Bitdeer, a company best known for bitcoin mining, has signed customers for most of a new artificial intelligence computing center in Malaysia. The site draws about as much electricity as a small town, and those customers are expected to pay more than $1.7 billion over five years.
The surprise is the rate. For the same amount of electricity, this business brings in roughly nine times what Bitdeer earns simply renting building space and power to a tenant in Norway.
The catch: Bitdeer generally has to supply the costly computer chips itself, and some of its borrowing costs 13% a year.
Bitdeer AI, the AI cloud unit of Nasdaq-listed Bitdeer Technologies Group (BTDR), announced off-take commitments covering more than 70% of its 21.7-megawatt (MW) A201 data center in Malaysia, with more than $1.7 billion in expected revenue over five years, Investing.com and MarketScreener reported. An off-take commitment is a customer’s contractual promise to buy a set amount of capacity over a fixed term.
The deal follows a 6-K filed September 29, in which Bitdeer told US regulators that Bitdeer AI had already sold out the roughly 9.5MW A102 site in Malaysia in August on five-year commitments, and that its Norwegian subsidiary had signed a separate colocation lease worth about $4.7 billion over 16 years.
Executive Summary
On its face this is a contract announcement: roughly 15MW of AI computing capacity in Malaysia, sold forward for five years. The more useful number is what it implies per megawatt. Spread evenly, $1.7 billion over five years on about 15MW comes to roughly $22 million of revenue per megawatt per year.
Bitdeer’s own filings provide an unusually clean comparison. Its Tydal, Norway colocation deal, disclosed in the same 6-K, is expected to earn an average of $2.4 million per IT megawatt per year. The same company, selling power-backed capacity two different ways, is booking roughly nine times more revenue per megawatt when it sells finished AI compute rather than space, power and cooling.
That gap is the story. It shows where the revenue in AI infrastructure sits for a company with power but no hyperscale customers of its own, and it also shows the trade-off: the higher top line comes with GPU purchases, shorter terms and far less of the landlord-style margin Bitdeer projects in Norway.
The Math: Roughly $22 Million per Megawatt-Year
Start with the disclosed figures. More than 70% of 21.7MW is at least 15.2MW. More than $1.7 billion over five years is at least $340 million a year. Dividing one by the other gives roughly $22 million per contracted megawatt per year. Because both figures are floors (more than 70%, more than $1.7 billion), the true rate could move modestly in either direction, but the order of magnitude holds.
Now set that beside Tydal. In its 6-K, Bitdeer said the Norway colocation lease runs at an average of about $202 per kilowatt per month, or $2.4 million per IT megawatt per year, with electricity reimbursed separately by the tenant. Colocation means renting out a powered, cooled building; the tenant brings its own servers. AI cloud means renting out the computing itself, usually by the GPU-hour or under reserved contracts, with the provider owning the servers. Comparing $22 million with $2.4 million, Malaysia generates roughly nine times the revenue per megawatt.
Two caveats keep this honest. Bitdeer has not said whether 21.7MW refers to IT load (power delivered to servers) or gross site capacity, and AI cloud pricing typically bundles electricity into the service. Neither caveat closes a ninefold gap. The bulk of the difference is the hardware and software layer sitting on top of the power.
Revenue Density Is Not Margin
The operational conclusion runs the other way from the headline number. At Tydal, Bitdeer estimates a net operating income margin of about 90%, because a landlord’s main costs are the building and its upkeep. At A201, Bitdeer AI must supply the accelerators, networking and storage that generate the $22 million, and that equipment is where most of the capital in an AI cloud goes. A five-year commitment tries to match contract length to the working life of that hardware, so the question for margins is how much of each megawatt’s revenue goes toward paying off the servers before the contract ends.
That makes financing the real constraint on how far this model scales. Bitdeer’s interim financial statements show $373.5 million of principal on its asset-collateralized loans from BIT Group due within twelve months of June 30, 2026, at an effective interest rate of 13.0% for the first half of the year, plus $375 million of 5.00% convertible notes issued in February. Buying GPUs for revenue this dense can pay back quickly, but only if the contracted customers keep paying for the full term.
Two Business Models, One Power Portfolio
Bitdeer is now running both models side by side. In Norway, it is a landlord: 121 IT MW leased to one tenant for 16 years, backed by about $1.3 billion in letters of credit, a form of bank guarantee. In Malaysia, it is a cloud operator: small sites of 9.5MW and 21.7MW sold on five-year terms. The first model trades revenue per megawatt for duration and credit quality; the second trades duration for density.
Against a total portfolio the company puts at roughly 3.0 gigawatts, including 742MW of existing and pipeline capacity in Milam County, Texas, the Malaysian sites are a small slice by power. They matter as a price signal. For a former bitcoin miner deciding how to use each additional megawatt, the choice is between a long, low-risk lease and a short, capital-heavy compute business earning several times more. Bitdeer’s filings suggest it intends to keep doing both.
