液冷与智算中心日报｜2026-06-25
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。
System-Level Thermal Validation of 2.5D Packages in GPU Servers: Impact o…
The scalability and long-term reliability of 2.5D System-in-Package (SiP) platforms are increasingly governed by complex thermal ma…
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System-Level Thermal Validation of 2.5D Packages in GPU Servers: Impact of TCB vs HCB HBM Platforms
- 发布时间
- 2026-05-26
- 作者
- Woohyun Park、Youchang Na、S. Hong、Yoko Tomo、H. Yu、Yanggyoo Jung、Gyungbum Kim、H. Kang
- 主题
- 芯片与算力
摘要
The scalability and long-term reliability of 2.5D System-in-Package (SiP) platforms are increasingly governed by complex thermal management requirements, particularly as the integration of High-Bandwidth Memory (HBM) introduces concentrated heat profiles that challenge the system’s operational limits. The package platform—Thermo-Compression Bonding (TCB) versus Hybrid Copper Bonding (HCB) of HBM—strongly influences intra- and inter-package thermal behavior. This work implements 2.5D system-in-package (SiP) thermal test vehicles (TTVs) in an Open Compute Project (OCP)-standard GPU server with embedded sensors and controllable heaters across HBM stacks and GPU dies, faithfully mirroring functional heterogeneous package floorplans. Experimental results demonstrate thermal nonlinearity - strong platform- and cooling-dependent. At 1030 W per package, HCB reduces intra-package GPU to HBM thermal crosstalk versus TCB by 2.2% under air cooling and 9.8% under liquid cooling, while inter-package thermal crosstalk varies by up to 13.7% across cooling conditions. Comparative evaluation confirms that HCB measurably improves thermal conduction, reducing both intra- and inter-package thermal resistance. From a data-center perspective, the reduction in GPU to HBM crosstalk resistance enables up to 0.9°C higher allowable coolant inlet temperature in liquid cooling relative to the TCB baseline, which translates to approximately 3% cooling power reduction and PUE improvement from 1.26 to 1.24. For a 1000-rack AI cluster, this corresponds to roughly 31 GWh annual energy savings. Measured thermal trends further indicate that as AI infrastructure evolves toward inference-heavy, memory-focused workloads with increased HBM base-die power, HCB platforms will deliver progressively larger thermal benefits due to the shift toward more vertical-resistance-limited behavior. This study establishes GPU server-integrated 2.5D SiP TTV methodology as a robust platform for system-level thermal validation and demonstrates that HBM platform selection directly impacts data-center operational efficiency and future inference 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，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
Woohyun Park, Youchang Na, S. Hong, 等. System-Level Thermal Validation of 2.5D Packages in GPU Servers: Impact of TCB vs HCB HBM Platforms[J/OL]. Electronic Components and Technology Conference. (2026-05-26)[2026-06-25]. https://www.semanticscholar.org/paper/2ca4f8beb1ea19fe6d038cdee022de662a80ecd6.
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)[2026-06-25]. http://arxiv.org/abs/2606.17466v1.
Data Center Life Cycle Co-Design Optimization
Liquid cooled supercomputers dissipate tens of megawatts of waste heat through cooling plants organized as parallel subloops that s…
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Data Center Life Cycle Co-Design Optimization
- 发布时间
- 2026-06-14
- 作者
- Shrenik Jadhav、Vidhyashree Nagaraju、Zheng Liu
- 主题
- 余热回收
摘要
Liquid cooled supercomputers dissipate tens of megawatts of waste heat through cooling plants organized as parallel subloops that serve coolant distribution units. The number of subloops and the assignment of units to them are design decisions fixed at construction, yet they have not been systematically optimized for facilities at this scale. As electricity grids decarbonize, embodied carbon becomes a larger share of facility life cycle emissions and the cost of an unnecessary subloop becomes harder to justify. We present a framework that integrates operational energy from a validated control optimizer based on sequential least squares programming, embodied carbon from a bill of materials, and expected unplanned downtime from a per subloop reliability model. The framework is applied to the Frontier supercomputer, evaluating all 611 ways of partitioning its 25 coolant distribution units into two through six subloops. The life cycle cost and carbon optimum is found at two subloops holding 14 and 11 units, achieving 3,320.7 tonnes of carbon dioxide equivalent and $3.99 million over a seven year horizon, a saving of 50.2 tonnes and $100,000 compared to built four subloop configuration. The optimum remains on the Pareto front in all 15 scenarios of a one at a time sensitivity sweep. A semi-analytical decision rule generalizes the result, predicting four subloops for Aurora, two for El Capitan, and one for LUMI. When reliability is treated as a hard constraint set by operations policy, the four subloop Frontier deployment is consistent with the constrained optimum.
