液冷与智算中心日报｜2026-06-30
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，扩建继续推进，但电力、选址审批与能源获取仍是主约束，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。
Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
The rapid expansion of artificial intelligence (AI) infrastructure is driving unprecedented growth in electricity demand from data …
展开全文
Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
- 发布时间
- 2026-06-24
- 作者
- Chris Williams、Philip Colangelo、Ayse Coskun、Ethan Levine、Andy Neale、Ciaran Roberts、Shayan Sengupta、Nikhil Shirolkar
- 主题
- 算电协同
摘要
The rapid expansion of artificial intelligence (AI) infrastructure is driving unprecedented growth in electricity demand from data centers. Traditional power-system planning treats large computing facilities as inflexible peak loads, leading to costly infrastructure upgrades and long delays in grid interconnection. Recent work has shown that AI clusters can reduce electricity consumption during peak demand through software-based workload orchestration. This article explores how modern GPU-based AI data centers can operate as grid-interactive assets that respond dynamically to power system conditions. We describe an architecture integrating grid signals, workload scheduling, and power telemetry for fine-grained cluster power control. Experimental results from a real-world deployment on a 130 kW GPU cluster demonstrate multiple forms of flexibility, including rapid load reduction, sustained curtailment, and carbon-aware operation while preserving service levels for priority jobs. We further demonstrate performance-aware load shifting across geographically distributed clusters, enabling workloads to migrate toward regions with lower grid stress. Together, these capabilities transform AI infrastructure from static electricity consumers into flexible resources that support grid reliability, accelerate interconnection, and improve computing sustainability.
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
Chris Williams, Philip Colangelo, Ayse Coskun, 等. Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute[J/OL]. (2026-06-24)[2026-06-30]. http://arxiv.org/abs/2606.25098v1.
Node-Level Performance and Energy Characterization of Flagship Science Ap…
We present a systematic performance and energy-efficiency characterization of five flagship scientific workloads on SuperMUC-NG pha…
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Node-Level Performance and Energy Characterization of Flagship Science Applications on SuperMUC-NG Phase 2
- 发布时间
- 2026-06-22
- 作者
- Salvatore Cielo、Elmira Birang、Alexander Pöppl、Sajad Azizi、Plamen Dobrev、Margarita Egelhofer、Ivan Pribec、Gerald Mathias
- 主题
- 芯片与算力
摘要
We present a systematic performance and energy-efficiency characterization of five flagship scientific workloads on SuperMUC-NG phase 2, the 28 PetaFLOPs system at the Leibniz Supercomputing Center (LRZ) equipped with Intel Xeon Platinum 8480+ and Intel Data Center GPU Max 1550 (Ponte Vecchio, PVC) accelerators. The selected codes span molecular dynamics (gromacs, lammps), astrophysics and cosmology (OpenGadget3, AthenaK), and finite-element PDE solvers from the dealii-X Center of Excellence. For each code we measure throughput and energy efficiency expressed as compute-elements per wall-clock second (or per Joule of consumed energy) on a single compute node, comparing CPU-only (SPR) against combined CPU+GPU (SPR+PVC) configurations where available. Energy measurements rely on lightweight code instrumentation with p3em, or the Energy Aware Runtime (EAR) present on the system. Our results show that GPU offload yields $4-12\times$ higher throughput and up to $15\times$ better energy efficiency compared to CPU-only execution, with lammps and AthenaK benefiting most. However, both throughput and energy gains are sensitive to problem granularity: insufficient work per GPU tile erodes the accelerator advantage, as clearly observed in AthenaK at small mesh-block sizes. The power-budget utilization is systematically lower for CPUs than it is for GPUs, indicating that even at peak useful-work rate, most applications running on CPUs leave a significant fraction of the node's thermal envelope unused.
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：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。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
Salvatore Cielo, Elmira Birang, Alexander Pöppl, 等. Node-Level Performance and Energy Characterization of Flagship Science Applications on SuperMUC-NG Phase 2[J/OL]. (2026-06-22)[2026-06-30]. http://arxiv.org/abs/2606.23265v1.
