AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions. This paper presents Green AI Carbon Optimizer with two primary contributions: (i) a carbon aware cloud region recommendation method for training workloads, and (ii) a power law forecasting pipeline for global AI energy demand. For location recommendation, we combine regional grid carbon intensity, renewable share, and data center Power Usage Effectiveness (PUE) into a unified scoring model across 100+ regions from major cloud providers. For a reference workload (8*A100, 100h), estimated emissions in our sampled regions range from 7.74kg to 272.00kg CO2. Selecting the best region instead of the worst corresponds to a 97.2% reduction relative to the worst case. Ablation shows that ranking by renewable share alone can select regions with higher CO2 emissions than rankings that include grid carbon intensity. For forecasting, we fit a power law relation between parameter count and training energy using 26 anchor models. We combine this fit with scenario assumptions on model growth, hardware efficiency, and training frequency, and evaluate sensitivity to inference ratio and ecosystem scaling. Across scenarios, projected 2030 demand ranges from 7TWh to 1,436TWh under the stated assumptions, highlighting the importance of deployment choices, model scaling discipline, and transparent energy reporting.

翻译：AI训练和部署消耗大量电力，但碳排放结果在模型开发的常规决策中仍未被充分整合。本文提出绿色AI碳优化器，包含两项主要贡献：（i）一种面向训练工作负载的碳感知云区域推荐方法，以及（ii）一个用于全球AI能源需求的幂律预测流程。在地点推荐方面，我们将区域电网碳强度、可再生能源占比以及数据中心电能利用效率（PUE）整合为一个统一评分模型，覆盖来自主要云提供商的100多个区域。对于参考工作负载（8*A100，100小时），我们采样区域内的估算排放量范围从7.74千克到272.00千克二氧化碳当量。选择最佳区域而非最差区域，相较最差情况可实现97.2%的减排。消融实验表明，仅按可再生能源占比排序可能导致所选区域的二氧化碳排放量高于包含电网碳强度的排序。在预测方面，我们利用26个锚定模型拟合了参数数量与训练能量之间的幂律关系。我们将这种拟合与关于模型增长、硬件效率及训练频率的情景假设相结合，并评估对推理比例和生态系统扩展的敏感性。在不同情景下，根据所述假设，预估2030年需求范围从7太瓦时到1,436太瓦时，突显了部署选择、模型扩展规范及透明能源报告的重要性。