What it means
GPU depreciation is how the cost of an AI accelerator is spread across the years it stays in service, and how fast its economic value falls. It matters because GPUs are expensive and improve quickly: a chip bought today can be outclassed within a few product cycles, so the assumed useful life directly drives the cost of every hour of compute. In the AI supply chain, depreciation sits at the economics layer and feeds straight into TCO and cost per token; it is also a place where upstream shocks and buildout risk accumulate. Longer assumed lives lower reported costs but raise the risk of stranding older hardware; shorter lives raise costs but keep fleets current. For operators funding GPU purchases with debt, the depreciation schedule becomes a central lever on reported margins — and a key source of AI buildout risk if reality diverges from the assumption.
Why it matters to investors
Depreciation assumptions are one of the biggest swing factors in AI-infrastructure economics: aggressive useful-life estimates flatter reported margins, while faster obsolescence raises the risk of stranded, under-earning GPUs across the buildout.
Companies on this part of the chain
Named to show where the term sits in the AI supply chain — research, not advice, and never a recommendation to buy or sell.
Related terms
See GPU depreciation in the live AI chain.
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THE ENTITY is an educational read on the AI supply chain — research, not investment advice. It explains how the chain works and who sits where, never price targets or buy/sell calls.