液冷与智算中心日报｜2026-06-30
追踪液冷技术、AI 智算中心、数据中心能效、学术论文、产品发布、政策标准、投融资与供应链动态的每日中文报告。
1. 今日一句话总结
24小时内，资本继续加码智算中心，但电力、审批与能效约束已前置，液冷和算电协同正转为项目准入项。
从公开信号看，资本并未因为约束而降温，资本开支仍向AI数据中心与液冷环节集中，说明头部厂商和基础设施资本仍在前置锁定园区、容量和交付窗口；但与此同时，扩建继续推进，但电力、选址审批与能源获取仍是主约束，意味着行业竞争的关键变量已不再只是“拿到多少 GPU”，而是“能否把 GPU 放进一个可并网、可散热、可控成本、可持续运行的系统”。技术侧技术侧继续围绕高带宽互连与服务器能效优化，论文侧论文侧继续指向算电协同、液冷优化与能效度量重构，共同指向同一个趋势：单点器件优化的边际价值在下降，网络、供电、储能、液冷和调度软件的系统级协同正在上升为真正的产能约束。对产业链而言，未来更稀缺的不是单一硬件，而是把算力、热管理和能源调度耦合起来的工程交付能力。
学术与产业速览
将论文、视频、产业动态和政策项压缩为可快速扫描的标签；每个标签只保留题目、摘要和来源入口。
学术
论文、研究趋势、学术视频与方法论线索。
Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computatio…
Energy-intensive data centers (DCs) have emerged as substantial and flexible loads in modern power systems, underscoring the critic…
展开全文
Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computation-Electricity Coordination
- 发布时间
- 2026-06-03
- 作者
- Yugui Liu、Yibo Ding、Xudong Li、Jing Qu、Wenyi Zhang、Tong Qian、Wuyou Xiao、Zhengyang Hu
- 主题
- 算电协同
摘要
Energy-intensive data centers (DCs) have emerged as substantial and flexible loads in modern power systems, underscoring the critical need for computation-electricity coordination. Harnessing the spatio-temporal flexibility of DC workloads is a promising approach to facilitate this coordination. However, existing studies overlook the collaborative potential of computational resource sharing among geo-distributed DCs, thereby failing to fully unlock this flexibility. In this paper, a bi-level computation-electricity coordination framework is proposed to explicitly capture the bidirectional interactions between DCs and power grid. Firstly, a peer-to-peer cloud service market (P2P-CSM) for geo-distributed DCs is proposed, which enables bilateral cloud service transactions to leverage regional heterogeneities (e.g., electricity prices, cooling efficiency). Secondly, locational marginal prices are embedded into the framework to reflect network congestion and nodal price disparities. Thirdly, a dual consensus alternating direction method of multipliers (ADMM)-based decentralized algorithm is developed as the P2P market clearing algorithm, and a bisection-assisted iterative algorithm is proposed to ensure rigorous convergence of the framework. Case studies conducted on modified IEEE 30-bus system validate that the P2P-CSM achieves a win-win computation-electricity coordination: it not only increases total DC operational profit by 22.8\%, but also effectively alleviates grid congestion and yields a 3.2\% reduction in total energy consumption.
中文解读
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。
参考文献
Yugui Liu, Yibo Ding, Xudong Li, 等. Peer-to-Peer Cloud Service Market for Data Centers Oriented to Computation-Electricity Coordination[J/OL]. (2026-06-03)[2026-06-30]. http://arxiv.org/abs/2606.04981v1.
Maximizing Compute Capacity in AI Data Centers through Cooling, Energy St…
The deployment of artificial intelligence is increasingly constrained by limited site-level power capacity, which must support both…
展开全文
Maximizing Compute Capacity in AI Data Centers through Cooling, Energy Storage, and Computing Adaptation
- 发布时间
- 2026-05-30
- 作者
- Shaolei Ren、Mohammad A. Islam、Adam Wierman
- 主题
- 热管理与液冷
摘要
The deployment of artificial intelligence is increasingly constrained by limited site-level power capacity, which must support both compute systems and non-compute systems (primarily cooling) at all times. Cooling power demand, especially in non-evaporative cooling systems, can increase substantially with ambient temperature in the summer, producing recurring periods of elevated cooling power that often lasts for multiple hours per day. Therefore, maximizing compute capacity under a limited site-level power budget is an important planning and operational challenge. Sizing the compute system conservatively based on peak cooling power can leave part of the site-level power capacity underutilized when the cooling power is below its peak, particularly in cooler months. On the other hand, sizing the compute system aggressively based on low cooling power can cause the total site-level power demand to exceed the site-level power capacity during hot days in the summer. This paper proposes ComputeAmp (Compute Amplifier), a framework that maximizes the compute capacity by jointly and dynamically leveraging cooling, battery energy storage, and computing-based adaptation. We discuss the opportunities and limitations of ComputeAmp and illustrate its potential to significantly expand usable compute capacity within local power and water resource limits. We also present a problem formulation for ComputeAmp and highlight a few algorithmic and operational challenges.
