Liquid Cooling and AI Data Center Daily | 2026-06-02
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 2026-06-02.
- Collection window: 2026-06-01 08:00 北京时间 - 2026-06-02 08:00 北京时间.
- Coverage snapshot: 8 industry items; 6 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, 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.
Battery-Assisted Operation of Hyperscale AI Data Centers under Connect-an…
Emerging connect-and-manage practices allow new transmission-connected mega-loads to connect while enforcing time-varying admissibl…
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Battery-Assisted Operation of Hyperscale AI Data Centers under Connect-and-Manage Interconnection Practices
- Published
- 2026-05-14
- Authors
- Xin Lu, Jing Qiu, Jiafeng Lin, Sihai An, Mingyang Sun, Junhua Zhao
- Theme
- 算电协同
Abstract
Emerging connect-and-manage practices allow new transmission-connected mega-loads to connect while enforcing time-varying admissible power exchange limits at the point of common coupling (PCC) in real time. Hyperscale artificial intelligence data centers (AIDCs), whose demand can reach hundreds of megawatts and whose internal computing-cooling dynamics evolve rapidly, can therefore face frequent conflicts between workload continuity requirements and externally imposed PCC envelopes. This paper proposes a battery-assisted operational framework in which on-site battery energy storage (BESS) serves as a physical buffering interface to reconcile fast internal dynamics with time-varying interconnection limits. A continuity-aware energy-computation model is developed to jointly capture checkpoint-constrained AI training workloads, information technology (IT) computing power-throughput characteristics, and IT-cooling thermal dynamics. A two-stage decision framework is then formulated, consisting of scenario-based day-ahead workload commitment and a real-time receding-horizon delivery assurance controller that enforces battery, thermal, and grid-interaction constraints. Case studies on the IEEE 39-bus system with Australian real data demonstrate that BESS substantially increases credible day-ahead workload commitment and improves real-time delivery robustness under transmission congestion. Sensitivity analyses further reveal a regime-dependent role transition of BESS -- from feasibility-oriented continuity support when PCC limits are binding to economy-driven flexibility provision as transmission constraints are relaxed.
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
Xin Lu, Jing Qiu, Jiafeng Lin, 等. Battery-Assisted Operation of Hyperscale AI Data Centers under Connect-and-Manage Interconnection Practices[J/OL]. (2026-05-14)[2026-06-02]. http://arxiv.org/abs/2605.14105v1.
Toward Communication-Efficient Space Data Centers: Bottlenecks, Architect…
The rapid growth of foundation model training and large-scale AI services has driven ground data centers toward unprecedented power…
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Toward Communication-Efficient Space Data Centers: Bottlenecks, Architectures, and New Paradigms
- Published
- 2026-05-13
- Authors
- Minghao Sun, Zehui Chen, Jinbo Hou, Kezhi Wang, Xiaoli Chu
- Theme
- 热管理与液冷
Abstract
The rapid growth of foundation model training and large-scale AI services has driven ground data centers toward unprecedented power densities, intensifying challenges in energy supply, cooling, and spatial scalability. Space Data Centers (SDCs) have emerged as a promising paradigm for hosting energy-intensive computing infrastructures in orbit, leveraging continuous solar energy and radiative cooling advantages. However, unlike ground facilities primarily constrained by power and site availability, SDCs are fundamentally limited by communication capability. The gap between petabit-scale internal data exchange in ground data centers and the gigabit-scale capacity of ground-space links forms a critical bottleneck. This article systematically analyzes communication constraints in SDC architectures and explores semantic communication as a key enabling paradigm. By transmitting compact, task-relevant semantic representations instead of raw data, uplink pressure can be substantially reduced. The feasibility of communication-efficient orbital AI infrastructures is demonstrated through the evaluation of a multi-layer heterogeneous SDC framework consisting of relay satellites and orbital computing nodes operating under coupled energy and thermal constraints. The article further outlines open research challenges toward scalable deployment.
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：研究重点指向冷却效率、能源利用或运维策略的改进方向。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
Minghao Sun, Zehui Chen, Jinbo Hou, 等. Toward Communication-Efficient Space Data Centers: Bottlenecks, Architectures, and New Paradigms[J/OL]. (2026-05-13)[2026-06-02]. http://arxiv.org/abs/2605.12681v1.
