Rohan Paul
@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://x.com/jonsaadfalcon/status/2118079242340814896/video/1
