shell team and colo team is doing is out looking for colo capacity that is undergoing, say, renovations or just coming online, that is in the single-digit megawatts that we can move into quickly. That go-to-market motion has been working really well for us. That is where you are seeing a lot of growth. That is where you are seeing our new colo lease announcements recently, because that is the fastest path to get chips online.

That being said, the AI-XP initiative and those sites that we announced are still in progress. That project is making good progress. They’ve been posting pretty good updates online. The collective groups are responsible for bringing the land, the power, the shell to us. And our responsibility at that point is to, once connectivity and power and cooling is ready, we roll in the racks and bring the GPUs online. That’s the role we play in that arrangement. The sites are energizing in phases right now. And really, just to be a little more specific, the first site in Wichita is really the one we’re focused on today. The additional sites are still a little further out in our pipelines.

Michael Donovan, Analyst, Compass Point: That’s helpful. I appreciate it.

Mike Maniscalco, Chief Executive Officer, QumulusAI: Thank you.

Operator: Your next question comes from the line of Brett Knoblauch with AI Infrastructure. Your line is open. Please go ahead.

Brett Knoblauch, Analyst, AI Infrastructure: Hi, guys. Thank you for taking my question, and congrats on the quarter. As we look at the remainder of the year, I am just curious on the power ramp. I think previously you talked about getting up to 16 or 18 megawatts. Is that still the target? Then the $16-$18 million revenue per megawatt figure of, say, a kind of existing base, at what point would you expect that to start to show up or all those contracts to be kind of up and running where we see that on the 8 megawatt figure that you guys disclosed?

Mike Maniscalco, Chief Executive Officer, QumulusAI: Yeah, thanks, Brett. I will answer the first part, and Scott, if you want to round anything out after I am done, please feel free. On that, we look back at the 18 megawatts that we guided to on July 14. We had put out a plan to hit an additional 10 megawatts on top of the 8 megawatts that we already had. So 18 megawatts total, with 8 already leased, and that is actually fully sold and some of that is generating revenue now. We expect all of that 8 megawatts to generate revenue this year, or to begin generating revenue this year. That additional capacity, we just announced the 3.75 that we have added to that capacity, so we are continuing to push really hard on that, and still see a fair amount of smaller scale capacity out in the market that is available RFS this year.

To give you a little more specifics there, our preference is to find, call it 1, 2 megawatts that is available RFS this year, but can expand beyond that. So if it is a 3.75 that has the capability to expand to 10, that is a much better fit for our longer term needs. However, we are also seeing smaller pockets of, say, stranded 1 to 2 megawatts that are still out there that have not been prioritized. So we still see some good options out to hit the 18 that we were looking back at in July. I think you had a second part of the question.

Brett Knoblauch, Analyst, AI Infrastructure: Maybe just on the CapEx front of all the contracts that you have signed so far, I guess how much maybe GPU purchases or CapEx or GPU-related CapEx is left for you guys? Then I will hop back to Q. Thank you.

Mike Maniscalco, Chief Executive Officer, QumulusAI: Okay. I think you asked about the revenue per megawatt as well. Scott, I will take the revenue per, you can jump in on the CapEx piece. We are seeing, I think that the latest economics are coming into the $18 million-$20 million per megawatt on the revenue side of the equation. Scott, do you want to comment on the CapEx side of that?

Scott Krosnowski, Chief Financial Officer, QumulusAI: Sure. All of the CapEx that is scheduled to go into the 8 megawatts has been ordered and financed. As Mike mentioned, we expect all 8 megawatts to be active and revenue producing by the end of the year.

Brett Knoblauch, Analyst, AI Infrastructure: Thank you, guys.

Mike Maniscalco, Chief Executive Officer, QumulusAI: Thank you.

Operator: Your next question comes from the line of Bill Papanastasiou with Chardan. Your line is open. Please go ahead.

