Liquid Cooling and AI Data Center Daily | 2026-06-13
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 2026-06-13.
- Collection window: 2026-06-12 08:00 北京时间 - 2026-06-13 08:00 北京时间.
- Coverage snapshot: 8 industry items; 2 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, AI 芯片供给与交付, NVIDIA Blackwell/GB200/GB300.
- 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.
Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for…
随着数据中心能耗接近电网约束，优化传统服务器基础设施对可持续增长至关重要。“更冷更好”的长期假设，即降低CPU温度可减少功耗，并不完全适用于现代低压CPU，因为逆温度依赖（ITD）会在低温时驱动更高供电电压。这形成非单调的性能每瓦曲线，效率在中间热节点达到峰值。…
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Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation
- Published
- 2026-06-10
- Authors
- Jason Crop, Hayden Moore, Sudeep Pasricha
- Theme
- 算电协同
Abstract
As data center energy demand approaches grid-level constraints, optimizing conventional server infrastructure is essential for sustainable growth. The long-standing assumption that "cooler is better", i.e., lower CPU temperatures reduce power, does not fully hold for modern low-voltage CPUs, where inverse temperature dependence (ITD) drives higher supply voltages at lower temperatures. This creates a non-monotonic performance-per-watt curve where efficiency peaks at an intermediate thermal point. In this paper, for the first time, we empirically characterize ITD on production Intel Xeon CPUs and demonstrate that efficiency-optimal temperatures are CPU part-specific, and frequently higher than typical data center operating conditions. Measurements from commercial cloud data center platforms (Amazon, Equinix) reveal that approximately half of modern high-power CPUs operate about 10°C below their efficiency-optimal thermal point. By implementing ITD-aware thermal grouping of CPUs and inlet temperature adjustments, data center operators can optimize facility-level cooling and overall sustainability. Our case study shows that this approach can reduce total data center energy by 4-13% without sacrificing performance or reliability.
Chinese interpretation
研究问题：传统“更冷更好”假设在现代低压CPU上因ITD效应不再成立，导致数据中心能效未达最优。方法线索：基于生产Intel Xeon CPU的实证表征，结合Amazon与Equinix云平台测量数据，分析温度与效率的非单调关系，并提出ITD感知热分组方法。对AI数据中心/算电协同的意义：揭示约一半高功率CPU可通过适度提高运行温度提升效率，减少电网压力，支持可持续算电协同。核验边界：仅依据给定摘要中的ITD表征与平台测量，未涉及具体分组实现细节或额外实验结果。
Reference
Jason Crop, Hayden Moore, Sudeep Pasricha. Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation[J/OL]. (2026-06-10)[2026-06-13]. http://arxiv.org/abs/2606.11163v1.
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。太空丰富的太阳能与发射成本下降，使轨道数据中心成为AI算力扩展的潜在路径。真空环境虽利于冷却，但计算系统最终依赖辐射冷却，需大面积散热器，热管理限制对部署标准液冷/风冷计算机构成挑战。本文研究空间热约…
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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：空间平台热约束限制标准计算系统部署，需探索适合轨道AI算力的新架构。方法线索：分析GPU与CIM在辐射冷却下的热影响，构建散热器在环协同设计流程，评估不同加速器在空间环境中的可行性。对AI数据中心/算电协同的意义：为未来轨道数据中心提供热-算协同设计思路，减轻地面电网负担。核验边界：仅使用摘要中提及的GPU、CIM及辐射冷却约束，未补充具体性能数据或实验平台细节。
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)[2026-06-13]. http://arxiv.org/abs/2606.05741v1.