Learning Burst-Aware Early Warning Models for Capacity Stress under AI Wo…
The rapid growth of large-scale AI workloads, particularly Large Language Model (LLM) training and inference, is fundamentally resh…
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Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers
- 发布时间
- 2026-06-19
- 作者
- Zihan Yu、Xianling Zeng、Zhiming Xue、Yalun Qi、Sichen Zhao
- 主题
- AI 运维优化
摘要
The rapid growth of large-scale AI workloads, particularly Large Language Model (LLM) training and inference, is fundamentally reshaping the operational dynamics of hyperscale data centers. Unlike traditional cloud workloads, AI-driven jobs exhibit bursty, high-intensity, and rapidly shifting resource demands, often leading to sudden capacity stress that cannot be effectively handled by reactive threshold-based mechanisms. In this paper, we propose a deployment-oriented, burst-aware early warning framework for proactive capacity stress prediction under AI workload surges. We formulate the problem as a high-recall forecasting task over multivariate telemetry windows, with the explicit goal of enabling operational intervention before system degradation occurs. The proposed framework integrates workload intensity, temporal variation, and system pressure signals, and employs a lightweight tree-based learning model to capture nonlinear interactions in highly imbalanced environments. To evaluate the system under realistic conditions, we introduce an AI workload surge injection methodology that simulates burst-driven demand patterns observed in large-scale AI systems. Our XGBoost-based model achieves an ROC AUC of 0.697 and an AP of 0.670, significantly outperforming baseline methods. Under deployment-oriented threshold selection, the framework achieves a Recall of 0.914, enabling the detection of the majority of stress-prone periods with acceptable false-alarm cost. Beyond predictive performance, we show how the proposed framework can be integrated into operational control loops to support proactive actions such as workload throttling and resource scaling. Our results highlight the practical value of high-recall, learning-based early warning systems in enabling resilient and adaptive data center operations in the era of AI-driven workloads.
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，AI 运维、负载预测和设施调优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AI 工具是否能降低运维复杂度并提升可用性。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
Zihan Yu, Xianling Zeng, Zhiming Xue, 等. Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers[J/OL]. (2026-06-19)[2026-06-30]. http://arxiv.org/abs/2606.21130v1.
A Bilevel Framework for Data Center-Grid Coordination with DLMPs in Unbal…
This paper proposes a grid-aware coordination framework between data centers and distribution grids using a DLMP-based bilevel opti…
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A Bilevel Framework for Data Center-Grid Coordination with DLMPs in Unbalanced Three-Phase Distribution Systems
- 发布时间
- 2026-06-25
- 作者
- Arash Baharvandi、Duong Tung Nguyen
- 主题
- 算电协同
摘要
This paper proposes a grid-aware coordination framework between data centers and distribution grids using a DLMP-based bilevel optimization model. The data center aggregator (DCA) determines active power demand in response to distribution locational marginal prices (DLMPs), while the distribution system operator (DSO) solves a network-constrained optimal power flow problem to determine DLMPs in an unbalanced three-phase system. The model incorporates both active and reactive power consumption of data centers to evaluate their impacts on voltage regulation and phase imbalance. To mitigate adverse network effects, two operating cases are analyzed: without reactive power compensation and with static var generator (SVG)-based compensation. The proposed approach is validated on the IEEE 37-bus unbalanced distribution test system. Simulation results show that DLMP-based coordination captures economically efficient data center operation, and phase- and location-dependent network conditions, while SVG-based compensation improves voltage profiles and reduces phase unbalance.
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
Arash Baharvandi, Duong Tung Nguyen. A Bilevel Framework for Data Center-Grid Coordination with DLMPs in Unbalanced Three-Phase Distribution Systems[J/OL]. (2026-06-25)[2026-06-30]. http://arxiv.org/abs/2606.26328v1.
GaN Power Devices and Converter Architectures for AI Data Centers: Effici…
The growth of artificial-intelligence workloads is increasing the electrical and thermal demands on data-center power-delivery syst…
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GaN Power Devices and Converter Architectures for AI Data Centers: Efficiency, Reliability, and Deployment Pathways
- 发布时间
- 2026-06-24
- 作者
- Donald Intal、Abasifreke Ebong
- 主题
- 算电协同
摘要
The growth of artificial-intelligence workloads is increasing the electrical and thermal demands on data-center power-delivery systems, making conversion efficiency, power density, and reliability critical design priorities. This review examines how gallium-nitride (GaN) power devices can be matched to specific stages of the grid-to-load conversion chain, including power-factor correction, isolated DC/DC conversion, 48-V intermediate-bus conversion, and point-of-load regulation. Si, SiC, and GaN are compared using converter-relevant metrics, and lateral, vertical, and specialized GaN architectures are evaluated in terms of voltage scalability, switching behavior, reverse conduction, thermal pathways, gate control, and technology maturity. The analysis shows that GaN provides a stage-dependent rather than universal advantage. Commercial lateral GaN HEMTs are particularly effective in high-frequency, low-to-mid-voltage stages, while specialized and hybrid devices support bidirectional operation, normally-off control, extreme conversion ratios, and integration. Vertical GaN remains an emerging option for higher-voltage and higher-power conversion. A quantitative framework links cascaded converter efficiency to electrical-loss reduction, cooling demand, annual facility energy use, and operational carbon emissions. Broad deployment further requires low-parasitic packaging, disciplined gate-drive and EMI co-design, mission-profile reliability qualification, scalable manufacturing, and supply-chain resilience. GaN is therefore best treated as a stage-specific system lever whose value depends on coordinated device, topology, package, and thermal co-design.
