斯坦福 研究顯示，小型 AI 模型在能源效率上可創造更高經濟價值，凸顯模型規模與成本平衡的重要性。
- Joules-to-GDP 指標：衡量 AI 工作的美元價值減去執行成本，再除以能量消耗。
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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