Bill Papanastasiou, Analyst, Chardan: Yeah, good evening, gentlemen. Congrats on the progress and the inaugural earnings call as a public company. First one for me, as mentioned in the print that QumulusAI has signed a total of 21 new direct contracts in the quarter. Maybe we could just spend some time speaking about what customer profiles you’re targeting as the company scales. Are you looking for particular enterprise customers, and what goes into that decision?

Mike Maniscalco, Chief Executive Officer, QumulusAI: Yeah, thanks, Bill, and we’re excited to get this first one in front of us and behind us, and so thank you for that. Yeah, the new contracts we’ve announced, that is made up of a total of 8 customers. We have announced a couple of those customers, where the customers have been willing to. But generally speaking, what we’re seeing as far as those types of customers, it’s a combination of things. Some of it is some of our marketplace partners, which we’ve been working with for the past couple of years. Some of it is with the and the way I explain this is they’re the AI inference, GPU model as a service, generative AI as a service type companies that are seeing really strong customer growth. They’ve usually gone out and raised significant capital from brand name venture capitalists.

For argument’s sake, I’ll say they’ve gone out and raised, let’s say, $100 million plus from a16z or Sequoia or the likes of those categories. They’re in a rapid growth phase with a lot of customer demand, and they need the compute to satisfy it. I would say that’s where a lot of the near term growth is coming from. We call those the AI natives. Then in our pipeline, we do have a lot of interest and continue to look at the enterprise segments. However, the enterprise segments tend to make decisions and move a little less quickly than, say, the AI natives. But we are working with a lot of those verticals. I’m happy to talk about some of the verticals. I think we call that out in our investor slides from July, and I don’t think that’s changed too much.

But we continue to focus on the enterprise verticals as well. I would say as we are proving ourselves in the market, as we are bringing more supply and that word’s getting out to market, and as we are increasing our organizational maturity level, the quality of our customers, we also see increasing. That’s another positive note I will highlight.

Bill Papanastasiou, Analyst, Chardan: Great. Appreciate that color. Then, hoping you would be able to provide an update on the GPU financing environment. Have you seen any changes in economics that become more favorable now that you are a public company and you are seeing the shift in your customer profile base?

Mike Maniscalco, Chief Executive Officer, QumulusAI: Yeah, the way I have explained this pretty consistently over the last couple of months is it seems like the capital markets side of the equation is moving quickly to try to keep pace with how quickly the AI space is moving. So we have got our historical ways of funding. Those have been working out really well. As a reminder, those have been a combination of equity and debt. A portion of that debt has come from USDI and the Permian Labs agreement. Some of that has come from traditional lease agreements with companies like Tech Financial Services. So that has been working really well, but we are also seeing new announcements from our peers and from our partners about other creative financing opportunities and options.

Really from our standpoint, it is about aligning the right capital to the right asset, whether it is a data center power agreement or GPU, or other, and then the right customer. Then understanding what the right cost of capital is. Yeah, I would say there are more options now than ever that we are seeing out there, and that is encouraging.

Bill Papanastasiou, Analyst, Chardan: Appreciate that. Then last one from me, apologies if I missed this, but can we just touch on the prior guidance figure of $300 million exiting in the year? Did you mention that that was still in play, and can you speak to the demand funnel from July 14th when that guidance figure was published? Are you seeing the demand funnel kind of grow at the same, faster or slower clip than was previously expected?

Mike Maniscalco, Chief Executive Officer, QumulusAI: Yeah, so we did put out the guidance on July 14th with a $300 million ARR target. We are not reaffirming any of the guidance today, but what I can tell you is we’re not currently demand constrained. We’re not able to satisfy all the customer requests that our sales team and marketing are stirring up and bringing in, and our partners are bringing in. So really the largest constraint to our growth right now is the land powered shell in the near term. That’s why we’ve got a big focus on our land powered shell team on going out and finding that additional power. Specifically in that July guidance was 18 additional megawatts or 18 total megawatts into the year on top of the eight we already had.