中文解读
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。
参考文献
Shaolei Ren, Mohammad A. Islam, Adam Wierman. Maximizing Compute Capacity in AI Data Centers through Cooling, Energy Storage, and Computing Adaptation[J/OL]. (2026-05-30)[2026-06-30]. http://arxiv.org/abs/2606.00457v1.
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…
展开全文
Hot AI in Cold Space: Thermal-Crosstalk-Aware Scheduling for Sustainable Orbital AI Clusters
- 发布时间
- 2026-06-23
- 作者
- Shuyi Chen、Zhengchang Hua、Nikos Tziritas、Georgios Theodoropoulos
- 主题
- AI 运维优化
摘要
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.
中文解读
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。
参考文献
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)[2026-06-30]. http://arxiv.org/abs/2606.26150v1.
Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for…
As data center energy demand approaches grid-level constraints, optimizing conventional server infrastructure is essential for sust…
展开全文
Revisiting "Cooler is Better": ITD-Aware Per-CPU Thermal Optimization for Sustainable Data Center Operation
- 发布时间
- 2026-06-10
- 作者
- Jason Crop、Hayden Moore、Sudeep Pasricha
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
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)[2026-06-30]. http://arxiv.org/abs/2606.11163v1.
Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructu…
Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debate…
展开全文
Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa
- 发布时间
- 2026-06-24
- 作者
- Kai-Hsin Hung、Sumaya Nur Adan、Krupa Suchak、Armita Sadeghian Barzoki、Kofi Yeboah、Mohammad Amir Anwar
- 主题
- 热管理与液冷
摘要
Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debates treat compute primarily as a technical input rather than as an outcome of investment, ownership, and financial control. This paper examines AI infrastructure investment flows across Africa through a systematic analysis of 46 publicly announced projects totalling USD $12.7 billion between 2019 and 2025. Using a value chain framework, we analyze who invests in AI-relevant infrastructure and where investments concentrate. Our findings reveal a highly concentrated landscape dominated by global data center operators, hyperscale technology firms, and development finance institutions, clustering in South Africa, Kenya, Nigeria, and Egypt. We introduce asymmetrical interdependence to describe a structural condition in which capital and physical infrastructure account for 73% of total funding while control remains concentrated in the compute layer among a small number of global technology firms. We argue that compute governance must account for capital flows, ownership, and control, not only geographic access, because these dynamics shape AI compute equity. Infrastructure presence is necessary but insufficient for meaningful governance capacity.
中文解读
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。
参考文献
Kai-Hsin Hung, Sumaya Nur Adan, Krupa Suchak, 等. Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa[J/OL]. (2026-06-24)[2026-06-30]. http://arxiv.org/abs/2606.28404v1.
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 …
展开全文
Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
- 发布时间
- 2026-06-24
- 作者
- Chris Williams、Philip Colangelo、Ayse Coskun、Ethan Levine、Andy Neale、Ciaran Roberts、Shayan Sengupta、Nikhil Shirolkar
- 主题
- 算电协同
摘要
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.
中文解读
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。
参考文献
Chris Williams, Philip Colangelo, Ayse Coskun, 等. Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute[J/OL]. (2026-06-24)[2026-06-30]. http://arxiv.org/abs/2606.25098v1.
AI Data Centers and the Water Use Feedback Loop
AI data centres consume water for cooling, water scarcity constrains siting, and AI tools can improve water system efficiency. Thes…
展开全文
AI Data Centers and the Water Use Feedback Loop
- 发布时间
- 2026-06-20
- 作者
- Basit A. Akinade、Amobichukwu C. Amanambu、Jonathan M. Frame、Shaolei Ren
- 主题
- 热管理与液冷
摘要
AI data centres consume water for cooling, water scarcity constrains siting, and AI tools can improve water system efficiency. These dynamics are studied separately yet form a feedback loop. This review formalises the Water and AI Feedback Loop, introduces the Water Consumption Impact index to quantify community-scale utility burden, and demonstrates across ten US sites that burden spans three orders of magnitude, from 0.2% to 134% of host capacity.
中文解读
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。
参考文献
Basit A. Akinade, Amobichukwu C. Amanambu, Jonathan M. Frame, 等. AI Data Centers and the Water Use Feedback Loop[J/OL]. (2026-06-20)[2026-06-30]. http://arxiv.org/abs/2606.21760v1.
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…
展开全文
Toward Next-Generation AI Data Centers: Power Delivery Architecture Shifts, Emerging Technologies, and Challenges
- 发布时间
- 2026-06-24
- 作者
- Sangwhee Lee、Rafal P. Wojda、Cheol-Hee Jo、Shuntaro Inoue、Pedro Ribeiro、Gui-Jia Su、Mostak Mohammad、Himel Barua
- 主题
- 热管理与液冷
摘要
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.
中文解读
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。
参考文献
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)[2026-06-30]. http://arxiv.org/abs/2606.25095v1.