Background
Bitdeer Technologies Group is a Cayman Islands company listed on the Nasdaq under the symbol BTDR. Its business spans bitcoin self-mining, cloud hash rate, mining-rig sales, hosting and, more recently, AI cloud services, all of which appear as separate revenue lines in its interim financial statements. Like several large bitcoin miners, it controls substantial power capacity, which it now says totals about 3.0 gigawatts across sites in the US, Norway, Bhutan, Ethiopia, Malaysia and elsewhere.
AI workloads need that same scarce resource, grid power, which is why miners have been repurposing capacity for AI. They generally do so in one of two ways: leasing powered buildings to AI tenants as colocation landlords, or buying GPUs and selling compute directly. Bitdeer is doing both, with a large 16-year lease in Norway and smaller five-year AI cloud contracts in Malaysia.
Two companies that rent out powerful computers for artificial intelligence recently borrowed money, and one is paying far more for it.
CoreWeave took a $2.6 billion loan that costs 5.5 percentage points above a standard benchmark interest rate. Its rival Nebius borrowed $775 million at just 2.5 points above that same benchmark.
The difference comes down to what backs each loan. Nebius tied its loan to one financially strong customer’s contract, while CoreWeave’s covers many customers. If demand for artificial intelligence cools, cheaper borrowing could become a real advantage.
Dealroom reported that CoreWeave closed a $2.6 billion delayed-draw term loan in August, priced at Term SOFR plus 5.50% and maturing in September 2031, with $1.2 billion drawn so far. That margin is three percentage points above the SOFR plus 2.50% Nebius Group secured on a $775 million facility in July. Nebius has described that facility as backed by deployed GPU infrastructure and cash flows from an investment-grade customer.
Separately, Nebius closed $5.75 billion of convertible notes in August. In a Form 6-K filed September 8, it announced that Palantir had named it a preferred sovereign AI infrastructure partner.
Executive Summary
Two of the best-known neoclouds, the specialist providers that rent GPU computing for artificial intelligence, have borrowed against their buildouts within weeks of each other on very different terms. CoreWeave’s $2.6 billion facility carries a 5.50% margin over the Secured Overnight Financing Rate (SOFR), the US benchmark for floating-rate loans. Nebius’s smaller $775 million facility carries 2.50%.
The gap does not show lenders favoring one company’s AI prospects over the other’s. It shows lenders pricing the specific collateral and customer contracts behind each loan. Nebius’s loan sits on a single contracted deployment with an investment-grade customer, meaning one with a strong credit rating. CoreWeave’s loan finances committed deployments across a broader customer pool and gives the company more flexibility, and it pays more for that flexibility.
This matters beyond the two companies. AI infrastructure is being built largely with borrowed money. When credit markets start pricing each buildout on its own contracts and collateral, the cost of capital becomes a competitive variable, and it could separate winners from strugglers if GPU rental prices or utilisation weaken.
Same Industry, Very Different Price of Money
Floating-rate corporate loans charge a benchmark rate plus a fixed margin, or spread. The benchmark here is SOFR, which tracks the cost of overnight borrowing secured by US Treasuries. Because both CoreWeave and Nebius pay SOFR, the spread is the part that reflects how lenders view the risk. CoreWeave’s 5.50% is 2.2 times Nebius’s 2.50%, a gap of three percentage points. Describing it as ‘triple’ is loose shorthand. Once the shared benchmark is added back, the difference in all-in borrowing cost is proportionally smaller, though still substantial.
The structure also matters. A delayed-draw term loan lets the borrower tap committed funds in stages as spending arrives, which suits GPU deployments that come online in phases. CoreWeave has drawn $1.2 billion of its $2.6 billion. As simple arithmetic before fees, three extra percentage points on the drawn balance comes to roughly $36 million a year. On the full facility it would be roughly $78 million a year.
Collateral and Contracts, Not Confidence, Set the Spread
Nebius’s facility resembles project finance. It is secured by hardware already deployed and by cash flows from an investment-grade customer, and Nebius said it covers more than 100% of that deployment’s capital expenditure. Lenders are underwriting a known asset with a known payer, which is why the margin is low. The model works best if Nebius can repeat it, and the company points to more than $40 billion of additional customer commitments as the pipeline for doing so.
CoreWeave’s loan spreads risk across a broader set of committed deployments and runs to September 2031. That breadth gives CoreWeave room to serve shorter contracts and higher-margin enterprise work that a single-customer structure would not accommodate. The trade-off is a higher hurdle rate, the minimum return each financed GPU must earn before it adds value for shareholders. Neither structure is inherently better. One buys cheap money through narrow contracts, and the other pays for flexibility.
