Liquid Cooling and AI Data Center Daily | 2026-06-17
A daily English mirror of liquid cooling, AI data center efficiency, research papers, products, policy, financing, and supply-chain signals.
1. Executive brief
This English edition mirrors the same public-source dataset used by the Chinese daily report for 2026-06-17.
- Collection window: 2026-06-16 08:00 北京时间 - 2026-06-17 08:00 北京时间.
- Coverage snapshot: 8 industry items; 3 technology items; 8 paper or white-paper items; 8 video signals.
- Current hot topics: 电力并网与能源约束, 智算中心 CapEx/扩建, NVIDIA Blackwell/GB200/GB300, PUE/WUE 与能效优化.
- The heat score is 10/10 and should be read as a source-density signal, not as an investment indicator.
All claims should be verified against the original source links listed at the end of this report.
Academic and Industry Briefs
Papers, videos, industry updates, policy, financing, and projects are compressed into scannable tags with a title, summary, and source link.
Academic
Research papers, methods, research-oriented videos, and academic signals.
Data Center Life Cycle Co-Design Optimization
液冷超级计算机通过并行子回路冷却系统排放数十兆瓦废热，子回路数量及冷却分配单元的分配方式在建设阶段即已固定，却缺乏针对大规模设施的系统优化。随着电网脱碳，隐含碳在设施全生命周期排放中占比上升，不必要的子回路成本难以 justify。研究提出一个框架，整合基于序列…
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Data Center Life Cycle Co-Design Optimization
- Published
- 2026-06-14
- Authors
- Shrenik Jadhav, Vidhyashree Nagaraju, Zheng Liu
- Theme
- 余热回收
Abstract
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.
Chinese interpretation
研究问题：液冷数据中心冷却子回路数量与分配在建设阶段固定，缺乏全生命周期优化。方法线索：构建整合运行能耗、隐含碳与可靠性模型的框架，对Frontier超级计算机的611种分区方案进行评估。对AI数据中心/液冷/算电协同的意义：为液冷设施在脱碳电网下的子回路设计提供量化依据，降低不必要子回路带来的碳与成本负担。核验边界：仅基于给定摘要描述的框架与应用，未包含具体最优解数值或额外实验结果，需核对原论文完整内容确认。
Reference
Shrenik Jadhav, Vidhyashree Nagaraju, Zheng Liu. Data Center Life Cycle Co-Design Optimization[J/OL]. (2026-06-14)[2026-06-17]. http://arxiv.org/abs/2606.15408v1.
Spatial Load Correlation in AI Data-Center-Dominated Power Systems
大规模数据中心的激增带来了空间相关的需求曲线，挑战了电力系统分析中负荷统计独立性的长期假设。本文研究此类负荷相关性的出现及其对数据中心主导电网的影响。解析推导表明，相关负荷波动会放大总体随机扰动，通过削弱无功功率刚度降低电压稳定裕度，并通过侵蚀自然负荷多样性效应…
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Spatial Load Correlation in AI Data-Center-Dominated Power Systems
- Published
- 2026-06-12
- Authors
- Chandan Chaudhary, Alaaeldein Abdelkader, Yansong Pei, Mohammed Benidris, Joydeep Mitra
- Theme
- 算电协同
Abstract
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.
Chinese interpretation
研究问题：AI数据中心大规模接入后，负荷空间相关性如何打破传统独立假设并影响电网稳定性。方法线索：通过解析推导揭示相关波动对电压与频率稳定裕度的削弱作用，再以实时数字仿真验证多母线同步偏差现象。对AI数据中心/算电协同的意义：为算电协同场景下电网稳定性评估提供可量化的负荷相关结构视角，帮助运营商建立适应数据中心主导电网的规划准则。核验边界：仅基于给定摘要中的解析与仿真描述，未包含具体数值、实验平台或额外结论，需核对原论文全文确认细节。
Reference
Chandan Chaudhary, Alaaeldein Abdelkader, Yansong Pei, 等. Spatial Load Correlation in AI Data-Center-Dominated Power Systems[J/OL]. (2026-06-12)[2026-06-17]. http://arxiv.org/abs/2606.13853v1.