Recasting AI Data Centers as Engines for Carbon Removal
AI数据中心电力需求与CO2排放快速增长，同时产生持续低品位余热。本文评估该余热能否通过热泵升级驱动直接空气捕集（DAC），降低AI基础设施气候影响。开发热力集成DAC-AIDC系统，在美国多区域开展评估，考虑AIDC容量、服务器构成、当地气候、电价与电网碳强度…
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Recasting AI Data Centers as Engines for Carbon Removal
- Published
- 2026-05-13
- Authors
- No reliable data
- Theme
- 余热回收
Abstract
AI data centers (AIDCs) are rapidly increasing electricity demand and associated CO2 emissions, yet they also generate continuous low-grade waste heat. Here, we assess whether this heat can be upgraded by heat pumps to drive direct air capture (DAC) and reduce the climate impact of AI infrastructure. We develop a thermodynamically integrated DAC-AIDC system and conduct a region-resolved assessment across the United States, accounting for AIDC capacity, server composition, local climate, electricity prices, and grid carbon intensity. We find that AIDC waste heat can substantially improve net CO2 removal and lower the levelized cost of capture. In carbon-intensive regions, integration can flip DAC from net-positive to net-negative. Under a 2030 scenario with more GPU-intensive AIDCs and cleaner grids, several states achieve removal ratios above 1, indicating that integrated systems can offset their own operational emissions and deliver additional carbon removal.
Chinese interpretation
研究问题：AI数据中心高排放与余热如何转化为碳去除资源。方法线索：构建热力集成DAC-AIDC系统，进行美国区域分辨评估，纳入容量、气候、电价与碳强度因素。对AI数据中心/算电协同的意义：展示余热回收可降低碳影响，支持算电协同下的可持续运营。核验边界：仅依据摘要中的系统评估与情景描述，未添加具体数值或额外结论。
Reference
佚名. Recasting AI Data Centers as Engines for Carbon Removal[J/OL]. (2026-05-13)[2026-06-13]. http://arxiv.org/abs/2605.13114v1.
Power Grid Infrastructure for AI Data Centers
人工智能最新进展引发科技前沿竞相建设大型数据中心。本文探讨大型数据中心对电力电网规划与运行的影响。
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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：大型AI数据中心对电网规划与运行造成何种影响。方法线索：综述近期AI驱动的数据中心扩张及其电网交互。对AI数据中心/算电协同的意义：提供电网适应大规模算力需求的洞见，促进算电协同规划。核验边界：仅使用摘要中“规划与运行影响”的描述，未补充具体措施或数据。
Reference
Amir Sajadi, Muhy Eddin Za'ter, Maria Vabson, 等. Power Grid Infrastructure for AI Data Centers[J/OL]. (2026-05-31)[2026-06-13]. http://arxiv.org/abs/2606.00941v1.
Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster
AI数据中心电力供应已成为通向通用人工智能竞赛中的最大瓶颈，超越加速器可用性约束。本文首次描述超大规模AI数据中心端到端电力管理过程：从新一代加速器上市前6-12个月的早期电力规划，到大规模部署后的功率设置调优，再到面向演进工作负载的动态运行时功率管理。展示15…
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Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster
- Published
- 2026-05-23
- Authors
- Ehsan K. Ardestani, Leonardo Piga, Jovan Stojkovic, Pavan Balaji, Mustafa Ozdal, Mikel Jimenez Fernandez, Mihaela Dimovska, Luka Tadic
- Theme
- 芯片与算力
Abstract
The electric power supply for AI data centers is now the most significant bottleneck in the race toward Artificial General Intelligence, surpassing even the constraint of AI accelerator availability. To our knowledge, this paper is the first to describe the end-to-end power management process for a hyper-scale AI datacenter; from early power planning to accommodate next-generation accelerators 6--12 months before their general availability, to tuning power settings after large scale deployment, and finally to dynamic, runtime power management for evolving workloads. We present detailed power measurements for a 150 MW datacenter hosting a cluster of 83K GB200 GPUs. We share insights from building this state-of-the-art AI cluster. We hope this work encourages practitioners across the industry to share their own experiences as well.
Chinese interpretation
研究问题：AI数据中心电力供应瓶颈如何从规划延伸至运行时管理。方法线索：描述从早期规划、部署调优到动态运行时管理的全流程，并给出150MW集群实测数据。对AI数据中心/算电协同的意义：为超大规模AI集群电力管理提供实践参考，缓解电网压力。核验边界：仅依据摘要中的流程描述与集群规模，未添加具体测量数值或额外结论。
Reference
Ehsan K. Ardestani, Leonardo Piga, Jovan Stojkovic, 等. Provisioning to Runtime Optimization of a 100 MW-Scale AI Cluster[J/OL]. (2026-05-23)[2026-06-13]. http://arxiv.org/abs/2605.24461v2.
