一项斯坦福研究比较 AI 模型完成任务的能源效率

@rohanpaul_ai ty for sharing our work @rohanpaul_ai 🙌🏽🙌🏽🙌🏽

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 https://t.co/KAj3vxhHto