Maximizing Compute Capacity in AI Data Centers through Cooling, Energy St…
人工智能部署日益受限于站点级电力容量，该容量需始终同时支持计算系统与非计算系统（主要是冷却）。冷却功率需求，尤其在非蒸发冷却系统中，会随夏季环境温度大幅增加，形成每天持续数小时的高冷却功率期。因此，在有限站点级电力预算下最大化计算容量成为重要规划与运营挑战。基于…
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Maximizing Compute Capacity in AI Data Centers through Cooling, Energy Storage, and Computing Adaptation
- Published
- 2026-05-30
- Authors
- Shaolei Ren, Mohammad A. Islam, Adam Wierman
- Theme
- 热管理与液冷
Abstract
The deployment of artificial intelligence is increasingly constrained by limited site-level power capacity, which must support both compute systems and non-compute systems (primarily cooling) at all times. Cooling power demand, especially in non-evaporative cooling systems, can increase substantially with ambient temperature in the summer, producing recurring periods of elevated cooling power that often lasts for multiple hours per day. Therefore, maximizing compute capacity under a limited site-level power budget is an important planning and operational challenge. Sizing the compute system conservatively based on peak cooling power can leave part of the site-level power capacity underutilized when the cooling power is below its peak, particularly in cooler months. On the other hand, sizing the compute system aggressively based on low cooling power can cause the total site-level power demand to exceed the site-level power capacity during hot days in the summer. This paper proposes ComputeAmp (Compute Amplifier), a framework that maximizes the compute capacity by jointly and dynamically leveraging cooling, battery energy storage, and computing-based adaptation. We discuss the opportunities and limitations of ComputeAmp and illustrate its potential to significantly expand usable compute capacity within local power and water resource limits. We also present a problem formulation for ComputeAmp and highlight a few algorithmic and operational challenges.
Chinese interpretation
研究问题：站点级电力容量有限且需同时满足计算与冷却需求，冷却功率随环境温度波动导致容量利用不均。方法线索：分析非蒸发冷却系统在夏季的功率峰值特征，对比保守与激进的计算系统规模设定策略，探讨能源存储与计算自适应作为缓解手段。对AI数据中心/液冷/算电协同的意义：为热管理与液冷场景下的算电协同规划提供思路，避免电力闲置或超载。核验边界：仅依据给定摘要，方法细节与完整结论需打开论文链接核验。
Reference
Shaolei Ren, Mohammad A. Islam, Adam Wierman. Maximizing Compute Capacity in AI Data Centers through Cooling, Energy Storage, and Computing Adaptation[J/OL]. (2026-05-30)[2026-06-17]. http://arxiv.org/abs/2606.00457v1.