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
Donald Intal, Abasifreke Ebong. GaN Power Devices and Converter Architectures for AI Data Centers: Efficiency, Reliability, and Deployment Pathways[J/OL]. (2026-06-24)[2026-06-30]. http://arxiv.org/abs/2606.25281v1.
AI Data Centers and Power System Sustainability: Understanding the Sustai…
The rapid expansion of artificial intelligence (AI) has driven unprecedented growth in data center electricity demand. The scale an…
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AI Data Centers and Power System Sustainability: Understanding the Sustainability Implications of AI-Driven Data Centers on Power Systems
- 发布时间
- 2026-06-19
- 作者
- Yuhao Huang、Novarun Deb、Hamidreza Zareipour
- 主题
- 算电协同
摘要
The rapid expansion of artificial intelligence (AI) has driven unprecedented growth in data center electricity demand. The scale and pace of this load growth carry significant implications for the sustainability of electric power systems. On the one hand, rapid, spatially concentrated data center load growth is outpacing clean energy deployment in several major regions, raising emissions and challenging both grid flexibility and reliability. On the other hand, this fast-developing and capital-intensive sector offers abundant opportunities to advance sustainability through clean energy integration and operational innovations. This article provides an overview of the mechanisms through which data center affect power system sustainability, underscoring both risks and the potential. Specifically, this article (i) characterizes AI data center load behavior and categorizes electricity supply configurations by function and sustainability profile, as well as situates these loads within global and regional electricity demand trends; (ii) analyzes sustainability impacts across short-run operational and long-run planning mechanisms, evaluates effects on grid carbon emissions and renewable energy utilization, and feasibility of offering system flexibility and participating in ancillary service; and (iii) evaluates real-world corporate sustainability pathways and highlighting both the system benefits and feasibility limits of current carbon accounting practices. The goal of this work is to synthesize existing knowledge and technological developments and to guide research and development toward a more sustainable integration of AI data centers and electric power 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，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
Yuhao Huang, Novarun Deb, Hamidreza Zareipour. AI Data Centers and Power System Sustainability: Understanding the Sustainability Implications of AI-Driven Data Centers on Power Systems[J/OL]. (2026-06-19)[2026-06-30]. http://arxiv.org/abs/2606.21064v1.
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、Xi 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, Xi Weng, Yue Chen. Contextual Robust Optimization for AI Data Center Scheduling with Statistical Guarantees[J/OL]. (2026-06-16)[2026-06-30]. http://arxiv.org/abs/2606.17466v1.
Spatial Load Correlation in AI Data-Center-Dominated Power Systems
The proliferation of large-scale data centers introduces spatially correlated demand profiles that challenge the long-standing assu…
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Spatial Load Correlation in AI Data-Center-Dominated Power Systems
- 发布时间
- 2026-06-12
- 作者
- Chandan Chaudhary、Alaaeldein Abdelkader、Yansong Pei、Mohammed Benidris、Joydeep Mitra
- 主题
- 算电协同
摘要
The proliferation of large-scale data centers introduces spatially correlated demand profiles that challenge the long-standing assumption of statistical independence of loads in power system analysis. This paper examines the emergence of such load correlations and evaluates their impact on data-center-dominated grids. Analytical derivations reveal that correlated load fluctuations amplify aggregate stochastic disturbances, reduce voltage stability margins through weakened reactive power stiffness, and degrade frequency stability margin by erosion of natural load diversity effects. Real-time digital simulation studies confirm that moderate spatial correlation in distributed data centers produces simultaneous frequency deviations and voltage fluctuations across multiple buses. The findings offer transmission system operators a physics-based perspective to interpret emerging oscillatory phenomena and establish stability planning criteria grounded in measurable load-correlation structures rather than traditional diversity assumptions.
