Stanford 研究者在 220 个真实专业任务上用同一模型 Qwen3.6-27B 测试 4 种单颗 AI 芯片，衡量每单位能量产生的有用工作。结果显示 NVIDIA B200 相比 AMD MI350X 每单位能量带来超过 4 倍的美元价值和超过 9 倍的专家时间节省，即模型不变、仅换芯片也会显著改变同样电力预算下的经济回报。作者引用的论文还构建了 Joules-to-GDP 指标，测试 5 个开源模型，其中 Gemma 4 31B 用 23% 的能量达到 GLM 5.2 每任务净价值的 56%，Qwen3.6-27B 从 1 卡 H100 扩到 8 卡仅快 22% 却耗能 3.2 倍。
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