Space-CIM: Enabling Compute-In-Memory Accelerators for Thermally-Constrai…
人工智能算力需求激增推动数据中心建设，引发能源与可持续性危机。太空轨道数据中心因太阳能丰富、发射成本下降而成为潜在路径。然而真空环境仅依赖辐射冷却，需大面积散热器，热管理限制使标准液冷或风冷计算机难以部署。本文研究太空热约束对配备高带宽内存的GPU及新兴存算一体…
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Space-CIM: Enabling Compute-In-Memory Accelerators for Thermally-Constrained Space Platforms
- Published
- 2026-06-04
- Authors
- Sohan Salahuddin Mugdho, Md. Shahedul Hasan, Cheng Wang
- Theme
- 芯片与算力
Abstract
The rapid growth in compute demand from artificial intelligence (AI) has driven a massive surge in data center construction, precipitating an energy and sustainability crisis. Motivated by the abundant solar energy in outer space and the recent sharp reduction in space launch costs, orbital data centers are emerging as a potential pathway for the future scaling of AI compute infrastructure. While the cold background in vacuum seems appealing for cooling, computing systems operating in space without convection ultimately rely on radiative cooling, requiring large-area radiators. Such limitations in thermal management pose a significant challenge for deploying the standard liquid/air-cooled computers in space. In this work, we investigate the impact of the thermal constraints in space on both graphics processing units (GPUs) with high-bandwidth memory (HBM) and the emerging compute-in-memory (CIM) accelerators. We develop a radiator-in-the-loop co-design methodology that directly links the permitted system TOPS (terra-operations per second) with the practical radiator cooling capacity in space. Our thermal simulations reveal that the separately located GPU die and HBMs create severe thermal hotspots under limited radiator capacity, necessitating GPU thermal throttling. In contrast, CIM accelerators exhibit a much more uniform heat distribution and consistently outperform GPUs in TOPS/W across a wide range of radiator budgets. We systematically evaluated the performance of CIM and GPU across various AI workloads and demonstrated that CIM has a magnified advantage for deployment in space under realistic thermal constraints.
Chinese interpretation
研究问题：太空平台热约束如何限制AI计算系统部署。方法线索：针对GPU-HBM与CIM加速器，构建散热器在环协同设计框架，分析辐射冷却对算力密度的影响。对AI数据中心/液冷/算电协同的意义：为缓解地面数据中心能耗危机提供太空替代思路，强调热管理与计算架构联合优化对可持续AI基础设施的必要性。核验边界：仅基于给定摘要描述，具体性能数据、实验平台与完整结论需打开论文链接核验。
Reference
Sohan Salahuddin Mugdho, Md. Shahedul Hasan, Cheng Wang. Space-CIM: Enabling Compute-In-Memory Accelerators for Thermally-Constrained Space Platforms[J/OL]. (2026-06-04)[2026-06-17]. http://arxiv.org/abs/2606.05741v1.
Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Po…
超大规模AI数据中心引发空间和时间相关的负荷波动，违反经典独立性假设，且未被时间平均频谱方法捕捉。这些相关性呈偶发且非平稳特征，需要能解析瞬态结构的分析方法。本文将动态模态分解（DMD）应用于成对母线间相关系数的时序演化，形成低维状态表示，实现无需平稳性假设的模…
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Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Power Systems
- Published
- 2026-06-12
- Authors
- Chandan Chaudhary, Michael Murillo, Mohammed Ben-Idris, Joydeep Mitra, Dilip Pandit, Atri Bera
- Theme
- 算电协同
Abstract
Hyperscale AI data centers induce spatially and temporally correlated load fluctuations that violate classical independence assumptions and are not captured by time-averaged spectral methods. These correlations are episodic and non-stationary, requiring analysis that resolves transient structure. This paper applies Dynamic Mode Decomposition (DMD) to the temporal evolution of pairwise inter-bus correlation coefficients to form a low-dimensional state representation that enables modal analysis without a stationarity assumption. DMD eigenvalues encode the correlation regime: their location in the complex plane distinguishes sustained coherence, decaying transients, and intensifying events, while oscillation frequency maps to underlying physical coupling mechanisms. Using an IEEE 39-bus Real-Time Digital Simulator (RTDS) testbed with three converter-interfaced AI data center loads driven by synthetic workload profiles, global DMD provides a time-averaged modal baseline in a slow thermal band ($f \approx 0.005$\,Hz, $|μ| = 0.91$) captures 93.6\% of total correlation energy. A sliding-window DMD formulation identifies transient intensification events: 51 of 775 windows (6.6\%) satisfy the $|μ_k^{(n)}| > 1$ criterion, which aligns with stochastic workload coincidences. Cross-validation with RTDS voltage coherence confirms elevated coupling during these intervals. The proposed modal growth indicator provides an early-warning signal of correlation intensification prior to peak pairwise coherence.