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production
LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. Th…
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Position: LLM Inference Should Be Evaluated as Energy-to-Token Production
- Published
- 2026-05-12
- Authors
- Xiang Liu, Shimiao Yuan, Zhenheng Tang, Peijie Dong, Kaiyong Zhao, Qiang Wang, Bo Li, Xiaowen Chu
- Theme
- 能效优化
Abstract
LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the relevant output is a quality-conditioned token produced under joint constraints from effective compute, delivered data-center power, cooling capacity, PUE, and utilization. We argue that the ML community should treat inference as \emph{energy-to-token production}. We formalize this view with a dimensionally consistent Token Production Function in which token rate is bounded by both compute-per-token and energy-per-token ceilings. Listed API prices vary by over an order of magnitude across providers, but we use price dispersion only as directional motivation, not as causal evidence of marginal cost. The core physical question is instead: under fixed quality and service targets, when does the binding constraint move from theoretical peak compute toward delivered power, cooling, and operational efficiency? Under this framing, system optimizations -- latent KV-cache compression, sparse or heavily compressed attention, quantization, routing, and difficulty-adaptive reasoning -- are not merely local engineering tricks. They are energy-to-token levers because they reduce FLOPs/token, joules/token, memory traffic, or utilization losses under fixed $(q^{*},s^{*})$. We therefore call for inference papers and benchmarks to report Joules/token, active binding constraint, PUE-adjusted delivered power, and utilization-adjusted token output alongside accuracy and latency.
Chinese interpretation
背景：AI 数据中心负载、功率密度和能源约束同步上升，PUE/WUE、能效指标和运营成本控制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，它可用于判断不同能效指标是否真实反映节能和成本收益。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Xiang Liu, Shimiao Yuan, Zhenheng Tang, 等. Position: LLM Inference Should Be Evaluated as Energy-to-Token Production[J/OL]. (2026-05-12)[2026-06-02]. http://arxiv.org/abs/2605.11733v1.
The Case for Space-Based Particle Colliders: Orbital Infrastructure as a …
The Standard Model of particle Physics has been validated to extraordinarily high precision by the Large Hadron Collider (LHC). Yet…
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The Case for Space-Based Particle Colliders: Orbital Infrastructure as a Path to Grand Unification Energy Scales
- Published
- 2026-05-07
- Authors
- Viktor Danchev, Alex Dyer, Sebastian Grau, Guillaume Vazeille
- Theme
- 热管理与液冷
Abstract
The Standard Model of particle Physics has been validated to extraordinarily high precision by the Large Hadron Collider (LHC). Yet it leaves some of the most fundamental questions in Physics unresolved: the nature of dark matter, the hierarchy problem, and the unification of forces. Multiple next-generation terrestrial colliders have been proposed such as the Future Circular Collider (FCC) which will reach centre-of-mass energies of $\approx$100 TeV, yet the energy scales at which hints of Grand Unified Theories (GUTs) and string theory are expected to be observed ($10^{11}-10^{13}$ TeV) remain orders of magnitude beyond the reach of any terrestrial facility. We argue that the path to these energy frontiers inevitably leads to Space. By examining the fundamental scaling law for circular proton colliders, we establish that colliders of radius $10^3-10^5$ km are required to enter the PeV-EeV regime. In addition, Space-based colliders benefit from virtually free ultra-high vacuum ($< 10^{10}$ particles/m$^3$ above 1000 km altitude), passive cryogenic cooling, reduction of geological and political constraints, and perhaps most importantly -- the substantial reduction of the thermodynamic penalty that dominates terrestrial cryogenic power budgets. We survey existing proposals for beyond-Earth colliders, derive order-of-magnitude requirements for an orbital collider constellation, and assess feasibility against current and near-term spacecraft capabilities in formation flying, power generation, and precision attitude control. We conclude that recent developments in orbital infrastructure -- particularly gigawatt-scale orbital power architectures being developed for Space-based data centers -- are converging with the needs of a Space-based mega collider, making serious feasibility studies warranted and promising a more certain path towards the core questions of modern Physics.