A lot of focus is on that both into the end of the year, but also into 2027 and even 2028. I feel really good about the land powered shell team and the team in itself. I think that we’ve done a great job at having capital markets and the land powered shell teams focus on those efforts while the operations team is heads down focusing on execution and delivering the contracts and bringing in the revenue on the contracts we’ve announced. So that’s working really, really well. I just think that’s a good point to highlight.

Bill Papanastasiou, Analyst, Chardan: Great. Thank you for the color.

Operator: Your next question comes from the line of Allen Klee with Maxim Group LLC. Your line is open. Please go ahead.

Allen Klee, Analyst, Maxim Group LLC: Yes. Hi. Could you talk a little about the competitive environment of companies you were going up against for GPU compute?

Mike Maniscalco, Chief Executive Officer, QumulusAI: Yeah, thanks, Allen. Absolutely. I talk a lot about this in a lot of our historical materials and also I think in our recording, that the differentiation is really about the what we are not, right? There are a lot of Neoclouds pursuing the big, big opportunities, hundreds of megawatts, gigawatts right now. Those things are signing out years out. To the biggest companies in the world, that is really important. To the largest frontier model training companies, they need to lock down that capacity for years to come to train and host the largest models. So that is an important part. I cannot emphasize that enough. Those gigawatt scale campuses and those longer-term projects are extremely important to the future of AI and to continue the AI momentum.

However, there is just a lot of near-term demand of customers who are calling and saying, "Hey, we would love compute yesterday if you could get it to us." That is a theme we hear from our sales team, we hear it from our customers, and we hear it from the market. It is not just us. If you listen to our peers’ calls and you listen to hyper-scaler calls, I mean, everybody is saying we are restricted, growth constrained by supply, I think is the exact words that some of them are using right now. So our differentiated approach right now is really, it is a combination of things, and I refer to it as our right to play, right? The right to play in the space is do you have access to the capital? Do you have the land power shell to get the chips into?

Do you have the partnerships and the supply chain to procure those chips? I highlight the NVIDIA Cloud Partner relationship. That’s a key piece of it, but also our OEM relationships with the likes of the Supermicro, Lenovo, Dell, et cetera. Then it’s can you bring those online and operate those effectively, and do you have the team to do it? This is one of my favorite things to say as a former public company CTO, it feels great to have a CTO I can really trust and lean on. You put those pieces together, and I think we check the boxes. Then it’s, what is the additional differentiation that we’re seeing and what’s making us win these contracts? It really is, to me, speed. Speed and fair costs.

We can get it to our customers quickly because we’re not out looking for 150 megawatts at a time, and our customers are open to a few megawatts of compute at a time, if not less. We can move that a lot faster. I think to summarize it’s that sub 50 megawatt thesis where we believe that we can deploy and find pockets of power, shell, roll the GPUs, get them online a lot faster if we are focused on smaller to more moderate pockets of compute rather than large deployments.

Allen Klee, Analyst, Maxim Group LLC: Thank you. One other question. Could you just remind me of when you’re trying to finance GPUs for the debt component, how much does it typically amortize? Is there any changes in rates with the current environment or that you’re seeing now versus maybe six months ago?

Mike Maniscalco, Chief Executive Officer, QumulusAI: Scott, I might defer that to you. I think you can probably speak the clearest and cleanest right now.

Scott Krosnowski, Chief Financial Officer, QumulusAI: Yeah. Most of our financing arrangements are 3 years. We do have a few that are longer, but most of those are financed over 3 years, which lines up typically with some of our recent longer-term compute contracts. In terms of our partners, we have some great partners that we have already done some financing relationships with, and we expect to continue to grow with them. As we get bigger, as we deploy more, we expect obviously the cost of capital to go down. But that is really what we are focused on is finding the lowest cost of capital in each case.

Allen Klee, Analyst, Maxim Group LLC: Thank you.

Mike Maniscalco, Chief Executive Officer, QumulusAI: Thanks, Allen Klee.

Operator: This concludes today’s call. Thank you for-