Chinese interpretation
研究问题：AI数据中心负荷在空间和时间上呈现强相关波动，打破传统独立性与平稳性假设。方法线索：采用动态模态分解处理母线间相关系数的时间演化，提取低维模态并通过特征值位置与频率区分不同相关 regime。意义：为算电协同提供无需平稳假设的负荷相关分析工具，有助于数据中心与电网的动态耦合建模及运行优化。核验边界：结果基于IEEE 39-bus RTDS仿真平台与合成工作负载，实际系统验证仍需进一步核实。
Reference
Chandan Chaudhary, Michael Murillo, Mohammed Ben-Idris, 等. Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Power Systems[J/OL]. (2026-06-12)[2026-06-17]. http://arxiv.org/abs/2606.13847v1.
GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
全球数据中心电力需求增速超过电网供应，系统运营商需要大型灵活负荷在秒级调整功率以吸收风光波动。对于多兆瓦AI/HPC设施，核心问题是软件栈将电网请求转化为设施电表GPU功率实际变化的速度。GridPilot提出跨毫秒、秒和小时的三层预测控制器，并配备确定性安全岛…
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GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
- Published
- 2026-05-26
- Authors
- Denisa-Andreea Constantinescu, David Atienza
- Theme
- 算电协同
Abstract
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.
Chinese interpretation
研究问题：数据中心电力需求增速超过电网，如何在毫秒级将电网请求转化为GPU功率实际变化。方法线索：采用三层预测控制器覆盖毫秒、秒、小时尺度，并加入确定性安全岛旁路实现快速响应，同时集成瞬时PUE修正。意义：直接服务算电协同场景，支持AI超级计算机作为灵活负荷参与电网调节，提升响应速度与调度可靠性。核验边界：结果仅基于三GPU V100测试平台，完整多兆瓦设施验证与长期运行数据需查阅论文全文。
Reference
Denisa-Andreea Constantinescu, David Atienza. GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers[J/OL]. (2026-05-26)[2026-06-17]. http://arxiv.org/abs/2605.26384v1.
Pushing the Frontiers for Floating Solar Photovoltaics -- The Case for So…
浮动太阳能光伏系统为能源匮乏地区提供土地高效的清洁电力途径。南美洲全球FSPV潜力最高，约每百万英亩水面38.26 TWh，但部署有限。本研究提出技术-社会-经济框架，评估FSPV对能源获取、水安全和电网灵活性的作用，以尼加拉瓜、洪都拉斯和圭亚那为案例。50至3…
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Pushing the Frontiers for Floating Solar Photovoltaics -- The Case for South America
- Published
- 2026-06-11
- Authors
- Soham Ghosh, Anik Goswami, Krishna Kumba
- Theme
- 算电协同
Abstract
Floating solar photovoltaic (FSPV) systems provide a land-efficient pathway to expand clean electricity access in energy-poor regions. South America has among the highest global FSPV potential (approx 38.26 TWh per million acres of water surface), yet deployment remains limited. This study presents a techno-socio-economic framework to assess FSPV for energy access, water security, and grid flexibility, with case studies in Nicaragua, Honduras, and Guyana. Estimated yields for 50 to 398 MW systems exceed 1,500 to 2,000 kWh per kW annually with capacity factors above 20 percent. At El Cajon, FSPV could significantly reduce emissions relative to fossil generation. Results show competitive costs with land-based PV when accounting for avoided land use, shared hydropower infrastructure, and water benefits. The framework also highlights co-location with hydropower and AI data centers, offering a scalable model for deployment in underserved regions.
Chinese interpretation
研究问题聚焦南美洲浮动太阳能光伏部署有限却潜力巨大，如何通过框架实现能源、水与电网协同。方法线索采用技术-社会-经济评估，结合尼加拉瓜等三地案例，量化发电量、容量因子及成本竞争力。对AI数据中心/算电协同的意义在于提出FSPV与水电、AI数据中心共址模式，支持电网灵活性与能源获取。核验边界限于材料中给出的潜力数据、案例地点及框架描述，未涉及具体液冷技术或额外实验结果，需打开论文链接核验方法细节。
Reference
Soham Ghosh, Anik Goswami, Krishna Kumba. Pushing the Frontiers for Floating Solar Photovoltaics -- The Case for South America[J/OL]. (2026-06-11)[2026-06-17]. http://arxiv.org/abs/2606.12798v1.
Power Grid Infrastructure for AI Data Centers
本文探讨人工智能近期进展引发的技术前沿竞赛，推动大规模数据中心建设。文章重点分析大型数据中心对电力电网规划与运行的影响，阐述相关基础设施应对挑战的必要性。研究基于公开文献与行业观察，总结数据中心电力需求增长对电网稳定性和扩展策略的潜在作用。
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Power Grid Infrastructure for AI Data Centers
- Published
- 2026-05-31
- Authors
- Amir Sajadi, Muhy Eddin Za'ter, Maria Vabson, Kyri Baker, Bri-Mathias Hodge
- Theme
- 算电协同
Abstract
This article addresses recent advances in artificial intelligence, which have set off an astounding race among technology frontiers to build large data centers. It provides insights into impacts of large data centers on the planning and operation of the power grid.
Chinese interpretation
研究问题：人工智能快速发展如何驱动大规模数据中心建设，并对电力电网规划与运行产生何种影响。方法线索：文章通过文献综述与行业观察，梳理数据中心电力需求增长对电网的影响路径。对AI数据中心/算电协同的意义：为数据中心与电网协同规划提供洞见，强调基础设施适配对算电协同的重要性。核验边界：仅基于给定标题与摘要，未包含具体方法、数据或实验结论，需查阅全文核实细节。
Reference
Amir Sajadi, Muhy Eddin Za'ter, Maria Vabson, 等. Power Grid Infrastructure for AI Data Centers[J/OL]. (2026-05-31)[2026-06-17]. http://arxiv.org/abs/2606.00941v1.
Data Center Leaders on Building AI’s Infrastructure
Bloomberg Live · Query: AI data center energy conference keynote。Useful as technical or research context.
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WeCan'22: Brainstorming Session with the Audience - Minghua, George, Davi…
Noman Bashir · Query: ACM SIGEnergy data center energy talk。Useful as technical or research context.
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Data Democratization Panel | Priya Donti, Julia Stewart Lowndes, Nikki Tu…
WiDS Worldwide · Query: ACM SIGEnergy data center energy talk。Useful as technical or research context.
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Liquid Cooling Technology in Data Centers: How It Supports AI Workloads
Equinix · Query: data center thermal management seminar。Useful as technical or research context.
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电力并网与能源约束
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智算中心 CapEx/扩建
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NVIDIA Blackwell/GB200/GB300
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Industry
Industry news, products, policy, financing, projects, and market-oriented videos.
AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：NVIDIA Blackwell Leads on First Agent…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：NV…
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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 300MW（原文标题：Data center compa…
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心/数据中心建设进展：Data Center Dynamics 发布相…
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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 750MW（原文标题：Bitdeer looks to …
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心/数据中心建设进展：Data Center Dynamics 发布相…
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 $1.16bn（原文标题：Ireland’s Red Admira…
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道，涉…
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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 $10bn（原文标题：Amazon commits $1…
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心/数据中心建设进展：Data Center Dynamics 发布相…
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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 $1.5bn（原文标题：Google to spend …
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心/数据中心建设进展：Data Center Dynamics 发布相…
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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：Philippine telco PLDT looks to …
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：Data Center Dynamics 发布相关报道（原…
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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：HPE Interview: Why Data Cente…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Knowledge 发布相关报道…
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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：Data Centers in Space: Hype, …
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Knowledge 发布相关报道…
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技术与产品进展：Data Center Dynamics 发布相关报道（原文标题：Kerun launches integrated transf…
Same-source item from the Chinese report. Verify details against the original linked source: 技术与产品进展：Data Center Dynamics 发布相关报道（原文…
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AI 算力基础设施动态：Data Center Knowledge 发布相关报道，涉及 $124（原文标题：QumulusAI’s $124M D…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：Data Center Knowledge 发布相关…
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政策、标准或能效观察：The Register 发布相关报道（原文标题：Feds snooze as US datacenter law set …
Same-source item from the Chinese report. Verify details against the original linked source: 政策、标准或能效观察：The Register 发布相关报道（原文标题：Fe…
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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：Data Centers’ Next Hurdle: W…
Same-source item from the Chinese report. Verify details against the original linked source: 政策、标准或能效观察：Data Center Knowledge 发布相关报…
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电力与能源约束观察：Data Center Dynamics 发布相关报道，涉及 490MW（原文标题：Iren completes acquis…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：Data Center Dynamics 发布相关报道，…
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项目、采购或专利线索：Data Center Knowledge 发布相关报道（原文标题：Federal Colocation Readiness…
Same-source item from the Chinese report. Verify details against the original linked source: 项目、采购或专利线索：Data Center Knowledge 发布相关报…
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Datacenter Cooling Focus on HPC
Institution of Mechanical Engineers - IMechE · Query: high performance computing data center cooling workshop。Useful for product, m…
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How Data Centers in Space Could Change AI
Bloomberg Originals · Query: AI infrastructure datacenter panel discussion。Useful for product, market, or deployment context.
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Inside the Data Center Boom: Understanding the Massive Infrastructure Tha…
Steve Eisman · Query: AI infrastructure datacenter panel discussion。Useful for product, market, or deployment context.
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The Biggest Bottleneck in AI? Experts Break Down Data Center Challenges
Cisco · Query: AI infrastructure datacenter panel discussion。Useful for product, market, or deployment context.
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产业热度指数 10/10
Same-source item from the Chinese report. Verify details against the original linked source: 产业热度指数 10/10
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NVIDIA Blackwell/GB200/GB300
Same-source item from the Chinese report. Verify details against the original linked source: NVIDIA Blackwell/GB200/GB300
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AI 芯片供给与交付
Same-source item from the Chinese report. Verify details against the original linked source: AI 芯片供给与交付
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智算中心 CapEx/扩建
Same-source item from the Chinese report. Verify details against the original linked source: 智算中心 CapEx/扩建
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4. Video signals
Data Center Leaders on Building AI’s Infrastructure
学术会议报告 · Bloomberg Live · Query: AI data center energy conference keynote
Open on YouTubeWeCan'22: Brainstorming Session with the Audience - Minghua, George, David, and Jay
学术讲座 · Noman Bashir · Query: ACM SIGEnergy data center energy talk
Open on YouTubeData Democratization Panel | Priya Donti, Julia Stewart Lowndes, Nikki Tulley, Michela Taufer
学术讲座 · WiDS Worldwide · Query: ACM SIGEnergy data center energy talk
Open on YouTubeDatacenter Cooling Focus on HPC
技术研讨会 · Institution of Mechanical Engineers - IMechE · Query: high performance computing data center cooling workshop
Open on YouTubeHow Data Centers in Space Could Change AI
专家圆桌 · Bloomberg Originals · Query: AI infrastructure datacenter panel discussion
Open on YouTubeInside the Data Center Boom: Understanding the Massive Infrastructure That Supports AI
专家圆桌 · Steve Eisman · Query: AI infrastructure datacenter panel discussion
Open on YouTubeLiquid Cooling Technology in Data Centers: How It Supports AI Workloads
专家讲座 · Equinix · Query: data center thermal management seminar
Open on YouTubeThe Biggest Bottleneck in AI? Experts Break Down Data Center Challenges
专家圆桌 · Cisco · Query: AI infrastructure datacenter panel discussion
Open on YouTubeSources
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