GTC 2025 and the Inference Cost Curve

NVIDIA’s March conference kept the focus on scale, but the number that matters to product teams is not training throughput. It is cost per inference, and it has been falling steadily.

Why that changes product decisions

Features that were uneconomical at last year’s prices become viable at this year’s. A summarization step on every record, a classification pass over an entire archive, a suggestion generated on each page load — all of these move from no to maybe as the curve bends.

The corollary is that a feasibility assessment from twelve months ago is stale. We re-run the arithmetic on shelved AI features roughly twice a year, and a surprising number of them have quietly become affordable.

The caveat

Falling unit cost with rising usage does not automatically produce a smaller bill. Instrument consumption before you scale a feature, or you will find out from finance rather than from your dashboard.