斯坦福大学研究团队提出 Joules-to-GDP 框架，旨在衡量 AI 系统在完成专业任务时，扣除运行成本后的单位能耗净价值。研究通过对 5 款开源模型在 220 项职业任务中的测试发现，盲目追求最强模型并不总是经济最优，部分小模型在单位能耗下的经济回报率显著更高，且增加 GPU 数量在提升任务速度方面的边际收益有限。
New Stanford paper finds that a smaller AI model can return far more economic value per unit of energy than the most capable one.
Now that the world is limited by energy/power, how much energy each AI task burns should indeed by studied.
Here, the researchers built Joules-to-GDP, which measures the dollar value of an AI's work, minus its running cost, per joule of energy used. They tested 5 open models on 220 professional tasks from GDPval, covering 44 occupations.
GLM 5.2 produced the most net value per task, but Gemma 4 31B got 56% of it with 23% of the energy.
Moving Qwen3.6-27B from 1 to 8 H100s cut task time by just 22% while using 3.2x the energy.
GPT-OSS 120B used 68% less energy than GLM 5.2 despite nearly the same price and speed.
Running the same model on 8 H100 chips instead of 1 made tasks only 22% faster but used 3.24x the energy, for nearly the same dollar value of work
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来源：Rohan Paul · x.com