Liquid Cooling and AI Data Center Daily | 2026-07-01
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 2026-07-01.
- Collection window: 2026-06-30 08:00 北京时间 - 2026-07-01 08:00 北京时间.
- Coverage snapshot: 8 industry items; 8 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, 液冷路线（冷板/浸没/两相）, AI 芯片供给与交付.
- 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.
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)[2026-07-01]. http://arxiv.org/abs/2606.25095v1.
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)[2026-07-01]. http://arxiv.org/abs/2606.21064v1.
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)[2026-07-01]. 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
- 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)[2026-07-01]. http://arxiv.org/abs/2606.21130v1.
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
- Published
- 2026-06-17
- Authors
- Bojun Du, Xiaoyi Fan, Ershun Du, Long Chen, Jianpei Han, Qingchun Hou, Ning Zhang, Chongqing Kang
- Theme
- 算电协同
Abstract
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.
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
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)[2026-07-01]. http://arxiv.org/abs/2606.18851v1.
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)[2026-07-01]. http://arxiv.org/abs/2606.25098v1.
Power Grid Infrastructure for AI Data Centers
This article addresses recent advances in artificial intelligence, which have set off an astounding race among technology frontiers…
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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
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
Amir Sajadi, Muhy Eddin Za'ter, Maria Vabson, 等. Power Grid Infrastructure for AI Data Centers[J/OL]. (2026-05-31)[2026-07-01]. http://arxiv.org/abs/2606.00941v1.
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
- Published
- 2026-06-16
- Authors
- Yijie Yang, Xi Weng, Yue Chen
- Theme
- 算电协同
Abstract
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.
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。
Reference
Yijie Yang, Xi Weng, Yue Chen. Contextual Robust Optimization for AI Data Center Scheduling with Statistical Guarantees[J/OL]. (2026-06-16)[2026-07-01]. http://arxiv.org/abs/2606.17466v1.
The environmental impact of AI | Isha Gollapudi | TEDxNormal
TEDx Talks · 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
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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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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液冷路线（冷板/浸没/两相）
Same-source item from the Chinese report. Verify details against the original linked source: 液冷路线（冷板/浸没/两相）
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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：How NVIDIA’s Inference Software Stack…
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AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：Claude Meets Blackwell Ultra: Anthrop…
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：Cl…
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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…
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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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Proposed data center outside Ca…
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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：Shaking off the rust: Pennsylva…
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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：Michigan's Oakland University b…
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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：Mayor could block data center p…
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 32.5MW（原文标题：Virtus to develop 32.…
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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AI 算力基础设施动态：The Register 发布相关报道（原文标题：Arm64 on the desktop? It’s spendy an…
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数据中心产业动态：The Register 发布相关报道（原文标题：How is AI changing datacenter network f…
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数据中心产业动态：The Register 发布相关报道（原文标题：Australia investigating five social med…
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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: Understanding Ital…
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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液冷与热管理进展：Data Center Dynamics 发布相关报道，涉及 8kW（原文标题：JetCool debuts liquid-co…
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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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…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：ServeTheHome 发布相关报道（原文标题：T…
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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…
Same-source item from the Chinese report. Verify details against the original linked source: 技术与产品进展：Data Center Knowledge 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 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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电力与能源约束观察：Data Center Dynamics 发布相关报道（原文标题：DCD Studio: Italian 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 Dynamics 发布相关报道（…
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投融资、财报或公司动态：Data Center Dynamics 发布相关报道，涉及 $3.5 billion、288MW（原文标题：Digita…
Same-source item from the Chinese report. Verify details against the original linked source: 投融资、财报或公司动态：Data Center Dynamics 发布相关报…
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电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：Stargate Update: AI’s Biggest…
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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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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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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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
The environmental impact of AI | Isha Gollapudi | TEDxNormal
学术会议报告 · TEDx Talks · 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 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 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 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
Collection notes
- 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 21 条候选；池更新时间 2026-07-01 02:32。
- When optional automation services are unavailable, this page uses traceable public sources and conservative rule-based summaries only; unverifiable facts are not filled in.