Global Banks Rush Into Asian GPU Financing as $3.8 Billion in Deals Close
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Six global banks — Citi, JPMorgan, Barclays, Deutsche Bank, Santander and SMBC — are entering Asian GPU lending, with roughly $3.8 billion already committed across three deals; the shift expands AI-infrastructure funding from private credit into mainstream banking, raising the ceiling on deal size.
Who is lending, and where is the money going?
Citi, JPMorgan, Barclays, Deutsche Bank, Santander and Sumitomo Mitsui Banking Corporation are all evaluating GPU-linked loans.
The $3.8 billion deployed so far spans three deals: Zankore's $3.1 billion facility in Indonesia (Citi as sole debt adviser), PaleBlueDot AI's $255 million credit facility (JPMorgan as placement agent), and GMI Cloud's $300 million loan under negotiation for chip procurement at a Thailand data center.
This means → GPU financing is no longer a private-credit niche; mainstream banks can underwrite single deals at the multi-billion-dollar scale.
Why are Asian local banks following in?
Singapore's United Overseas Bank co-underwrote Zankore's $3.1 billion loan alongside four other banks and is now leading the next funding round.
Zankore chairman Vikram Sinha said the company plans to expand AI data-center capacity tenfold to 1 gigawatt and needs ongoing financing; working with banks is "the right way."
In plain terms = the global banks set the template; local Asian banks saw the structure work and started leading deals themselves.
What will this money ultimately buy?
PwC estimates Asian data-center spending could reach $8.2 trillion by 2050, with the vast majority going to GPUs and servers.
Hundreds of data centers are under construction across Asia, and their builders are simultaneously raising capital for chip procurement.
This reflects a structural shift: GPU financing is becoming a long-term, high-volume market — not a one-off arrangement.
Chips as collateral — what is the biggest risk?
Eric Tan, banking partner at Hogan Lovells, noted that GPU financing exposes lenders to "rapid depreciation, technological obsolescence, and rental-income volatility."
In plain terms = chips are not real estate; a GPU worth $100 million today may be worth half that in two years — making collateral valuation inherently uncertain.
Ares Management CEO Mike Arougheti was blunt: "No one has been able to explain to me what the depreciation curve for this technology looks like." Returns on such loans typically run only about 100 to 200 basis points above other AI-infrastructure debt.
How are the loans structured, and who backstops them?
Most Asian GPU loans follow the structure CoreWeave pioneered — a U.S. GPU cloud provider that first securitized compute revenue into bankable cash flow: loans are repaid from data-center compute sales, with customer contracts and the chips themselves pledged as collateral.
For lenders, customer creditworthiness and contract duration are the most critical underwriting dimensions.
Nvidia-backed deals get approved most easily. In both the GMI Cloud Taiwan facility (syndicated across 14 banks) and the Zankore loan, Nvidia agreed to purchase all unsold compute capacity, backstopping the deal if customer contracts fall through. This means → Nvidia effectively acts as guarantor of last resort, sharply reducing credit risk for the banks.
Can this market scale beyond pilot stage?
With banks now involved, borrowers face stricter proof-of-revenue requirements and more conservative underwriting standards and debt-service reserves.
Eric Tan expects "deal structures to tighten, sovereign support to step in, and deal templates to evolve."
In plain terms = this is still pilot stage — whether banks can build a standardized lending framework while the chip depreciation curve remains unclear will determine how large this market can actually become.
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