GPU financing has become a core pain point in AI infrastructure. AI startups report that instead of reserving one year of compute, they're increasingly asked to reserve three while putting down 30-40% upfront. Compute providers aren't imposing these onerous terms arbitrarily: demand is outstripping supply, and building a data center is capital-intensive, with financing itself expensive to obtain. Part of why: financial markets still treat GPUs as fast-depreciating assets. Standard market practice still leans on 3-year straight-line schedules to assess GPU residual value, an assumption that flows straight through to the terms providers can offer, and the terms they in turn ask of their customers. We plotted in the quoted post the actual evolution of residual values for major GPU generations as a share of their original purchase prices. These are market-based fair value estimates, derived from the forward curves implied by observed GPU rental contracts. The A100 and H100 are both holding value well in excess of what 3 or 5-year straight-line schedules would imply. In fact, B200, whose supply is scarce, we estimate a current residual value well in excess of its original purchase price! We believe that the financial institutions and capital markets will eventually come view GPUs as the long-lived income-generating capital assets they are and allow them to be financed accordingly. Silicon Data is committed to bringing price transparency and indices to help improve the capital efficiency around GPU financing.