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Banks are moving into Asian loans backed by graphics processing units (GPUs) and data-center computing revenue, a market previously funded mainly by private-credit firms. The lending is still nascent: repayments depend on demand for computing capacity, while chip resale values and useful lives are difficult to predict. The often-cited US$8.2 trillion figure is a PricewaterhouseCoopers estimate of potential Asian data-center spending by 2050—not the amount already lent to buy AI chips.
How a GPU-backed loan works
A data-center or cloud-computing operator borrows to buy GPUs, then sells computing capacity from those machines. Revenue from customers is expected to repay the borrowing. Depending on the deal, lenders may have security over the GPUs and customer contracts, tying repayment to both the equipment and the business that uses it.
That structure makes the loan’s value depend on more than the original purchase price of the chips. Lenders need to assess whether the operator can keep the GPUs in use, whether customers will pay for the capacity, and what the equipment could be worth if the borrower defaults or demand weakens.
What the reported Asian deals show
Bloomberg reporting published by The Business Times on 6 October 2026 described roughly US$3.8 billion in GPU loans involving GMI Cloud, Zankore and PaleBlueDot AI. It reported the following roles; they are not all equivalent, and an advisory or placement role should not be confused with lending the full amount.
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| Company or financing | Reported amount or role | What the report says |
|---|---|---|
| Zankore | US$3.1 billion borrowing in Indonesia | Citigroup advised Zankore; Singapore’s UOB jointly underwrote the loan with four other banks. |
| PaleBlueDot AI | Amount not stated by The Business Times | JPMorgan Chase acted as placement agent for the facility. |
| GMI Cloud, Zankore and PaleBlueDot AI | Roughly US$3.8 billion combined | The figure covers GPU loans involving the three companies in recent months; it is not an estimate of all Asian AI infrastructure lending. |
The report also said Citigroup, JPMorgan Chase, Barclays, Deutsche Bank, Banco Santander and Sumitomo Mitsui Banking Corp were evaluating GPU-linked loans at the time. Those evaluations are reported discussions, not evidence of completed commitments.
A demand backstop in two reported transactions
The Business Times report said Nvidia agreed in two September 2026 transactions involving GMI Cloud and Zankore to buy computing capacity that went unsold. Such an arrangement can support demand if customer contracts fall through. The report also said the operators would charge buyers more than Nvidia’s promised rate and share revenue with Nvidia. These are reported transaction terms, not publicly filed loan documents.
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What the US$8.2 trillion figure means
The Business Times attributed the figure to PricewaterhouseCoopers: Asia’s data-center spending could reach US$8.2 trillion by 2050, with much of that expenditure going to hardware such as GPUs and servers. It is a long-range estimate of potential data-center spending, not observed investment, a current loan balance, or spending on AI chips alone. The underlying PwC publication is not identified in the reporting, so the figure should be treated as an attributed estimate rather than a precise forecast of lending.
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Why lenders see risk in GPU loans
Collateral can lose value quickly
GPU generations change, and a chip that is productive today may be less attractive to customers or harder to resell later. Eric Tan, a banking and finance partner at Hogan Lovells Cadwalader, identified rapid depreciation, technological obsolescence and volatile rental rates as lender exposures. Mike Arougheti, who runs Ares Management, said: “No one could really articulate, at least to me, what the depreciation curve looks like for that technology.” The reviewed reporting does not establish a long-term depreciation curve for the named loans.
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Revenue depends on customers and utilization
Operators must sell enough computing capacity, at rates that support debt service, to make scheduled payments. Customer concentration, contract duration and the reliability of buyers therefore matter alongside the number of GPUs pledged. A purchase commitment for unsold capacity can reduce demand risk in a particular deal, but it does not eliminate operating, counterparty or repayment risk.
Export controls and geopolitics can affect the assets
For cross-border transactions, lenders may need to establish where chips can be deployed and who ultimately uses the computing capacity. The Business Times report described due diligence concerns involving export restrictions, end users and geopolitical tensions. A change in rules or access to a market could affect how equipment is used, the customers it can serve, or its resale prospects.
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Conservative terms may be necessary
The report said traditional lenders are likely to demand more conservative underwriting and larger debt-service reserves as they become a more important source of funding. In practice, scrutiny should cover the borrower’s ability to withstand weaker utilization or lower rental prices, not just an optimistic demand forecast. Tan said that as deals grow and borrowers seek more competitive pricing, banks will become increasingly important. Arougheti’s caution was: “You have to be leading with your risk appetite and not your appetite to deploy.”
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Questions a bank needs answered before lending
The relevant diligence varies by transaction; the reporting does not provide complete term sheets for the named facilities. The core questions follow from the loan’s repayment and collateral structure:
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- Customers: Who has contracted for capacity, how creditworthy are they, and how concentrated is the borrower’s revenue?
- Contracts: How long do customer commitments last, and do their terms align with the loan’s repayment schedule?
- Utilization and pricing: What utilization and rental rates are needed to cover debt service, and how sensitive is repayment to a fall in either?
- Collateral: What are the GPUs worth today, how quickly could they be sold, and what might reduce their value before the loan matures?
- Backstops: Is there a sponsor, equipment vendor or other counterparty committed to buy unused capacity or support residual value? What are the limits and conditions of that commitment?
- Resilience: How much debt-service reserve is available, and can the borrower keep paying through a period of weaker demand?
- Legal and geopolitical exposure: Are the equipment’s location, permitted uses, end users and relevant export restrictions understood?
How this fits into wider AI financing
GPU loans are one part of a broader financing buildout, not a proxy for the entire market. A Federal Reserve Bank of Kansas City bulletin from October 2026 described AI-related borrowing through bonds, private credit, commercial real estate and asset-backed securities. It reported US$330 billion in investment-grade bond issuance by AI firms through the second quarter of 2026—ten times all of 2023 issuance—and said certain special-purpose vehicles financed 90 percent of assets with debt. Those figures concern broader AI financing and selected structures, not the named Asian GPU loans.
The Reserve Bank of Australia’s October 2026 review distinguished large listed AI companies, which may have other revenue sources, from some data-center and “neocloud” providers with concentrated customers or weaker balance sheets. It also noted that guarantees and special-purpose vehicles can enable projects while creating contingent exposures and less transparent connections among firms.
A February 2026 analysis by the Federal Reserve Bank of Chicago estimated that direct outstanding exposure to AI-adjacent industries averaged around 0.8 percent of total assets for US banks. That US-wide estimate is not an Asian exposure measure and does not describe the named GPU facilities. The analysis warned that stress may still travel through private-credit firms and interconnected industries, so direct bank holdings alone may not capture every channel of risk.
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The reports describe a young lending market and transactions whose terms are largely private. They do not establish realized losses, default rates or a verified long-run GPU depreciation curve for the named borrowers. Nor does the reported pipeline of banks evaluating deals show how much financing was ultimately completed. The practical test will be whether customer revenue, equipment value and contractual backstops hold up over the life of the loans.
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