Capital Hunger on Both Sides of the Comparison
Neither company is borrowing from a position of surplus. Nebius’s $5.75 billion convertible note sale in August dwarfs its $775 million facility. Convertible notes are debt that investors can later swap for shares, so they carry potential dilution. Nebius is also adding demand-side relationships. Its September 8 filing describes Palantir naming Nebius its preferred sovereign AI infrastructure partner and the two working to deploy new capacity, including modular data centers at sites where power is already available. That filing disclosed no financial terms, contracted capacity or timeline.
Investor positioning is mixed. CoreWeave’s hedge fund holder count rose to 71 in the second quarter of 2026 from 63, even as Magnetar Capital trimmed its stake 24% to 52,062,927 shares. Nebius’s count rose to 86 from 60, while Orbis Investment Management cut 22% to 6,494,719 shares. On August 31, 17.89% of Nebius’s float was sold short. With billions of convertible debt outstanding, part of that short interest likely comes from convertible holders hedging their positions rather than outright bets against the company.
Why the Pricing Gap Could Matter More If Demand Cools
While AI compute is scarce and rental prices hold, a three-point spread difference is easily absorbed. The risk appears when utilisation or pricing slips, a concern that resurfaced after September 14. Expensive debt then consumes a larger share of revenue, and strong top-line growth can coexist with weak returns to shareholders.
The likely beneficiaries are providers that can attach financing to long contracts with highly rated customers, and the lenders who have learned to tell those deals apart. Providers relying on broader, flexible facilities are not disadvantaged today, but they carry more exposure to a downturn. If lenders keep pricing buildouts deal by deal, a lower cost of capital becomes a durable advantage that is hard for rivals to copy quickly.
Background
CoreWeave and Nebius are leading examples of neoclouds, companies that build and rent out GPU computing capacity for training and running artificial intelligence models. CoreWeave, which listed on Nasdaq in 2025, is US-based. Nebius Group, listed on Nasdaq as NBIS and headquartered in Amsterdam, is led by founder and CEO Arkady Volozh and was formed from the international businesses of the former Yandex N.V.
Both companies need large amounts of capital, because GPUs, power and data center space must be paid for well before customers’ rental payments arrive. That has made debt markets central to the sector, and lenders have increasingly looked at the specific contracts and collateral behind each deal rather than lending against the AI growth story as a whole.
On August 28, 2026, Bullish (NYSE: BLSH), an institutionally focused digital asset platform, announced a $100 million stablecoin-based liquidity facility for USD.AI, a protocol that lends against high-performance computing hardware. Bullish frames the deal as its strategic entry into middle-market AI infrastructure financing; the release was issued under USD.AI’s name.
USD.AI, developed by Permian Labs, uses the capital to extend non-recourse loans secured solely by the GPUs being financed. Bullish also plans to list sUSDai — USD.AI’s yield-bearing token — across multiple trading pairs on Bullish Exchange with a dedicated market-making program, and the two firms are expanding a joint research effort on capital formation for AI capital expenditure.
Executive Summary
The headline number is modest by AI infrastructure standards, but the structure is the story. A $100 million facility denominated in stablecoins — digital tokens designed to hold a fixed value against the dollar — is being deployed as debt against graphics processing units, the chips that train and serve AI models. The borrower’s borrowers are not being asked to pledge their companies. They pledge the hardware.
That matters because the AI buildout has so far been financed overwhelmingly with equity: venture rounds, strategic investments, and public-market raises that dilute founders and existing shareholders. Debt secured by the machines themselves is cheaper on paper and non-dilutive, which is precisely how truck fleets, aircraft, and construction equipment have been financed for decades. The open question is whether GPUs behave like those assets.
The second half of the announcement — listing sUSDai on Bullish Exchange with market-making support — is an attempt to build a secondary market where compute-backed credit can be priced continuously rather than marked by a lender’s internal model. If that works, it is genuinely new market infrastructure. If it does not, the listing is a liquidity venue in search of participants.
Compute Is Being Reclassified From Capex to Collateral
For most of the last three years, buying GPUs has been an equity decision. An operator raised money, bought chips, and hoped utilization arrived before the cash ran out. USD.AI’s pitch inverts that: the chips are income-producing assets that can service their own debt, so they should be financed like assets rather than like ideas. The company describes its loans as non-recourse and secured exclusively by the underlying GPU infrastructure, which means a default is supposed to cost the borrower the hardware and nothing more — the corporate balance sheet stays insulated.
The economics are attractive to the middle of the market: regional cloud providers and specialist AI hosts, often called neoclouds, that have real customer demand but cannot raise hyperscaler-sized equity rounds. Non-dilutive capital lets them add capacity without surrendering ownership. This is the same logic that built the equipment-leasing industry, and Bullish’s Thomas Cowan, its Head of Tokenization, positions the facility as evidence that "credible, well-structured real-world assets belong onchain."
