Liquid Cooling and AI Data Center Daily | 2026-06-27
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 2026-06-27.
- Collection window: 2026-06-26 08:00 北京时间 - 2026-06-27 08:00 北京时间.
- Coverage snapshot: 8 industry items; 4 technology items; 8 paper or white-paper items; 8 video signals.
- Current hot topics: 智算中心 CapEx/扩建, 电力并网与能源约束, AI 芯片供给与交付, 液冷路线（冷板/浸没/两相）.
- 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.
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 …
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Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
- Published
- 2026-06-24
- Authors
- Chris Williams, Philip Colangelo, Ayse Coskun, Ethan Levine, Andy Neale, Ciaran Roberts, Shayan Sengupta, Nikhil Shirolkar
- Theme
- 算电协同
Abstract
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.
Chinese interpretation
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。
Reference
Chris Williams, Philip Colangelo, Ayse Coskun, 等. Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute[J/OL]. (2026-06-24)[2026-06-27]. http://arxiv.org/abs/2606.25098v1.
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
- Published
- 2026-06-24
- Authors
- Sangwhee Lee, Rafal P. Wojda, Cheol-Hee Jo, Shuntaro Inoue, Pedro Ribeiro, Gui-Jia Su, Mostak Mohammad, Himel Barua
- Theme
- 热管理与液冷
Abstract
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.
Chinese interpretation
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。
Reference
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]. (2026-06-24)[2026-06-27]. http://arxiv.org/abs/2606.25095v1.
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
- Published
- 2026-06-22
- Authors
- Salvatore Cielo, Elmira Birang, Alexander Pöppl, Sajad Azizi, Plamen Dobrev, Margarita Egelhofer, Ivan Pribec, Gerald Mathias
- Theme
- 芯片与算力
Abstract
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.
Chinese interpretation
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。
Reference
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)[2026-06-27]. http://arxiv.org/abs/2606.23265v1.
Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable …
Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operatio…
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Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters
- Published
- 2026-06-23
- Authors
- Shuyi Chen, Zhengchang Hua, Nikos Tziritas, Georgios Theodoropoulos
- Theme
- AI 运维优化
Abstract
Terrestrial AI training faces an unsustainable energy and water crisis, positioning Orbital Data Centers (ODCs) as a "zero operational carbon" alternative. However, the sub-$10μ\text{s}$ communication latency required for distributed Large Language Model (LLM) training forces ODCs into extreme physical density, triggering a critical "Proximity-Thermal Paradox." As these high-density systems scale into Monolithic Structures or Proximity Swarms, they suffer from intense thermal-fluid crosstalk (heat traps in shared cooling loops) and thermal-radiative crosstalk (mutual heating that blocks deep-space cooling radiators). If left unmitigated, this persistent heat stagnation not only triggers severe thermal throttling that degrades training throughput, but also induces severe thermal fatigue, drastically shortening hardware lifespans and generating premature space e-waste. To make orbital AI truly sustainable, this position paper challenges traditional uniform load-sharing. We propose the Thermal-Aware Heterogeneity Thesis, which treats spatial cooling variances as a primary resource management dimension. Building on this, we introduce Thermal-Load Balancing (TLB), a software framework that dynamically migrates LLM workloads to the coolest available units based on instantaneous fluid temperatures or absorbed radiation. Our analysis demonstrates that TLB resolves thermal bottlenecks to restore Model Flops Utilization (MFU), while simultaneously reducing physical thermal stress. Extending the operational lifespan of orbital hardware is crucial to amortize the massive embodied carbon of rocket launches, outlining a necessary pathway to scale orbital AI without accelerating e-waste.
Chinese interpretation
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。
Reference
Shuyi Chen, Zhengchang Hua, Nikos Tziritas, 等. Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters[J/OL]. (2026-06-23)[2026-06-27]. http://arxiv.org/abs/2606.26150v1.
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…
Expand
GaN Power Devices and Converter Architectures for AI Data Centers: Efficiency, Reliability, and Deployment Pathways
- Published
- 2026-06-24
- Authors
- Donald Intal, Abasifreke Ebong
- Theme
- 算电协同
Abstract
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.
Chinese interpretation
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。
Reference
Donald Intal, Abasifreke Ebong. GaN Power Devices and Converter Architectures for AI Data Centers: Efficiency, Reliability, and Deployment Pathways[J/OL]. (2026-06-24)[2026-06-27]. http://arxiv.org/abs/2606.25281v1.