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：研究重点指向冷却效率、能源利用或运维策略的改进方向。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
Viktor Danchev, Alex Dyer, Sebastian Grau, 等. The Case for Space-Based Particle Colliders: Orbital Infrastructure as a Path to Grand Unification Energy Scales[J/OL]. (2026-05-07)[2026-06-02]. http://arxiv.org/abs/2605.08239v1.
A Scalable Digital Twin Framework for Energy Optimization in Data Centers
This study proposes a scalable Digital Twin framework for energy optimization in data centers.The framework integrates IoT-based da…
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A Scalable Digital Twin Framework for Energy Optimization in Data Centers
- Published
- 2026-05-07
- Authors
- Raphael Hendrigo de Souza Gonçalves, Wendel Marcos dos Santos
- Theme
- 能效优化
Abstract
This study proposes a scalable Digital Twin framework for energy optimization in data centers.The framework integrates IoT-based data acquisition, cloud computing, and machine learning techniques to enable real-time monitoring, forecasting, and intelligent energy management. A controlled small-scale data center environment was developed to monitor variables such as power consumption, temperature, and computational workload. Long Short-Term Memory (LSTM) models were employed to predict energy demand and support operational decision-making. Experimental results demonstrated improvements in energy efficiency, including reductions in power consumption and enhancements in Power Usage Effectiveness (PUE). Despite being evaluated in a constrained environment, the proposed framework demonstrates strong potential as a scalable and cost-effective solution for sustainable data center 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，PUE/WUE、能效指标和运营成本控制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，它可用于判断不同能效指标是否真实反映节能和成本收益。仍需结合全文实验条件、样本范围和成本假设核验。
Reference
Raphael Hendrigo de Souza Gonçalves, Wendel Marcos dos Santos. A Scalable Digital Twin Framework for Energy Optimization in Data Centers[J/OL]. (2026-05-07)[2026-06-02]. http://arxiv.org/abs/2605.05581v1.
Carbon-Aware Compute--Power Scheduling for AI Data Centers with Microgrid…
AI data centers are increasingly becoming tightly coupled compute--energy systems, where workload placement, cooling demand, electr…
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Carbon-Aware Compute--Power Scheduling for AI Data Centers with Microgrid Prosumer Operations
- Published
- 2026-05-05
- Authors
- Johnny R. Zhang, Gaoyuan Du, Qianyi Sun, Shiqi Wang, Jiaxuan Li, Xian Sun
- Theme
- 算电协同
Abstract
AI data centers are increasingly becoming tightly coupled compute--energy systems, where workload placement, cooling demand, electricity procurement, storage operation, and carbon emissions interact over time. This paper studies carbon-aware compute--power scheduling for geographically distributed AI data centers with microgrid prosumer capabilities. We propose a mixed-integer linear programming (MILP) framework that jointly schedules rigid training jobs, routes elastic inference workloads, dispatches local generation and battery storage, and manages bidirectional grid interaction under latency, continuity, power-balance, and carbon-budget constraints. The model captures two key features of emerging AI infrastructure: heterogeneous workload flexibility and site-level energy prosumer operation. Experiments on synthetic yet practically motivated instances show that the proposed joint MILP substantially improves total operational benefit over compute-only and energy-only baselines while reducing emissions. The results further indicate that inference-routing flexibility is a major source of value, battery storage provides useful temporal flexibility, and local-generation-rich settings are particularly favorable. The framework provides a tractable optimization abstraction for sustainable and grid-interactive AI data centers.
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
Johnny R. Zhang, Gaoyuan Du, Qianyi Sun, 等. Carbon-Aware Compute--Power Scheduling for AI Data Centers with Microgrid Prosumer Operations[J/OL]. (2026-05-05)[2026-06-02]. http://arxiv.org/abs/2605.03751v2.
Limiting the Impact of AI Data Centers on Fatigue Life of Thermal Turbine…
A framework is established that assesses the impact of variations in artificial intelligence (AI) data center (DC) loads on the fat…
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Limiting the Impact of AI Data Centers on Fatigue Life of Thermal Turbine Generators in the Grid: A Frequency-Domain Approach
- Published
- 2026-05-02
- Authors
- Fiaz Hossain, Nilanjan Ray Chaudhuri, Alok Sinha, Sai Gopal Vennelaganti, Mohammed E. Nassar
- Theme
- 算电协同
Abstract
A framework is established that assesses the impact of variations in artificial intelligence (AI) data center (DC) loads on the fatigue damage of steam/gas turbines of the synchronous generators (SGs) from torsional oscillations. Next, a simple three-step process that is supported by frequency-domain analysis is laid out to quantify the limits on fluctuations in AI DC loads. In the first step, the maximum allowable variation in electrical power output at each SG terminal is independently determined from the first principles. This step needs only a lumped multi-mass model of the mechanical side of the SG. In the second step, we propose a new approach that relies on load flow to determine the so-called algebraic `interaction factor' that maps the change in AI DC load at a given bus to the corresponding change in each of the SG power outputs. In the third step, we propose a screening method to rank the candidate buses to site AI DCs and solve an optimization problem to determine the optimal allowable fluctuations in the AI DCs. We demonstrate the applicability of the proposed approach through frequency-domain and time-domain analyses in the modified IEEE 4-machine and IEEE-68 bus systems using a dynamic phasor framework. Finally, we demonstrate the scalability of the proposed approach on the synthetic 2000-bus Texas system.
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
Fiaz Hossain, Nilanjan Ray Chaudhuri, Alok Sinha, 等. Limiting the Impact of AI Data Centers on Fatigue Life of Thermal Turbine Generators in the Grid: A Frequency-Domain Approach[J/OL]. (2026-05-02)[2026-06-02]. http://arxiv.org/abs/2605.01173v1.
The Hidden Cost of Thinking: Energy Use and Environmental Impact of LMs B…
Modern language model development extends far beyond pretraining, yet environmental reporting remains narrowly focused on the cost …
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The Hidden Cost of Thinking: Energy Use and Environmental Impact of LMs Beyond Pretraining
- Published
- 2026-05-02
- Authors
- Jacob Morrison, Noah A. Smith, Emma Strubell
- Theme
- 热管理与液冷
Abstract
Modern language model development extends far beyond pretraining, yet environmental reporting remains narrowly focused on the cost of training a single final model. In this work, we provide the first detailed breakdown of the environmental impact of a full model development pipeline, from pretraining through supervised fine-tuning, preference optimization, and reinforcement learning, for Olmo 3, a family of 7 billion and 32 billion parameter models in both instruction-following and reasoning variants. We find that reasoning models are 17x more expensive to post-train than their instruction-tuned counterparts in terms of datacenter energy, driven by reinforcement learning rollout generation. Development costs (including experimentation, failed runs, and ablations) account for 82.2% of total compute, a roughly 65% increase over the ~50% reported for pretraining-focused pipelines in prior work. In total, we estimate our model development process consumed ~12.3 GWh of datacenter energy, emitted 4,251 tCO2eq, and consumed 15,887 kL of water, with water consumption driven entirely by power generation infrastructure rather than data center cooling. These costs, which are almost entirely unreported by model developers, are growing rapidly as post-training pipelines become more complex, and must be accounted for in environmental reporting standards and by the research community working to reduce AI's environmental impact.
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：研究重点指向冷却效率、能源利用或运维策略的改进方向。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
Jacob Morrison, Noah A. Smith, Emma Strubell. The Hidden Cost of Thinking: Energy Use and Environmental Impact of LMs Beyond Pretraining[J/OL]. (2026-05-02)[2026-06-02]. http://arxiv.org/abs/2605.01158v1.
Enhanced geothermal for AI data centers: Devilish or divine? | James F. G…
TEDx Talks · Query: AI data center energy conference keynote。Useful as technical or research context.
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Panel Discussion: India’s Transition to Liquid Cooling for AI-Ready Data …
W.Media- South Asia & Middle East · Query: data center liquid cooling conference presentation。Useful as technical or research conte…
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Realizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
IEEE Standards Association · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
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Saving energy & increasing density in information processing using photon…
David Miller Science · Query: IEEE data center energy efficiency lecture。Useful as technical or research context.
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Webinar: Data Centre Liquid Cooling Technology
Park Place Technologies · Query: data center liquid cooling conference presentation。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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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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Industry
Industry news, products, policy, financing, projects, and market-oriented videos.
