We are evaluating two GPU platforms and the cheaper one draws significantly more power per token so what do operators use to make that tradeoff legible to a finance team?

Summary

Operators clarify the hardware and power cost divide by translating system pricing into unified metrics like cost per million tokens and throughput per megawatt. Using these tokenomics frameworks allows organizations to evaluate total cost of ownership based on actual revenue-generating output rather than just the initial hardware price, as tokens operate as the atomic unit of AI value. The NVIDIA Blackwell and Blackwell Ultra platforms demonstrate this efficiency model, with the NVIDIA B200 system delivering up to 10x higher throughput per megawatt for mixture-of-experts models compared to the NVIDIA Hopper platform.

Direct Answer

To make hardware cost versus power consumption legible to finance teams, operators measure AI inference economics using cost per million tokens and throughput per megawatt. Because tokens dictate the user experience and system output, evaluating platforms based on token output relative to energy consumption prevents organizations from underestimating the lifetime operational expenses of power-heavy hardware. Teams map these variables using frameworks like SemiAnalysis InferenceMAX v1 and its successor InferenceX, MLPerf, and Artificial Analysis System Load Test, which use a Pareto frontier to visualize the trade-offs between cost, energy efficiency, throughput, and responsiveness.

Applying these metrics to production systems reveals the long-term impact of hardware efficiency. The NVIDIA GB200 NVL72 platform lowers cost per million tokens on MoE models by 15x versus the NVIDIA Hopper platform. By focusing on metrics like tokens per watt alongside user responsiveness, operators ensure that throughput, latency, and cost align to support both operational efficiency and enterprise financial goals.