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：摘要显示作者采用仿真建模和情景分析，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
Chandan Chaudhary, Alaaeldein Abdelkader, Yansong Pei, 等. Spatial Load Correlation in AI Data-Center-Dominated Power Systems[J/OL]. (2026-06-12)[2026-06-30]. http://arxiv.org/abs/2606.13853v1.
True Cost of Solar Panels | DON'T WASTE YOUR MONEY
23ABC News | KERO · 检索词：ACM SIGEnergy data center energy talk。适合作为技术背景或研究趋势补充。
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ASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
ASHRAE Italy · 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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Beyond the Grid: How Google’s Data Centers Power AI and Communities
Custom Content from WSJ · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
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Christos Kozyrakis: The Role of Hardware in Datacenter Efficiency. @ SICS…
RISE SICS · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
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Smartphone Powered Data Centers: Shifting Toward Energy Efficiency
IEEE Computer Society Silicon Valley · 检索词：IEEE data center energy efficiency lecture。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 17 条，热度分 56。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 13 条，热度分 34。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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AI 芯片供给与交付
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：Claude Meets Blackwell Ultra: Anthrop…
发布时间：2026-06-30；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：NVIDIA Blog 发布相关报道（原文标题：NVIDIA and AWS Collaborate to Bring A…
发布时间：2026-06-24；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：Kyivstar announces plans for AI…
发布时间：2026-06-30；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（原文标题：Lawsuit filed to lift data cent…
发布时间：2026-06-30；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（原文标题：900-acre data center could be b…
发布时间：2026-06-30；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（原文标题：First data center built inside …
发布时间：2026-06-30；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 $10bn、100MW（原文标题：Crusoe to invest…
发布时间：2026-06-30；检索窗口内；可核验指标：$10bn、100MW；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 1.5GW（原文标题：1.5GW data center…
发布时间：2026-06-30；检索窗口内；可核验指标：1.5GW；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 Dynamics 发布相关报道（原文标题：DCD Studio: Balancing rapid AI…
发布时间：2026-06-30；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（原文标题：Data center developer Blac…
发布时间：2026-06-30；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；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 Knowledge 发布相关报道（原文标题：Texas AI Data Centers: Po…
发布时间：2026-06-26；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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OCPSummit19 - EW: Advanced Cooling - Immersed Computing® for OCP
Open Compute Project · 检索词：OCP data center cooling workshop。用于补充产业、产品或工程部署观察。
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The Fluid Nature of Data Center Cooling
Open Compute Project · 检索词：OCP data center cooling workshop。用于补充产业、产品或工程部署观察。
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Enabling 1MW Data Center Racks through Innovations in Power and Liquid Co…
Open Compute Project · 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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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 21 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
OCPSummit19 - EW: Advanced Cooling - Immersed Computing® for OCP
行业论坛 · Open Compute Project · 检索词：OCP data center cooling workshop
在 YouTube 打开The Fluid Nature of Data Center Cooling
行业论坛 · Open Compute Project · 检索词：OCP data center cooling workshop
在 YouTube 打开True Cost of Solar Panels | DON'T WASTE YOUR MONEY
学术讲座 · 23ABC News | KERO · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开ASHRAE ITALY - LIQUID COOLING AND CHALLANGES IN IMPLEMENTATION
专家讲座 · ASHRAE Italy · 检索词：data center thermal management seminar
在 YouTube 打开Beyond the Grid: How Google’s Data Centers Power AI and Communities
学术会议报告 · Custom Content from WSJ · 检索词：AI data center energy conference keynote
在 YouTube 打开Christos Kozyrakis: The Role of Hardware in Datacenter Efficiency. @ SICS Software Week
学术讲座 · RISE SICS · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Enabling 1MW Data Center Racks through Innovations in Power and Liquid Cooling
技术研讨会 · Open Compute Project · 检索词：high performance computing data center cooling workshop
在 YouTube 打开Smartphone Powered Data Centers: Shifting Toward Energy Efficiency
学术讲座 · IEEE Computer Society Silicon Valley · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开来源链接区
本次检索说明
- 当前自动化环境未配置 Tavily、Bing News 或 SerpAPI 检索密钥；脚本将使用公开 RSS/Atom、公共 arXiv 接口与固定监测源，不会编造产业新闻。
- 论文池：已从本地论文池读取 21 条候选；池更新时间 2026-06-30 02:32。
- x.ai 论文解读：文本生成失败，已回退到规则化论文摘要；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 1 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 2 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 3 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 4 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 5 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 6 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 7 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 8 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- AI 分析：x.ai 调用失败，已回退到规则化模板；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit…