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GPU-hour Futures Several financial exchanges have developed compute futures. They’re due to start trading in November, following regulatory approval from CFTC who sought comment on compute derivatives in August. Compute is the processing power and hardware infrastructure that machines need to train and run AI models. Much of the discussion around compute markets is about the capacity supplied by GPUs.  You do not have to own these chips. You can rent access to GPU capacity from cloud providers and specialist infrastructure companies. That makes it possible to put a price on the use of a particular type of processor, commonly expressed as the cost of renting one GPU for one hour.  Interestingly GPU-hours and not tokens are being securitized. Token derivatives might come later, but for now the derivative market is being built upstream of the models, and downstream of the chips. This sets the first financial commodification point in the AI tech stack. HOWEVER, not all GPU-hours are equal. The price can vary depending on the type and configuration of the GPU, its location, and the terms under which capacity is rented. That variability is one of the central challenges in creating a derivatives market for compute.  The idea is to create a futures contract whose value is tied to the cost of renting computing capacity. This can be a synthetic GPU-hour that represents a composite basket of chips, or it could be a contract for specific chip models. Contracts have already been announced by CME Group and Silicon Data. CME plans to list futures based on Silicon Data's H100 and B200 Rental Indices, which measure the hourly rental cost of two models of Nvidia GPUs. Each contract represents about a month of continuous capacity for a single GPU. ICE is taking a different approach, working with two partners to list two types of compute futures.  One set of contracts will reference Ornn's Compute Price Index, which tracks live-traded spot prices for GPU compute across major hardware types. The other set is based on the COIL index developed by NATIVX, which takes a different approach by normalising compute prices for energy costs, helping make prices from locations with different power costs more comparable.  The most recent entrant is Nodal Exchange, which announced a partnership with Compute Desk, a provider of GPU rental price indexes, to launch its own set of compute futures. Although Nodal is smaller than CME and ICE, it has more than half of the open interest in the US electricity futures market, and that gives users a way to capture correlations between power and compute. Why is this happening now? The expansion of AI has made computing capacity a strategically important operating input. Companies developing AI models need access to GPUs, while cloud providers, specialist "neoclouds" and data centre businesses are investing in the infrastructure supplying that capacity.  Goldman estimates global AI investment will exceed $1 trillion in 2026 while McKinsey project $6.7 trillion in capital spending on data centres required worldwide by 2030. McKinsey breaks this down as $5.2 trillion for AI workloads and $1.5 trillion for traditional IT.  Who will buy and sell these futures? An AI developer expecting to rent large amounts of GPU capacity in future could take a futures position that gains value if the relevant compute benchmark rises, helping to offset a higher rental bill.  A provider investing in GPUs faces the opposite risk: it has invested in hardware whose earning power depends partly on future rental rates. It could use futures to reduce its exposure to falling prices.   This creates a need for hedging on both sides of the market, although neither would necessarily get a perfect hedge.  This can dramatically lower the existential risk faced by all parties and therefore dramatically reduce the cost of capital. The challenge is fungibility, real prices vary by model and location.