Rolls-Royce’s Vittorio Pierangeli: Solving the AI Power Crisis : Data Cen…
Data Centre Magazine · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
The AI Infrastructure Utility | Wade Vinson, NVIDIA | DCAC Live 2025 Keyn…
Data Center Anti-Conference · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
Webinar: Data Centre Liquid Cooling Technology
Park Place Technologies · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
展开全文
Competitive Online Peak-Demand Minimization using Energy Storage
Cambridge Energy and Environment Group · 检索词：ACM SIGEnergy data center energy talk。适合作为技术背景或研究趋势补充。
展开全文
Data Center Leaders on Building AI’s Infrastructure
Bloomberg Live · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
展开全文
智算中心 CapEx/扩建
本期命中 16 条，热度分 54。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
电力并网与能源约束
本期命中 12 条，热度分 33。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
NVIDIA Blackwell/GB200/GB300
本期命中 2 条，热度分 8。可作为论文检索、技术路线和后续研究跟踪关键词。
展开全文
产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
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…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
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…
发布时间：2026-06-24；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Kyivstar announces plans for AI…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Lawsuit filed to lift data cent…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：900-acre data center could be b…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：First data center built inside …
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 $10bn、100MW（原文标题：Crusoe to invest…
发布时间：2026-06-30；检索窗口内；可核验指标：$10bn、100MW；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
智算中心/数据中心建设进展：Data Center Dynamics 发布相关报道，涉及 1.5GW（原文标题：1.5GW data center…
发布时间：2026-06-30；检索窗口内；可核验指标：1.5GW；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
电力与能源约束观察：Data Center Dynamics 发布相关报道（原文标题：DCD Studio: Balancing rapid AI…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
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：Data center developer Blac…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
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…
发布时间：2026-06-27；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
技术与产品进展：Data Center Knowledge 发布相关报道（原文标题：Rack-Based Environmental Monito…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
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…
发布时间：2026-06-26；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
电力与能源约束观察：HPCwire 发布相关报道（原文标题：NTT Global Data Centers Report Reveals What…
发布时间：2026-06-30；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
展开全文
Webinar ▶️ A Gamechanger: HPC Without the Datacentre
Asperitas · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
展开全文
2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
Condair USA/CA · 检索词：ASHRAE data center cooling webinar。用于补充产业、产品或工程部署观察。
展开全文
ASHRAE Ireland Technical Webinar - Efficiency in Data Center's Cooling Sy…
ASHRAE Ireland · 检索词：ASHRAE data center cooling webinar。用于补充产业、产品或工程部署观察。
展开全文
产业热度指数 10/10
产业热度指数为 10/10：本期自动化检索记录到 22 条候选条目，指数按候选条目数量、来源可信度和栏目覆盖度保守计算。
展开全文
NVIDIA Blackwell/GB200/GB300
今日延续上榜
展开全文
AI 芯片供给与交付
今日延续上榜
展开全文
智算中心 CapEx/扩建
今日延续上榜
展开全文
4. 最新视频观察
Rolls-Royce’s Vittorio Pierangeli: Solving the AI Power Crisis : Data Centre LIVE 2026
学术会议报告 · Data Centre Magazine · 检索词：AI data center energy conference keynote
在 YouTube 打开The AI Infrastructure Utility | Wade Vinson, NVIDIA | DCAC Live 2025 Keynote
学术会议报告 · Data Center Anti-Conference · 检索词：AI data center energy conference keynote
在 YouTube 打开Webinar ▶️ A Gamechanger: HPC Without the Datacentre
技术研讨会 · Asperitas · 检索词：high performance computing data center cooling workshop
在 YouTube 打开Webinar: Data Centre Liquid Cooling Technology
学术会议报告 · Park Place Technologies · 检索词：data center liquid cooling conference presentation
在 YouTube 打开2024 ASHRAE Webinar: Adiabatic Solutions for Data Centers
标准组织讲座 · Condair USA/CA · 检索词：ASHRAE data center cooling webinar
在 YouTube 打开ASHRAE Ireland Technical Webinar - Efficiency in Data Center's Cooling System - How To?
标准组织讲座 · ASHRAE Ireland · 检索词：ASHRAE data center cooling webinar
在 YouTube 打开Competitive Online Peak-Demand Minimization using Energy Storage
学术讲座 · Cambridge Energy and Environment Group · 检索词：ACM SIGEnergy data center energy talk
在 YouTube 打开Data Center Leaders on Building AI’s Infrastructure
学术会议报告 · Bloomberg Live · 检索词：AI data center energy conference keynote
在 YouTube 打开来源链接区
本次检索说明
- 当前自动化环境未配置 Tavily、Bing News 或 SerpAPI 检索密钥；脚本将使用公开 RSS/Atom、公共 arXiv 接口与固定监测源，不会编造产业新闻。
- 论文池：已从本地论文池读取 22 条候选；池更新时间 2026-06-30 13:31。
- x.ai 论文解读：文本生成失败，已回退到规则化论文摘要；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 1 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 2 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 3 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 4 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 5 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 6 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 7 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 8 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- AI 分析：x.ai 调用失败，已回退到规则化模板；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit…