液冷与智算中心日报｜2026-07-02
追踪液冷技术、AI 智算中心、数据中心能效、学术论文、产品发布、政策标准、投融资与供应链动态的每日中文报告。
1. 今日一句话总结
24小时内，资本继续加码智算中心，但电力、审批与能效约束已前置，液冷和算电协同正转为项目准入项。
从公开信号看，资本并未因为约束而降温，资本开支仍向AI数据中心与液冷环节集中，说明头部厂商和基础设施资本仍在前置锁定园区、容量和交付窗口；但与此同时，智算中心 CapEx/扩建、电力并网与能源约束、液冷路线（冷板/浸没/两相）仍是主线，但基础设施约束已前置，意味着行业竞争的关键变量已不再只是“拿到多少 GPU”，而是“能否把 GPU 放进一个可并网、可散热、可控成本、可持续运行的系统”。技术侧技术侧继续围绕高带宽互连与服务器能效优化，论文侧论文侧继续指向算电协同、液冷优化与能效度量重构，共同指向同一个趋势：单点器件优化的边际价值在下降，网络、供电、储能、液冷和调度软件的系统级协同正在上升为真正的产能约束。对产业链而言，未来更稀缺的不是单一硬件，而是把算力、热管理和能源调度耦合起来的工程交付能力。
学术与产业速览
将论文、视频、产业动态和政策项压缩为可快速扫描的标签；每个标签只保留题目、摘要和来源入口。
学术
论文、研究趋势、学术视频与方法论线索。
Contextual Robust Optimization for AI Data Center Scheduling with Statist…
The rapid growth of AI workloads is substantially increasing data center electricity demand and carbon emissions, motivating the de…
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Contextual Robust Optimization for AI Data Center Scheduling with Statistical Guarantees
- 发布时间
- 2026-06-16
- 作者
- Yijie Yang、Xiaochong Weng、Yue Chen
- 主题
- 算电协同
摘要
The rapid growth of AI workloads is substantially increasing data center electricity demand and carbon emissions, motivating the development of carbon-aware scheduling methods. However, effective scheduling is challenging because renewable generation and AI workloads are subject to forecast errors, while training and inference workloads exhibit heterogeneity in computational characteristics. This paper proposes a contextual robust optimization framework for AI data center operation. The proposed model explicitly captures the heterogeneous computational characteristics of AI training and inference workloads. To deal with renewable generation and workload forecast errors, we develop loss-based uncertainty learning models that directly map contextual features to covariate-dependent uncertainty sets. The resulting contextual joint chance-constrained scheduling problem is reformulated into a tractable robust optimization problem, and a calibration algorithm is developed to provide finite-sample probabilistic feasibility guarantees for multiple joint chance constraints. Numerical experiments based on real-world AI workload traces and renewable generation data show that the proposed method reduces operating costs by an average of 5.57% compared to benchmark methods while maintaining reliable feasibility and strong computational scalability.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向跨地域数据中心负载与电力资源之间的调度关系。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Yijie Yang, Xiaochong Weng, Yue Chen. Contextual Robust Optimization for AI Data Center Scheduling with Statistical Guarantees[J/OL]. (2026-06-16)[2026-07-02]. https://www.semanticscholar.org/paper/4002a655c0c91986009f3172ac3568644528ceea.
Server-Level Demonstration of Package-Integrated, Two-Phase Jet Impingeme…
The rapid growth of digitalization and data-driven technologies is driving large-scale deployment of data centers worldwide. As soc…
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Server-Level Demonstration of Package-Integrated, Two-Phase Jet Impingement Cooling on AI Chipsets
- 发布时间
- 2026-06-26
- 作者
- Sidharth Rajeev、Ketan Yogi、Venkata Achyuth Kunchapu、Yunchun Yang、Harish Kumar Lattupalli、Scott N. Schiffres、Tiwei Wei、S. Rangarajan
- 主题
- 芯片与算力
摘要
The rapid growth of digitalization and data-driven technologies is driving large-scale deployment of data centers worldwide. As societies become increasingly data-hungry, the energy consumption of data centers continues to rise at an unprecedented rate, with a significant fraction of this energy being expended on thermal management and cooling infrastructure. Improving cooling efficiency has therefore become a critical challenge for the thermal management community, directly impacting both the sustainability and scala-bility of future computing systems. In this manuscript, we demonstrate an energy-efficient cooling solution based on chip-integrated two-phase cooling, which leverages liquid to vapor phase-change heat transfer to achieve high heat-flux dissipation at reduced pumping power and thermal resistance. This paper investigates an aggressive cooling architecture utilizing direct-on-die two-phase jet impingement on an NVIDIA Tesla V100 GPU. By eliminating the TIM and impinging the working fluid?R1233zd(E), a low-GWP (< 1) refrigerant?directly onto the silicon backside, the primary thermal bottleneck is removed. Experimental results demonstrate a remarkably low thermal resistance of 0.056 °C/W and a theoretical pumping power of only 0.172 W. The system exhibits superior ther- mal stability and rapid transient response compared to conventional air-cooled solutions. Furthermore, the reliability of the direct-exposure manifold was validated through 200 hours of continuous, stable operation. This study positions direct jet impingement as a highly efficient, compact, and sustainable solution for next-generation high-performance computing (HPC) environments.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用实验验证、原型测试或测量对比，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向算力硬件、边缘计算或模型部署对基础设施的牵引。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Sidharth Rajeev, Ketan Yogi, Venkata Achyuth Kunchapu, 等. Server-Level Demonstration of Package-Integrated, Two-Phase Jet Impingement Cooling on AI Chipsets[J/OL]. Journal of Electronic Packaging. (2026-06-26)[2026-07-02]. https://www.semanticscholar.org/paper/02f61b7cdb675d1bdef557d6de236a502f2d830b.
Power Optimization in Data Centres using Artificial Intelligence
热管理与液冷方向论文；Semantic Scholar 未提供可展示摘要，建议打开原文核验方法和数据边界。
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Power Optimization in Data Centres using Artificial Intelligence
- 发布时间
- 2026-06-03
- 作者
- N. Nandakumar、Research Scholar、N. Mahibanlindsay、Professor Head、C. Professor
- 主题
- 热管理与液冷
摘要
Semantic Scholar 未提供可展示的原文摘要；请打开论文链接查看全文摘要。
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。摘要缺失，建议优先打开原文查看方法、数据和边界条件。
参考文献
N. Nandakumar, Research Scholar, N. Mahibanlindsay, 等. Power Optimization in Data Centres using Artificial Intelligence[J/OL]. 2026 7th International Conference on Inventive Research in Computing Applications (ICIRCA). (2026-06-03)[2026-07-02]. https://www.semanticscholar.org/paper/5b33a59bd51dfb893024c739ab73e404f2d42f5f.
Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computatio…
Energy-intensive data centers (DCs) have emerged as substantial and flexible loads in modern power systems, underscoring the critic…
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Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computation-Electricity Coordination
- 发布时间
- 2026-06-03
- 作者
- Yugui Liu、Yibo Ding、Xudong Li、Jing Qu、Wenyi Zhang、T. Qian、Wuyou Xiao、Zhengyang Hu
- 主题
- 算电协同
摘要
Energy-intensive data centers (DCs) have emerged as substantial and flexible loads in modern power systems, underscoring the critical need for computation-electricity coordination. Harnessing the spatio-temporal flexibility of DC workloads is a promising approach to facilitate this coordination. However, existing studies overlook the collaborative potential of computational resource sharing among geo-distributed DCs, thereby failing to fully unlock this flexibility. In this paper, a bi-level computation-electricity coordination framework is proposed to explicitly capture the bidirectional interactions between DCs and power grid. Firstly, a peer-to-peer cloud service market (P2P-CSM) for geo-distributed DCs is proposed, which enables bilateral cloud service transactions to leverage regional heterogeneities (e.g., electricity prices, cooling efficiency). Secondly, locational marginal prices are embedded into the framework to reflect network congestion and nodal price disparities. Thirdly, a dual consensus alternating direction method of multipliers (ADMM)-based decentralized algorithm is developed as the P2P market clearing algorithm, and a bisection-assisted iterative algorithm is proposed to ensure rigorous convergence of the framework. Case studies conducted on modified IEEE 30-bus system validate that the P2P-CSM achieves a win-win computation-electricity coordination: it not only increases total DC operational profit by 22.8\%, but also effectively alleviates grid congestion and yields a 3.2\% reduction in total energy consumption.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Yugui Liu, Yibo Ding, Xudong Li, 等. Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computation-Electricity Coordination[J/OL]. (2026-06-03)[2026-07-02]. https://www.semanticscholar.org/paper/9962e96ebd978879cc56a88a44a99bc7fe6c5653.
AI-on-Chip Systems: A Cross-Layer Review of Architectures, Interconnects,…
The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, mem…
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AI-on-Chip Systems: A Cross-Layer Review of Architectures, Interconnects, Design Automation, and Embedded Intelligence
- 发布时间
- 2026-06-15
- 作者
- Mohamed M. Morsy
- 主题
- 芯片与算力
摘要
The rapid growth of artificial intelligence (AI) workloads is reshaping semiconductor design across architecture, interconnect, memory hierarchy, packaging, timing, and design automation. Rather than converging on a single hardware solution, the field is expanding into a heterogeneous ecosystem that includes data-center graphics processing units (GPUs), edge neural processing units (NPUs), and application-specific integrated circuits (ASICs), field-programmable gate array (FPGA)-based and hybrid AI system-on-chip (SoC) platforms, chiplet-enabled systems, and emerging beyond-conventional-silicon approaches such as photonic, neuromorphic, and analog in-memory processors. This paper presents a comprehensive review of AI-on-chip systems from a cross-layer perspective. It examines AI chip architectures and hardware platforms, network-on-chip (NoC) designs for AI communication patterns, and algorithm–hardware co-design methods for model acceleration, including compression, quantization, and sparsity-aware optimization. It also reviews clocking, synchronization, and clock-domain-crossing (CDC) challenges in large heterogeneous systems and chiplets, as well as manufacturing, advanced packaging, and reliability issues, including two-and-a-half-dimensional (2.5D) and three-dimensional (3D) integration, thermal and mechanical constraints, assembly quality, and long-term yield considerations. In parallel, the paper surveys the growing role of AI in chip design itself, covering machine-learning-assisted analysis, Bayesian and reinforcement-learning-based optimization, and the emerging use of large language models (LLMs) and AI agents for register-transfer level (RTL) generation, design-space exploration, and autonomous electronic design automation (EDA) workflows. Finally, it discusses beyond-silicon AI chip directions and the broader economic and industry context shaping cloud, on-premises, and edge deployment. By integrating these topics into a unified framework, this review highlights the key technological drivers, system-level tradeoffs, and future research directions that will define next-generation scalable, reliable, and energy-efficient AI-on-chip systems.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用综述归纳和指标比较，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向跨地域数据中心负载与电力资源之间的调度关系。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Mohamed M. Morsy. AI-on-Chip Systems: A Cross-Layer Review of Architectures, Interconnects, Design Automation, and Embedded Intelligence[J/OL]. Electronics. (2026-06-15)[2026-07-02]. https://www.semanticscholar.org/paper/6559f17a3e4aaa83cbf55ab2f8c0657056399288.
Toward Next-Generation AI Data Centers: Power Delivery Architecture Shift…
The rapid growth of AI workloads is driving unprecedented increases in data center power demand, current transients, and thermal st…
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Toward Next-Generation AI Data Centers: Power Delivery Architecture Shifts, Emerging Technologies, and Challenges
- 发布时间
- 2026-06-23
- 作者
- Sangwhee Lee、Rafal P. Wojda、Cheol-Hee Jo、Shuntaro Inoue、Pedro Ribeiro、Gui-Jia Su、Mostak Mohammad、Himel Barua
- 主题
- 热管理与液冷
摘要
The rapid growth of AI workloads is driving unprecedented increases in data center power demand, current transients, and thermal stress, exposing fundamental limitations in traditional 48 V rack architectures, low-voltage AC distribution, and line-frequency transformer interfaces. This paper reviews the three stages of architectural shifts required to support next-generation AI data centers and identifies three enabling technological building blocks: high-voltage conversion-ratio DC/DC converters, facility-level low-voltage DC distribution, and medium-voltage solid-state transformers. The advantages, technical challenges, and potential solutions associated with each building block are reviewed. Finally, future research directions and open challenges are discussed.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用综述归纳和指标比较，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向跨地域数据中心负载与电力资源之间的调度关系。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Sangwhee Lee, Rafal P. Wojda, Cheol-Hee Jo, 等. Toward Next-Generation AI Data Centers: Power Delivery Architecture Shifts, Emerging Technologies, and Challenges[J/OL]. (2026-06-23)[2026-07-02]. https://www.semanticscholar.org/paper/f179b2632112d6f9413ff1aefd4faa3fe00130f4.