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，余热回收、热泵耦合和二次能源利用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，它可用于判断数据中心余热能否从成本项转化为能源资产。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
Shrenik Jadhav, Vidhyashree Nagaraju, Zheng Liu. Data Center Life Cycle Co-Design Optimization[J/OL]. (2026-06-14)[2026-06-25]. http://arxiv.org/abs/2606.15408v1.
Wafer-Level Integrated 1200 V SiC MOSFET Package with Room-Temperature Wa…
The rising demand for high-power semiconductor devices in sectors such as electric vehicles (EVs), renewable energy conversion, and…
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Wafer-Level Integrated 1200 V SiC MOSFET Package with Room-Temperature Wafer Bonding and Embedded Microfluidic Cooling
- 发布时间
- 2026-05-26
- 作者
- Jiajing Nie、Jiuyang Tang、Hao Guan、Xinyue Wang、Tao Jiang、Junran Zhang、Guoqi Zhang、Guangyin Lei
- 主题
- 芯片与算力
摘要
The rising demand for high-power semiconductor devices in sectors such as electric vehicles (EVs), renewable energy conversion, and data centers highlights the need for efficient and reliable thermal management technologies. In this work, we present a simulation-based study of a 1200 V SiC MOSFET wafer-level power package that integrates chip–package co-design, room-temperature wafer bonding, and embedded microfluidic cooling. By utilizing a room-temperature bonding process to mitigate fabrication-induced warpage and optimizing the chip geometry to balance thermal spreading with mechanical stress, this proposed architecture ensures structural integrity while maximizing heat transfer efficiency. Thermal-fluid-mechanical multiphysics modeling results revealed that the proposed wafer-level microfluidic package achieved a 35.14% reduction in total thermal resistance compared with conventional SiC MOSFET power modules. The design demonstrates improvements in junction temperature uniformity and overall heat dissipation efficiency, which is promising for next-generation high-power density applications.
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
Jiajing Nie, Jiuyang Tang, Hao Guan, 等. Wafer-Level Integrated 1200 V SiC MOSFET Package with Room-Temperature Wafer Bonding and Embedded Microfluidic Cooling[J/OL]. Electronic Components and Technology Conference. (2026-05-26)[2026-06-25]. https://www.semanticscholar.org/paper/11fa662b073d777b3f9125fd8ef8a3bb5cf601cc.
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)[2026-06-25]. http://arxiv.org/abs/2606.13853v1.
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)[2026-06-25]. https://www.semanticscholar.org/paper/11f6857398316b362b30dcdbd0b233df7100bb1e.
From Tokens to Energy Flexibility: Quantization-Enabled Demand Response f…
The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-mana…
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From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads
- 发布时间
- 2026-06-17
- 作者
- Bojun Du、Xiaoyi Fan、Ershun Du、Long Chen、Jianpei Han、Qingchun Hou、Ning Zhang、Chongqing Kang
- 主题
- 算电协同
摘要
The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-management challenges under tightening grid conditions and demand response (DR) requirements. Conventional data-center energy management mainly relies on temporal and spatial workload shifting and campus-level energy asset scheduling, but it usually treats LLM inference demand as an aggregate load. As a result, these approaches fail to exploit the internal characteristics of LLM serving and therefore overlook the flexibility offered by LLM-specific techniques such as model quantization. To unlock this flexibility, this paper proposes a quantization-enabled energy management framework for grid-responsive LLM inference data centers. First, a quantization-to-power model is established to map each model--quantization configuration to a compact set of dispatchable parameters. Second, a two-stage quantization-enabled DR model is developed to account for model instance switching, request routing, and precision selection. Third, a multi-campus co-optimization method is introduced for DR participation by integrating grid-side electricity and carbon signals with the quantization-enabled DR model. Case studies show that the proposed framework reduces total data-center operating cost by 34.3\% without curtailing served token volume, validating model quantization as an effective flexibility lever for grid-responsive LLM data-center energy management.
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
Bojun Du, Xiaoyi Fan, Ershun Du, 等. From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads[J/OL]. (2026-06-17)[2026-06-25]. http://arxiv.org/abs/2606.18851v1.
GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
At global scale, data-center electricity demand is growing faster than the grids that supply it, while system operators increasingl…
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GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
- 发布时间
- 2026-05-26
- 作者
- Denisa-Andreea Constantinescu、David Atienza
- 主题
- 算电协同
摘要
At global scale, data-center electricity demand is growing faster than the grids that supply it, while system operators increasingly require large flexible loads that can adjust power within seconds to absorb variable wind and solar generation. For multi-megawatt AI/HPC facilities, the key unresolved question is practical and measurable: how quickly can the software stack translate a grid request into a real change in GPU power at the facility meter, where commitments are settled? We answer this on real hardware with GridPilot, a three-tier predictive controller operating across milliseconds, seconds, and hours, augmented by a deterministic safety-island bypass for fast response. On a three-GPU NVIDIA V100 testbed, GridPilot achieves a measured end-to-end trigger-to-target response of 97.2 ms, which is 6.9x faster than the 700 ms requirement of Nordic Fast Frequency Reserve. We further incorporate an instantaneous Power Usage Effectiveness (PUE) correction so dispatched commitments remain robust at meter level rather than only at IT load level. In replay experiments across six representative European grids (from Sweden to Poland), the PUE-aware controller closes 2.5-5.8 percentage points of cooling-overhead drag. GridPilot is released as open source and serves as a proof of concept that MW-scale AI/HPC demand can be engineered as controllable, grid-responsive flexibility by 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
Denisa-Andreea Constantinescu, David Atienza. GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers[J/OL]. (2026-05-26)[2026-06-25]. http://arxiv.org/abs/2605.26384v1.
Purdue Engineering Distinguished Lecture Series: Babu Chalamala, Panel
Purdue Engineering · 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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"High Capacity, Energy Efficient Interconnects for Data Centers" - John B…
The Institute for Energy Efficiency · 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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Webinar Recording: Next Generations – Data Center Cooling Technologies
ASHRAE Pyramids Chapter · 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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IAP 2026: Modeling Energy Systems for a Data Center Driven Future - Pablo…
MIT Video Productions · 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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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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电力并网与能源约束
本期命中 14 条，热度分 43。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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智算中心 CapEx/扩建
本期命中 12 条，热度分 41。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 4 条，热度分 12。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：NVIDIA and AWS Collaborate to Bring A…
发布时间：2026-06-24；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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电力与能源约束观察：NVIDIA Blog 发布相关报道（原文标题：Hotter Than a Hot Tub: The 45°C Breakth…
发布时间：2026-06-22；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：First Street: 79 percent of glo…
发布时间：2026-06-25；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：Polaroid ads attack data center…
发布时间：2026-06-25；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 20MW、52MW（原文标题：Hyperscale Data si…
发布时间：2026-06-24；检索窗口内；可核验指标：20MW、52MW；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：Asad Malik named head of comput…
发布时间：2026-06-24；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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电力与能源约束观察：Data Center Dynamics 发布相关报道，涉及 16GW（原文标题：Sunrun, Renew Home, an…
发布时间：2026-06-24；检索窗口内；可核验指标：16GW；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：Fire at STT GDC/Tata data cente…
发布时间：2026-06-24；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 240MW（原文标题：Locals oppose 240MW da…
发布时间：2026-06-24；检索窗口内；可核验指标：240MW；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：Spanish ISP Sarenet launches da…
发布时间：2026-06-24；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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液冷与热管理进展：Data Center Dynamics 发布相关报道（原文标题：Sponsored: AI factory cooling v…
发布时间：2026-06-24；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：MiTAC Computex 2026 Booth Tour: Diam…
发布时间：2026-06-22；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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液冷与热管理进展：Data Center Knowledge 发布相关报道（原文标题：Evaporative Cooling in Data Ce…
发布时间：2026-06-19；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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政策、标准或能效观察：Data Center Knowledge 发布相关报道（原文标题：Building Data Centers Faster…
发布时间：2026-06-22；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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投融资、财报或公司动态：Data Center Knowledge 发布相关报道（原文标题：Data Centers Take Training …
发布时间：2026-06-23；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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Keynote Power Panel : AI, Power & Geopolitics: India’s Data Center Future…
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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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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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
昨日热度高，今日暂无新增高可信条目
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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
Purdue Engineering Distinguished Lecture Series: Babu Chalamala, Panel
专家讲座 · Purdue Engineering · 检索词：AI datacenter power grid university lecture
在 YouTube 打开"High Capacity, Energy Efficient Interconnects for Data Centers" - John Bowers
学术讲座 · The Institute for Energy Efficiency · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Webinar Recording: Next Generations – Data Center Cooling Technologies
专家讲座 · ASHRAE Pyramids Chapter · 检索词：data center thermal management seminar
在 YouTube 打开IAP 2026: Modeling Energy Systems for a Data Center Driven Future - Pablo Duenas (1/27/26)
专家讲座 · MIT Video Productions · 检索词：AI datacenter power grid university lecture
在 YouTube 打开Keynote Power Panel : AI, Power & Geopolitics: India’s Data Center Future in 2026 and Beyond
专家圆桌 · W.Media- South Asia & Middle East · 检索词：AI infrastructure datacenter panel discussion
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。
- 公开 RSS/Atom：HPCwire：未检索到符合条件的高相关条目。
- 论文池：已从本地论文池读取 23 条候选；池更新时间 2026-06-25 02:33。
- 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…