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载在秒级调整功率以吸收风光波动。对于多MW级AI/HPC设施，关键问题是如何将电网请求快速转化为设施电表处的GPU功率实际变化。GridPilot三层预测控制器跨毫秒、秒和小时尺度运行，辅以确定性安全…
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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：AI超级计算机如何在毫秒级实现电网请求到功率实际变化的快速响应。方法线索：开发三层预测控制器与安全岛旁路，在真实硬件上测量响应时间并校正PUE。对AI数据中心/算电协同的意义：展示AI设施可作为电网灵活资源，支持算电实时协同。核验边界：仅使用摘要中的响应时间与测试平台描述，未补充额外性能指标。
Reference
Denisa-Andreea Constantinescu, David Atienza. GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers[J/OL]. (2026-05-26)[2026-06-13]. http://arxiv.org/abs/2605.26384v1.
ScaleAcross Explorer: Exploring Communication Optimization for Scale-Acro…
大语言模型训练快速扩展需将GPU资源分布于多个数据中心建筑与区域，即“跨规模”训练。随着基础设施扩张，系统设计空间愈发复杂，涵盖新模型架构、硬件异构与演进通信模式。基于Meta生产经验，刻画跨数个容纳数十万GPU数据中心的部署复杂性。为加速探索并支持前沿模型高效…
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ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training
- Published
- 2026-05-23
- Authors
- Minghao Li, Alicia Golden, Samuel Hsia, Michael Kuchnik, Adi Gangidi, Xu Zhang, Ashmitha Jeevaraj Shetty, Zachary DeVito
- Theme
- 芯片与算力
Abstract
The rapid scaling of large language model training requires distributing GPU resources across multiple data center buildings and regions. We refer to such paradigm as "scale-across" training. As infrastructure expands, the system design space becomes increasingly intricate, encompassing new model architectures, hardware heterogeneity, and evolving communication patterns. Drawing from Meta's production experience, we highlight the complexities of deploying training jobs across a few data centers housing hundreds of thousands of GPUs. To accelerate exploration of the large design space and to enable efficient training for frontier model development, we conduct in-depth characterization of three key design dimensions: parallelism placement, parallelism scheduling, and network layer technologies. We then propose ScaleAcross Explorer, an optimizer that considers the interplay of design dimensions and holistically optimizes scale-across training. Testbed experiments and simulations demonstrate up to 64.62% training speedups over production configuration and up to 37.59% training speedups over the state-of-the-art baseline across a wide range of design points.
Chinese interpretation
研究问题：跨多数据中心的大规模AI训练面临复杂通信与设计挑战。方法线索：基于生产经验表征三维设计空间，开发整体优化器探索最优配置。对AI数据中心/算电协同的意义：提升跨区域算力利用效率，间接支持算电资源协同调度。核验边界：仅依据摘要中的设计维度与优化器描述，未添加实验结果。
Reference
Minghao Li, Alicia Golden, Samuel Hsia, 等. ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training[J/OL]. (2026-05-23)[2026-06-13]. http://arxiv.org/abs/2605.24326v1.
Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distilla…
大语言模型部署激增GPU需求与数据中心规模，引发电力使用、电网压力与AI工作负载影响担忧。蒸馏常被视为获得更高效模型的有效路径，但现有主张 rarely 计入完整端到端能耗与资源成本，包括教师端数据生成、logit缓存与评估等关键工作负载。本文提出综合能耗核算框…
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Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
- Published
- 2026-05-13
- Authors
- Katherine Lambert, Sasha Luccioni
- Theme
- 算电协同
Abstract
The rise in deployment of large language models has driven a surge in GPU demand and datacenter scaling, raising concerns about electricity use, grid stress, and the impacts of modern AI workloads. Distillation is often promoted as one of the most effective paths to obtain cheaper, more efficient models, yet these claims rarely account for the full end-to-end energy and resource costs, including crucial teacher-side workloads such as data generation, logit caching, and evaluation. We present a comprehensive energy accounting framework that measures the complete computational cost of distillation pipelines via detailed stage-wise tracking of GPU device power consumption. In our experiments, we separate and log empirical energy use across distinct phases and systematically measure the energy and emissions of two common distillation methods: the classic logit-based knowledge distillation and synthetic-data supervised fine-tuning, constructing energy-quality Pareto frontiers that expose the previously ignored costs. From these measurements and analyses, we derive practical design rules for selecting distillation methods and hyperparameters under energy and budget constraints, and release an open-source measurement harness and accounting protocol to provide a standardized foundation for comparable, reproducible distillation research, explicitly accountable for complete pipeline energy impact.