Heat transfer and flow characteristics of bionic Victoria Amazonica liqui…
芯片与算力方向论文；Semantic Scholar 未提供可展示摘要，建议打开原文核验方法和数据边界。
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Heat transfer and flow characteristics of bionic Victoria Amazonica liquid cooling plate for thermal management of chips in data centers
- 发布时间
- 2026-06-01
- 作者
- Feng Zhou、Wenlong Gu、Wenlong Li、G. Ma
- 主题
- 芯片与算力
摘要
Semantic Scholar 未提供可展示的原文摘要；请打开论文链接查看全文摘要。
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，芯片、服务器和高密度算力部署正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向算力硬件、边缘计算或模型部署对基础设施的牵引。意义：对日报读者而言，它可用于判断芯片路线和服务器密度变化如何传导到机房设计。摘要缺失，建议优先打开原文查看方法、数据和边界条件。
参考文献
Feng Zhou, Wenlong Gu, Wenlong Li, 等. Heat transfer and flow characteristics of bionic Victoria Amazonica liquid cooling plate for thermal management of chips in data centers[J/OL]. International Communications in Heat and Mass Transfer. (2026-06-01)[2026-07-02]. https://www.semanticscholar.org/paper/11f6857398316b362b30dcdbd0b233df7100bb1e.
Data Center Power Crisis? Utility Veteran Explains What Developers Miss
Data Center Sales & Marketing Institute (DCSMI) · 检索词：AI datacenter power grid university lecture。适合作为技术背景或研究趋势补充。
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Cooling the Core: Strategies for Sustainable, Scalable, and AI-Ready Data…
Official WMedia Studios · 检索词：data center thermal management seminar。适合作为技术背景或研究趋势补充。
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How Data Centers Manage Intense Heat: Cooling Systems Explained
Equinix · 检索词：data center thermal management seminar。适合作为技术背景或研究趋势补充。
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智算中心 CapEx/扩建
本期命中 15 条，热度分 52。可作为论文检索、技术路线和后续研究跟踪关键词。
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电力并网与能源约束
本期命中 12 条，热度分 37。可作为论文检索、技术路线和后续研究跟踪关键词。
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液冷路线（冷板/浸没/两相）
本期命中 3 条，热度分 8。可作为论文检索、技术路线和后续研究跟踪关键词。
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产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：How NVIDIA’s Inference Software Stack…
发布时间：2026-06-30；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：Claude Meets Blackwell Ultra: Anthrop…
发布时间：2026-06-30；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：It’s a sprint and a marathon: w…
发布时间：2026-07-02；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 150MW（原文标题：150MW data center plan…
发布时间：2026-07-02；检索窗口内；可核验指标：150MW；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 $400（原文标题：Egypt greenlights licen…
发布时间：2026-07-01；检索窗口内；可核验指标：$400；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 $919bn、18.4GW（原文标题：South Korea an…
发布时间：2026-07-01；检索窗口内；可核验指标：$919bn、18.4GW；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Sponsored: Data center sustaina…
发布时间：2026-07-01；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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智算中心/数据中心建设进展：Data Center Dynamics 发布相关报道，涉及 3GW、16GW、1.1GW（原文标题：Gigawatt…
发布时间：2026-07-01；检索窗口内；可核验指标：3GW、16GW、1.1GW；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 $31（原文标题：Omen AI raises $31m to d…
发布时间：2026-07-01；检索窗口内；可核验指标：$31；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：DCD Studio: Retelit's data cent…
发布时间：2026-07-01；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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AI 算力基础设施动态：ServeTheHome 发布相关报道（原文标题：Taking an Up-Close Look at the Super…
发布时间：2026-06-27；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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技术与产品进展：Data Center Knowledge 发布相关报道（原文标题：Rack-Based Environmental Monito…
发布时间：2026-06-30；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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智算中心/数据中心建设进展：HPCwire 发布相关报道（原文标题：FS Launches 800G ZR/ZR+ Coherent Optics…