Whether that logic survives contact with GPU economics is the substantive question, and the release does not attempt to answer it. Aircraft hold value for decades and trade in a deep, documented resale market. GPUs face a fast product cadence, and their resale value depends on power availability, hosting contracts, and whether a newer generation has made the previous one uneconomic for frontier work.
The Depreciation Curve Is the Whole Trade
Asset-backed lending works when the collateral’s decline in value is slower than the loan’s repayment schedule. If a borrower stops paying in year two of a three-year facility, the lender needs the recovered hardware to be worth more than the remaining principal. That is the pressure point in every GPU-backed structure, and it is sharpened by the non-recourse feature: a rational borrower whose chips have fallen below the outstanding balance has an economic incentive to hand back the hardware rather than keep paying.
Recovery is also physically awkward in a way that auto lending is not. A repossessed car can be driven to an auction lot. A repossessed GPU cluster sits in someone else’s data center, drawing power under a contract the lender may not control, and its value in the resale market depends on whether it can be redeployed somewhere with megawatts already energized. Lenders in this space typically address that with hosting-agreement step-in rights, utilization covenants, and conservative advance rates — none of which the release discloses.
None of this makes the structure unsound. Equipment finance handles depreciating collateral routinely by lending less than the asset is worth and amortizing quickly. It does mean the interesting terms are the ones not in the announcement: loan-to-value, tenor, and whether the underwriting assumes a functioning secondary market for used accelerators or assumes none at all.
One Firm, Several Roles in the Same Market
Bullish is doing four things here at once. It is the lender providing the facility. It operates the exchange that will list sUSDai. It is arranging the market-making program that supplies liquidity for those pairs. And, per its own description, it is the parent company of CoinDesk, a widely read digital asset news and data provider. The release states plainly that Bullish was an early investor in the protocol before this facility.
This is not unusual in digital asset markets, and vertical integration is often what makes a nascent asset class tradable at all — somebody has to stand up the venue and quote the first prices. But it is worth naming, because the release’s claim that the listings will improve "price discovery" for GPU-backed debt is strongest when prices come from many independent participants and weakest when they come from an affiliated market maker in a thin book. The stated goal, a transparent market for the cost of compute, is a real and valuable one; readers should judge it on the breadth of participation it eventually attracts rather than on the launch announcement.
The credible version of the argument is USD.AI’s own: onchain settlement means loan positions, collateral, and repayments are visible to anyone rather than buried in a private credit fund’s quarterly letter. That transparency is a genuine differentiator from conventional private credit, where mark-to-model valuations have drawn scrutiny across the industry. It is a claim that can be verified over time by watching the chain.
Read the Market-Size Claim Carefully
The release asserts that AI infrastructure financing has become one of the largest sectors in private credit, at a scale that "eclipses legacy debt markets such as auto loans and home equity lines of credit." No figure, source, date, or definition accompanies that statement, and the distinction matters enormously: announced financing commitments, annual originations, and outstanding balances are three very different measures, and auto lending and HELOCs are long-established consumer credit markets with decades of accumulated balances.
The directional point — that debt is arriving in AI infrastructure quickly and at serious size — is well supported by the pattern of deals, including USD.AI’s own prior transactions: a $34 million three-year facility for NexGen Cloud’s GPU deployment in Sweden, and a joint venture with Singapore-based BSQ Capital Partners to finance $300 million of AI compute across Asia-Pacific. Against that pattern, $100 million is a middle-market facility, not a landmark, and the release describes it as exactly that.
For buyers of infrastructure capacity and for investors, the useful takeaway is not the comparison but the trend it gestures at. When an asset class attracts dedicated lenders, tokenized instruments, and exchange listings within a short window, the cost of capital for that asset falls — and so does the barrier to building capacity that may or may not find tenants. Cheaper financing accelerates supply. Supply eventually meets demand, in one direction or the other.
Background
The AI buildout has been financed largely with equity so far — venture rounds, strategic investments, and public raises — because the assets involved were new, the demand curve was unproven, and lenders had no basis for valuing used accelerators. As GPU clusters began generating contracted revenue, a private credit market formed around them, borrowing structures from equipment and asset-based finance: lend against the machine, size the loan below its value, and amortize before the technology turns over.
USD.AI, built by Permian Labs, applies that model with blockchain settlement, making loan positions and collateral visible onchain rather than reported quarterly. Bullish, a New York–listed digital asset platform that operates an institutional exchange and owns CoinDesk, has been an investor in the protocol and is now extending it a balance-sheet facility — part of a broader industry push to bring "real-world assets" onto public ledgers, where the collateral is physical hardware rather than a financial instrument.