Chinese interpretation
研究问题：LLM蒸馏的端到端能耗是否被低估，需完整核算。方法线索：建立逐阶段GPU功耗跟踪框架，分离并记录经典logit蒸馏与合成数据微调的能耗。对AI数据中心/算电协同的意义：揭示蒸馏全流程能耗，支持更准确的算电影响评估。核验边界：仅使用摘要中的框架描述与两种方法，未补充具体能耗数值或前沿结果。
Reference
Katherine Lambert, Sasha Luccioni. Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines[J/OL]. (2026-05-13)[2026-06-13]. https://www.semanticscholar.org/paper/bccbd33ad04f7d4dbcd69559e7bc9715db08d0c5.
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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Data Center Power Crisis? Utility Veteran Explains What Developers Miss
Data Center Sales & Marketing Institute (DCSMI) · Query: AI datacenter power grid university lecture。Useful as technical or researc…
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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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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 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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AI 算力基础设施动态：NVIDIA Blog 发布相关报道（原文标题：NVIDIA Confidential Computing to Help…
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：Amazon data centers used 2.5bn …
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：Sentinel DC backs off paper mil…
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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：SpaceX IPO: Musk's firm set to …
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电力与能源约束观察：Data Center Dynamics 发布相关报道，涉及 1GW（原文标题：Jinko Power plots 1GW s…
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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电力与能源约束观察：Data Center Dynamics 发布相关报道（原文标题：Former printworks in northern …
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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：Overcoming data center power a…
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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：Building sustainable data cente…
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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：Proposal to build 640-acre data…
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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：Industry Groups Launch AI Dat…
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电力与能源约束观察：HPCwire 发布相关报道（原文标题：PNNL: Everything You Need to Know About Bui…
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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 4MW（原文标题：Private equity firm a…
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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财报与资本开支观察：The Register 发布相关报道，涉及 21%、$70（原文标题：Oracle's AI datacenter splu…
Same-source item from the Chinese report. Verify details against the original linked source: 财报与资本开支观察：The Register 发布相关报道，涉及 21%、$…
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投融资、财报或公司动态：HPCwire 发布相关报道（原文标题：Vertiv Completes Acquisition of ThermoKey…
Same-source item from the Chinese report. Verify details against the original linked source: 投融资、财报或公司动态：HPCwire 发布相关报道（原文标题：Vertiv…
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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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#HPCMatters - Asetek Liquid Cooling for HPC Data Centers
Asetek Inc · Query: high performance computing data center cooling workshop。Useful for product, market, or deployment context.
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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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Is AI Breaking the Data Center? | Tech Unscripted
Cisco · 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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AI Compute : Panel Securing the compute backbone
Economist Enterprise – Events · Query: AI infrastructure datacenter panel discussion。Useful for product, market, or deployment cont…
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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
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智算中心 CapEx/扩建
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4. Video signals
Data 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 YouTube#HPCMatters - Asetek Liquid Cooling for HPC Data Centers
技术研讨会 · Asetek Inc · Query: high performance computing data center cooling workshop
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 YouTubeIs AI Breaking the Data Center? | Tech Unscripted
专家圆桌 · Cisco · 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 YouTubeData Center Power Crisis? Utility Veteran Explains What Developers Miss
专家讲座 · Data Center Sales & Marketing Institute (DCSMI) · Query: AI datacenter power grid university lecture
Open on YouTubeAI Compute : Panel Securing the compute backbone
专家圆桌 · Economist Enterprise – Events · Query: AI infrastructure datacenter panel discussion
Open on YouTubeSources
Collection notes
- 公开 RSS/Atom：ServeTheHome：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 2026-06-13 21:33。
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- When optional automation services are unavailable, this page uses traceable public sources and conservative rule-based summaries only; unverifiable facts are not filled in.