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…
Expand
Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers
- Published
- 2026-06-19
- Authors
- Zihan Yu, Xianling Zeng, Zhiming Xue, Yalun Qi, Sichen Zhao
- Theme
- AI 运维优化
Abstract
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.
Chinese interpretation
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。
Reference
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)[2026-06-27]. http://arxiv.org/abs/2606.21130v1.
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
- Published
- 2026-06-19
- Authors
- Yuhao Huang, Novarun Deb, Hamidreza Zareipour
- Theme
- 算电协同
Abstract
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.
Chinese interpretation
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。
Reference
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)[2026-06-27]. http://arxiv.org/abs/2606.21064v1.
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
- Published
- 2026-06-25
- Authors
- Arash Baharvandi, Duong Tung Nguyen
- Theme
- 算电协同
Abstract
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.
Chinese interpretation
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。
Reference
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)[2026-06-27]. http://arxiv.org/abs/2606.26328v1.
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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How Data Centers Manage Intense Heat: Cooling Systems Explained
Equinix · Query: data center thermal management seminar。Useful as technical or research context.
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How Thermoelectric Cooling Solves AI Data Center Heat Challenges | Sheeta…
Sheetak Advanced Thermal Management · Query: data center thermal management seminar。Useful as technical or research context.
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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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电力并网与能源约束
Same-source item from the Chinese report. Verify details against the original linked source: 电力并网与能源约束
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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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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 and AWS Collaborate to Bring A…
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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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…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：NVIDIA Blog 发布相关报道（原文标题：Hott…
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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：Edged tops out data center in C…
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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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Unnamed data center developer e…
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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智算中心/数据中心建设进展：Data Center Dynamics 发布相关报道，涉及 4.4MW、120MW（原文标题：NorthVault …
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（原文标题：Galaxy Digital eyes second…
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（原文标题：Three-year data center mor…
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：Data center planned for Rhondda…
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 Dynamics 发布相关报道，涉及 540MW、660MW（原文标题：Aluminum smelte…
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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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：Mystery tech firm 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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电力与能源约束观察：HPCwire 发布相关报道（原文标题：Qualcomm and Meta Announce Strategic Multi-…
Same-source item from the Chinese report. Verify details against the original linked source: 电力与能源约束观察：HPCwire 发布相关报道（原文标题：Qualcomm…
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液冷与热管理进展：HPCwire 发布相关报道（原文标题：JetCool Brings Direct-to-Chip Liquid Cooling…
Same-source item from the Chinese report. Verify details against the original linked source: 液冷与热管理进展：HPCwire 发布相关报道（原文标题：JetCool B…
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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…
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 Knowledge 发布…
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液冷与热管理进展：Data Center Dynamics 发布相关报道，涉及 150MW（原文标题：Dogecoin cryptominer Z…
Same-source item from the Chinese report. Verify details against the original linked source: 液冷与热管理进展：Data Center Dynamics 发布相关报道，涉…
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YouTube video jA0eQV1Yu_g
YouTube · Query: OCP data center cooling workshop。Useful for product, market, or deployment context.
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Data Centers and the Future of AI Infrastructure: Federal, Local, and Ind…
Center for Strategic & International Studies · Query: AI infrastructure datacenter panel discussion。Useful for product, market, or …
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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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Expert Panel: Strategic Capital—Funding AI Infrastructure & Investment Ac…
W.Media- South Asia & Middle East · Query: AI infrastructure datacenter panel discussion。Useful for product, market, or deployment …
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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
YouTube video jA0eQV1Yu_g
行业论坛 · YouTube · Query: OCP data center cooling workshop
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 YouTubeData Centers and the Future of AI Infrastructure: Federal, Local, and Industry Perspectives
专家圆桌 · Center for Strategic & International Studies · 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 YouTubeExpert Panel: Strategic Capital—Funding AI Infrastructure & Investment Across India’s DC Regions
专家圆桌 · W.Media- South Asia & Middle East · Query: AI infrastructure datacenter panel discussion
Open on YouTubeHow Data Centers Manage Intense Heat: Cooling Systems Explained
专家讲座 · Equinix · Query: data center thermal management seminar
Open on YouTubeHow Thermoelectric Cooling Solves AI Data Center Heat Challenges | Sheetak Thermal Live 2025
专家讲座 · Sheetak Advanced Thermal Management · Query: data center thermal management seminar
Open on YouTubeSources
Collection notes
- 论文池：已从本地论文池读取 22 条候选；池更新时间 2026-06-27 12:52。
- When optional automation services are unavailable, this page uses traceable public sources and conservative rule-based summaries only; unverifiable facts are not filled in.