数据要素是培育新质生产力的核心引擎。近年来，青海立足"能源极足、绿电极优、电价极低、气候极 宜、区位极特、安全极稳"的独特优势，以绿色算力为基底、以数据要素赋能为核心，在数据领域改革 发展的赛道上蹄疾步稳、奋勇争先，交出了一份亮眼夺目的"青海答卷"。 青海省数据局局长靳力介绍，在绿色算力布局上，青海聚焦"绿色算电协同发展在全国先行先试"建设目 标，打造"1+2+N"绿色算力整体架构，算力规模一年增长近40倍。截至目前，全省已建在建标准机架超 19万架，投运算力规模达2.2万PFLOPS，算力规模同比增长161%，阿里、金山、新华三等头部企业算 力项目争相点亮运行。 今年以来，我省数据要素领域多项工作斩获国家级重要成果，两案例成功入选国家数据局发布的第三 批"数据要素×"典型案例名单；两项目在2025年"数据要素×"大赛全国总决赛中脱颖而出，分别斩获金融 服务赛道一等奖和优秀奖，彰显了青海数据产品创新应用的实践能力。这些成果不仅印证了青海数据要 素创新应用的实践成效，更提炼形成了一批可复制、可推广的"青海经验"，为全国数据领域改革贡献了 高原智慧。 本报讯（西海新闻记者 吴予琴）12月19日，"数联青苏・协同赋 ...

Overview of AI Computing Infrastructure in China - The report by Analysys focuses on the development status, core driving factors, key trends, and stakeholder recommendations for AI computing infrastructure in China by 2025 [1] - The evolution path of computing infrastructure is shifting from "scale expansion" to "quality and efficiency improvement" [1] National Strategy and Scale Position - The "East Data West Computing" project is central to the national strategy, with plans to build national computing hubs in eight regions including Beijing-Tianjin-Hebei and the Yangtze River Delta, and to establish ten data center clusters [5] - As of 2024, the number of operational computing center racks in China is expected to reach 8.3 million, with a total computing power exceeding 280 EFLOPS, making it the second largest globally [7] - Intelligent computing power accounts for over 30% of the total, with a growth of nearly 13 times since 2019, averaging an annual growth rate of about 90% [7] Development Environment - National policies are solidifying top-level design, with local governments setting clear goals for intelligent computing construction [12] - Technological advancements in AI chips and cooling technologies are reducing Power Usage Effectiveness (PUE) [17] - The demand for computing power is surging due to generative AI, with applications expanding from the internet to traditional industries like finance and healthcare [19] - The supply of computing power is transitioning from heavy asset investment to platform-based services, lowering barriers for SMEs [21] Development Progress and Core Drivers - The development stages include an exploration phase (~2019), a market activation phase (2020-2022), and a high-speed growth phase (2023-2028) [26][34] - Five core driving factors include the iteration of large models, policy and capital linkage, industrial application scaling, long-tail computing power release, and cloud scheduling technology [35][36][37][38] Key Trends for AI Computing Infrastructure by 2025 - Trend 1: Accelerated breakthroughs in autonomous controllable computing power, with a goal for over 70% of computing power in Shanghai to be domestically controlled by 2027 [39] - Trend 2: Green computing becoming a hard constraint, with new data centers required to meet specific PUE standards [41] - Trend 3: Deepening cross-regional computing interconnection, enhancing the national backbone network for free flow of computing power [44] - Trend 4: Dual-track development of intelligent computing cloud platforms, offering comprehensive and vertical services [46] - Trend 5: New demands driven by large language models and knowledge bases, increasing the need for specialized computing centers [48] - Trend 6: Accelerated cloud computing for inclusivity, with a projected 80% growth in the smart computing service market by 2024 [49] Stakeholder Recommendations - Government: Strengthen regional computing network planning and provide financial/tax incentives for green computing and autonomous technology development [51] - Enterprises: Supply side should create high-quality computing cloud platforms, while demand side should prioritize cloud leasing over self-built solutions [52] - Industrial Parks: Develop intelligent computing industry clusters with supporting green energy and high-speed networks [53] - Ecosystem: Collaborate among chip, server, and cloud platform companies to tackle key technologies and establish industry standards [54]