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：2,000-acre land parcel rezoned …
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，涉及 £1.2（原文标题：Police seize £1.2m wort…
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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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 250GW（原文标题：AI data center demand …
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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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Data decommissioning in data ce…
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，涉及 250MW（原文标题：BW Group signs deal 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，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 500MW（原文标题：Arcem buys land i…
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：Stockland files to develop anot…
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：The Register 发布相关报道（原文标题：Ohio hits pause on datacenter tax break…
Same-source item from the Chinese report. Verify details against the original linked source: 数据中心产业动态：The Register 发布相关报道（原文标题：Ohio…
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技术与产品进展：Data Center Dynamics 发布相关报道（原文标题：Environmental activist Erin Broc…
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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：Siemens, Nvidia, and Fluence d…
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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AI 算力基础设施动态：Data Center Dynamics 发布相关报道（原文标题：CoreWeave claims to have fir…
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 Dynamics 发布相关报…
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技术与产品进展：Data Center Knowledge 发布相关报道（原文标题：How the EPA’s New Rules Could S…
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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AI 算力基础设施动态：HPCwire 发布相关报道（原文标题：Supermicro Introduces DCBBS Blueprints fo…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：HPCwire 发布相关报道（原文标题：Superm…
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AI 算力基础设施动态：HPCwire 发布相关报道（原文标题：CoreWeave Completes Industry-First Bring-…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：HPCwire 发布相关报道（原文标题：CoreWe…
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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：Power and Permitting Are Redr…
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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AI 算力基础设施动态：HPCwire 发布相关报道（原文标题：DDN Unveils AI Data Intelligence Advances…
Same-source item from the Chinese report. Verify details against the original linked source: AI 算力基础设施动态：HPCwire 发布相关报道（原文标题：DDN Un…
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Major Changes to ASHRAE’s Fifth Edition of Thermal Guidelines: New Air-Co…
Upsite Technologies · Query: ASHRAE data center cooling webinar。Useful for product, market, or deployment context.
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Webinar ▶️ A Gamechanger: HPC Without the Datacentre
Asperitas · Query: high performance computing data center cooling workshop。Useful for product, market, or deployment context.
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[WEBINAR] For Most Data Centers, Liquid and Air Cooling Will Not be Mutua…
Upsite Technologies · Query: high performance computing data center cooling workshop。Useful for product, market, or deployment cont…
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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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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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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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4. Video signals
Enhanced geothermal for AI data centers: Devilish or divine? | James F. Groves | TEDxChantilly HS
学术会议报告 · TEDx Talks · Query: AI data center energy conference keynote
Open on YouTubeMajor Changes to ASHRAE’s Fifth Edition of Thermal Guidelines: New Air-Cooled Class for High Density
标准组织讲座 · Upsite Technologies · Query: ASHRAE data center cooling webinar
Open on YouTubePanel Discussion: India’s Transition to Liquid Cooling for AI-Ready Data Centers
学术会议报告 · W.Media- South Asia & Middle East · Query: data center liquid cooling conference presentation
Open on YouTubeRealizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
学术讲座 · IEEE Standards Association · Query: IEEE data center energy efficiency lecture
Open on YouTubeSaving energy & increasing density in information processing using photonics, David Miller, Stanford
学术讲座 · David Miller Science · Query: IEEE data center energy efficiency lecture
Open on YouTubeWebinar ▶️ A Gamechanger: HPC Without the Datacentre
技术研讨会 · Asperitas · Query: high performance computing data center cooling workshop
Open on YouTubeWebinar: Data Centre Liquid Cooling Technology
学术会议报告 · Park Place Technologies · Query: data center liquid cooling conference presentation
Open on YouTube[WEBINAR] For Most Data Centers, Liquid and Air Cooling Will Not be Mutually Exclusive
技术研讨会 · Upsite Technologies · Query: high performance computing data center cooling workshop
Open on YouTubeSources
Collection notes
- Semantic Scholar：未返回符合条件论文，已回退到 arXiv 公共接口。
- 公开 RSS/Atom：NVIDIA Blog：未检索到符合条件的高相关条目。
- arXiv：检索失败，原因：This operation was aborted
- 论文推荐：当日未形成新候选，按上一日排序池顺延补位。
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