发布时间：2026-07-02；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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投融资、财报或公司动态：Data Center Dynamics 发布相关报道，涉及 $6bn（原文标题：Cloud Capital and Re…
发布时间：2026-07-02；检索窗口内；可核验指标：$6bn；细节以来源原文为准，本页不复述未核验扩展信息
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电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：Stargate Update: AI’s Biggest…
发布时间：2026-06-30；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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电力与能源约束观察：HPCwire 发布相关报道（原文标题：Brookhaven Lab and AWS Partner to Connect A…
发布时间：2026-07-01；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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Air-Cooled vs Liquid-Cooled BESS Cabinet | 30s Energy Storage Guide
pure_power · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
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Data Centre Live: AI Factories and NEO Cloud panel | Durata
Durata Group · 检索词：AI infrastructure datacenter panel discussion。用于补充产业、产品或工程部署观察。
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Expert Panel Discussion: Build Fast, Build Smart—Modular Datacenters & Co…
W.Media- South Asia & Middle East · 检索词：AI infrastructure datacenter panel discussion。用于补充产业、产品或工程部署观察。
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NVIDIA hot-water cooling cuts AI data center energy use #NVIDIA #AIcoolin…
Andrew Shpanchuk | Web3 Builder · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
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Pioneers of Next Gen Datacenter Infra | theCUBE + NYSE Wired: AI Factories
SiliconANGLE theCUBE · 检索词：AI infrastructure datacenter panel discussion。用于补充产业、产品或工程部署观察。
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产业热度指数 10/10
产业热度指数为 10/10：本期自动化检索记录到 23 条候选条目，指数按候选条目数量、来源可信度和栏目覆盖度保守计算。
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NVIDIA Blackwell/GB200/GB300
今日延续上榜
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AI 芯片供给与交付
今日延续上榜
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智算中心 CapEx/扩建
今日延续上榜
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4. 最新视频观察
Data Center Power Crisis? Utility Veteran Explains What Developers Miss
专家讲座 · Data Center Sales & Marketing Institute (DCSMI) · 检索词：AI datacenter power grid university lecture
在 YouTube 打开Air-Cooled vs Liquid-Cooled BESS Cabinet | 30s Energy Storage Guide
技术研讨会 · pure_power · 检索词：high performance computing data center cooling workshop
在 YouTube 打开Cooling the Core: Strategies for Sustainable, Scalable, and AI-Ready Data Center Thermal Management
专家讲座 · Official WMedia Studios · 检索词：data center thermal management seminar
在 YouTube 打开Data Centre Live: AI Factories and NEO Cloud panel | Durata
专家圆桌 · Durata Group · 检索词：AI infrastructure datacenter panel discussion
在 YouTube 打开Expert Panel Discussion: Build Fast, Build Smart—Modular Datacenters & Commissioning Bottlenecks
专家圆桌 · W.Media- South Asia & Middle East · 检索词：AI infrastructure datacenter panel discussion
在 YouTube 打开How Data Centers Manage Intense Heat: Cooling Systems Explained
专家讲座 · Equinix · 检索词：data center thermal management seminar
在 YouTube 打开NVIDIA hot-water cooling cuts AI data center energy use #NVIDIA #AIcooling #datacenters
技术研讨会 · Andrew Shpanchuk | Web3 Builder · 检索词：high performance computing data center cooling workshop
在 YouTube 打开Pioneers of Next Gen Datacenter Infra | theCUBE + NYSE Wired: AI Factories
专家圆桌 · SiliconANGLE theCUBE · 检索词：AI infrastructure datacenter panel discussion
在 YouTube 打开来源链接区
本次检索说明
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- 论文池：本地池存在，但没有满足日期窗口的可用论文，本期将实时检索论文。
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