Nobody knows what a used GPU cluster is worth
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Nobody knows what a used GPU cluster is worth
Meg McNulty<br>May 05, 2026
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If xAI defaults on its debt, Apollo Global Management ends up in the GPU rental business. That is in the contract, signed in June 2025, on a five billion dollar debt facility arranged by Morgan Stanley. The lenders have the right to take over Colossus, the company’s 200,000 GPU cluster outside Memphis, and rent it to other AI companies until the loan is repaid.<br>The interesting question is whether Apollo, or Diameter Capital Partners, or any of the other lenders now financing the AI buildout this way, would want to exercise that right.<br>The hard question is what they would actually be holding if they did.<br>A GPU cluster bears little resemblance to a building. Its value at any given moment depends on how it has been provisioned, how it is currently performing, and whether the team that knows its quirks is still there. All of that sits off the lender's balance sheet, beyond the reach of anyone they can call.<br>This is one of the center problems of the AI infrastructure boom. I cannot determine why no one is talking about it. Tens of billions of dollars in debt is now collateralized by chips whose value depends on operational state, and the operational state is invisible to the people pricing the debt.<br>This week’s CipherTalk is about what happens to a specific kind of debt when the collateral itself can walk out the door with the operations team.<br>CipherTalk is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
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The cluster does not run itself
At the scale these GPU clusters operate, hardware and systems break constantly. Keeping them productive is a craft.<br>Modern data center GPUs fail at roughly 9% annually. The number traces to Meta’s Llama 3 technical report, which documented 419 unforeseen disruptions across 16,384 H100s over 54 days of training, of which 148 were GPU failures and 72 were HBM3 memory failures. At 200,000 GPUs, that annualized rate works out to approximately 50 GPU failures every day. At xAI’s stated million-GPU target, Epoch AI projects a failure roughly every three minutes. These are not catastrophic events. They are the steady state.<br>The failure modes that matter for a credit person are the ones that do not look like failures. Silent data corruption (SDC) is the most expensive, where a faulty GPU produces wrong answers without crashing anything, which means a multi-day training run can complete normally and the resulting model weights are quietly poisoned. Cascading failures are the second category, where one bad GPU crashes a training job spread across thousands of others, costing days of compute. Then there are the routine ones: thermal throttle, ECC memory errors, NVLink flap, GPUs falling off the bus.<br>NVIDIA built NVSentinel because traditional monitoring detects these problems but rarely fixes them. Crusoe built AutoClusters because queue wait time is the largest controllable variable in cluster goodput. Without these tools, remediation timelines run hours to days.<br>The job of an operations team is to keep all of this in steady state. They know which racks run hot in summer, which cooling loops have been flaky since the last firmware update, which jobs to re-route when a node degrades but has not failed yet. None of that knowledge is written down. It lives in the team.<br>This is the asset that serves as collateral for tens of billions of dollars in debt and counting.
© 2026 Cosmic Labs<br>Share<br>How the chips became the collateral
In the last eighteen months, AI infrastructure went from being financed by corporate debt, to being financed by the chips themselves .<br>The xAI Colossus 2 SPV is the cleanest example. The structure is roughly $7.5 billion in equity, with up to $2 billion of that contributed by NVIDIA itself, and $12.5 billion in debt. The special purpose vehicle (SPV) purchases NVIDIA GPUs and leases them to xAI on a five-year term. Apollo and Diameter sit on the debt tranche. Valor Equity Partners leads the equity. The debt is collateralized by the chips, not by xAI’s broader balance sheet.<br>Look at the pricing: xAI’s $5B round was priced at up to 12.5%. CoreWeave's GPU-backed deals priced at roughly 8.5% above the benchmark rate, before terms tightened as lenders got more comfortable with the structure.<br>If we assume here these are not unsophisticated lenders, then we have to assume they are charging what they think the risk costs. The premium is then, the price of guessing.<br>The scope is wider than one company. CoreWeave alone holds $18.8 billion in GPU-collateralized debt across multiple SPVs. FluidStack’s $50 billion deal with Anthropic uses a different wrapper, with Google providing a backstop on the lease payments, but the underlying logic is the same.<br>Every neocloud and most major AI labs are now financed this way.
What real collateral...