今天分享的是：2025电力行业算力之争，电力为王：聚焦美国AI能源革命核心赛道 报告共计：67页 AI浪潮席卷全球，算力需求呈指数级增长，而其背后一个至关重要的基础资源——电力，正成为制约发展的关键瓶颈。一份最新行业研究报告深入剖析了 美国在AI时代下面临的电力供需挑战与能源革命的新赛道，揭示了"算力之争，本质是电力之争"的核心逻辑。 报告指出，美国整体用电增速虽看似平缓，但由人工智能驱动的数据中心用电正成为电力需求的强劲增长极。目前，数据中心能耗已占美国总电力消耗的 4.4%，且这一比例正迅速攀升。多家权威机构预测，到2028年至2030年，数据中心耗电量可能占据全美用电量的6.7%至12%，所需新增电力装机容量最高可 达100吉瓦。然而，美国的电力供应体系却面临严峻考验：发电结构依赖天然气但新增产能有限，电网区域互联容量不足，且数据中心高度集中在弗吉尼亚 等少数州，导致局部电网压力巨大，供电可靠性问题日益凸显。 面对迫在眉睫的电力缺口，行业正在寻找多元化的解决路径。短期来看，现有能源的挖潜与快速部署成为关键。核电因其极高的运行稳定性和清洁性，被视 作数据中心理想的基础负荷电源，近期相关支持政策频出，科技企 ...

Core Viewpoint - The competition for computing power, particularly in AI model training, is intensifying, with the availability of graphics cards being a crucial factor in determining success in this arena [1][12]. Group 1: H20 Graphics Card Overview - The H20 graphics card, launched by NVIDIA at the end of 2023, is designed for AI model inference training and has a performance level of 10% to 15% compared to the previous flagship H100 [3]. - Despite the high price of 110,000 yuan (approximately 15.5 million USD), the H20 is currently the best-performing graphics card available in China, leading many tech companies to purchase significant quantities [4]. Group 2: Market Demand and Sales Dynamics - Major companies like ByteDance, Alibaba, and Tencent ordered at least 16 billion USD worth of H20 graphics cards in the first quarter of last year, with ByteDance reportedly stockpiling 100,000 GPU modules [5]. - The sales of H20 were initially banned in China on April 9, causing significant concern for NVIDIA, which prompted efforts to lift the ban [7]. Group 3: Competitive Landscape and Strategic Implications - NVIDIA's CEO is motivated to restore sales of the H20 due to the relatively low performance of the card compared to the latest GB200, which is estimated to be over 50 times more powerful [8]. - If NVIDIA fails to supply the H20, domestic competitors like Huawei's Ascend 910C, which is approaching the performance parameters of the H100, may fill the market gap [9]. - The resumption of H20 sales indicates that the race for computing power in the AI era is far from over, emphasizing the importance of graphics card availability in training superior AI models [10][13].