Rohan Paul· @rohanpaul_ai · X·· 2 小时前AI 评分59
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斯坦福一项新研究显示，在模型不变的情况下，NVIDIA B200 每单位能耗带来的美元价值超过 AMD MI350X 的 4 倍，节省的专家时间超过 9 倍。研究者让同一个 Qwen3.6-27B 模型在 4 款单 AI 芯片上运行 220 项真实专业任务，测量各自每单位能耗产出的有效工作量。作者引用的另一项发现称，小模型每单位能耗的经济回报可能远高于最强模型，例如 Gemma 4 31B 以 23% 的能耗拿到 GLM 5.2 净价值的 56%。
正文
Stanford's new research: The NVIDIA B200 delivered over 4x the dollar value and over 9x the expert time saved per unit of energy compared with the AMD MI350X.
i.e. chip alone, with the model unchanged, can decide how much economic return you get from your power budget.
Here, Stanford researchers ran the same AI model, Qwen3.6-27B, on 4 different single AI chips across 220 real professional tasks and measured how much useful work each produced for the energy it used.
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在 X 查看被引用的帖子
来源：Rohan Paul · x.com