液冷与智算中心日报｜2026-06-04
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，电力并网与能源约束、AI 芯片供给与交付、PUE/WUE 与能效优化仍是主线，但基础设施约束已前置，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Frontier液冷数字孪生研究指向35.48%年冷却节能，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。
GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
At global scale, data-center electricity demand is growing faster than the grids that supply it, while system operators increasingl…
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GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers
- 发布时间
- 2026-05-26
- 作者
- Denisa-Andreea Constantinescu、David Atienza
- 主题
- 算电协同
摘要
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.
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：摘要显示作者采用实验验证、原型测试或测量对比，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
Denisa-Andreea Constantinescu, David Atienza. GridPilot: Real-Time Grid-Responsive Control for AI Supercomputers[J/OL]. (2026-05-26)[2026-06-04]. http://arxiv.org/abs/2605.26384v1.
Energy-Aware Computing in the Year 2026
High-Performance Computing (HPC) has recently entered the Exascale era, and considerable efforts are being made to fully harness th…
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Energy-Aware Computing in the Year 2026
- 发布时间
- 2026-05-23
- 作者
- Roblex Nana Tchakoute、Claude Tadonki
- 主题
- AI 运维优化
摘要
High-Performance Computing (HPC) has recently entered the Exascale era, and considerable efforts are being made to fully harness this potential power for large-scale applications, such as cutting-edge generative AI (training and exploitation). The corresponding energy consumption is very high, and forecasts are alarming, making this metric a critical systemic bottleneck. Addressing this issue presents a genuine challenge for the entire cloud-edge-HPC continuum at all scales, from low-power IoT microcontrollers to multi-megawatt data centers. Beyond financial costs, green computing is driven by considerations related to climate change and environmental concerns such as carbon footprint ($CO_2e$), as well as constraints on energy production and supply, leading to a real need to regulate {\em information and communication technology} (ICT) activities. This article presents a comprehensive overview of energy-efficient computing, taking into account the most recent and significant contributions. Based on this exploration of the state of the art, we design and describe a holistic taxonomy of the aforementioned publications, structured around various perspectives, including {\em hardware and software aspects, measurement instrumentation, software optimizations, dynamic task scheduling, voltage scaling, workload consolidation, federated learning}, and {\em cooling}. Particular emphasis is placed on large-scale AI, which receives significant attention due to its considerable resource requirements. We conclude with an analysis of a forward-looking roadmap that considers the main perspectives of sustainable computing.
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
Roblex Nana Tchakoute, Claude Tadonki. Energy-Aware Computing in the Year 2026[J/OL]. (2026-05-23)[2026-06-04]. http://arxiv.org/abs/2605.24569v1.
ScaleAcross Explorer: Exploring Communication Optimization for Scale-Acro…
The rapid scaling of large language model training requires distributing GPU resources across multiple data center buildings and re…
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ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training
- 发布时间
- 2026-05-23
- 作者
- Minghao Li、Alicia Golden、Samuel Hsia、Michael Kuchnik、Adi Gangidi、Xu Zhang、Ashmitha Jeevaraj Shetty、Zachary DeVito
- 主题
- 芯片与算力
摘要
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.
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，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。
参考文献
Minghao Li, Alicia Golden, Samuel Hsia, 等. ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training[J/OL]. (2026-05-23)[2026-06-04]. http://arxiv.org/abs/2605.24326v1.
Co-Design Optimization for Data Center Cooling System via Digital Twin
Liquid-cooled exascale supercomputers dissipate heat through cooling plants organized as multiple parallel subloops, but how to all…
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Co-Design Optimization for Data Center Cooling System via Digital Twin
- 发布时间
- 2026-05-15
- 作者
- Shrenik Jadhav、Zheng Liu
- 主题
- 热管理与液冷
摘要
Liquid-cooled exascale supercomputers dissipate heat through cooling plants organized as multiple parallel subloops, but how to allocate coolant distribution units (CDUs) across subloops and how to distribute flow among them has not been systematically addressed for facilities at this scale. This paper presents a three-layer optimization framework that jointly determines the integer partition of CDUs across subloops, the continuous flow fraction allocation, and the per-timestep co-design optimization of total flow rate and supply temperature subject to per-subloop thermal safety constraints. The Modelica simulation model is built based on the data of Frontier exascale supercomputer at Oak Ridge National Laboratory. By developing a reduced-order surrogate model, all 611 feasible partitions of 25 CDUs are evaluated across the full year operational dataset of 49,353 timesteps. Three progressively richer operational strategies are compared, ranging from flow control optimization to full three-layer co-design optimization with dynamically adjusted flow fractions. The globally optimal design is a two-subloop plant achieving 35.48% annual cooling energy savings, only 0.18% above the current three-subloop Frontier design at 35.30%. Flow fraction optimization is shown to compensate for any feasible CDU-to-subloop assignment, reducing the design sensitivity by 93% and providing a low-cost software-only pathway to near-optimal performance on the existing Frontier hardware. The framework is transferable to other liquid-cooled high-performance computing plants.
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
Shrenik Jadhav, Zheng Liu. Co-Design Optimization for Data Center Cooling System via Digital Twin[J/OL]. (2026-05-15)[2026-06-04]. http://arxiv.org/abs/2605.15516v1.
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
- 发布时间
- 2026-05-14
- 作者
- Xin Lu、Jing Qiu、Jiafeng Lin、Sihai An、Mingyang Sun、Junhua Zhao
- 主题
- 算电协同
摘要
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.
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
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)[2026-06-04]. 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
- 发布时间
- 2026-05-13
- 作者
- Minghao Sun、Zehui Chen、Jinbo Hou、Kezhi Wang、Xiaoli Chu
- 主题
- 热管理与液冷
摘要
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.
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
Minghao Sun, Zehui Chen, Jinbo Hou, 等. Toward Communication-Efficient Space Data Centers: Bottlenecks, Architectures, and New Paradigms[J/OL]. (2026-05-13)[2026-06-04]. 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
- 发布时间
- 2026-05-12
- 作者
- Xiang Liu、Shimiao Yuan、Zhenheng Tang、Peijie Dong、Kaiyong Zhao、Qiang Wang、Bo Li、Xiaowen Chu
- 主题
- 能效优化
摘要
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.
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，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。
参考文献
Xiang Liu, Shimiao Yuan, Zhenheng Tang, 等. Position: LLM Inference Should Be Evaluated as Energy-to-Token Production[J/OL]. (2026-05-12)[2026-06-04]. 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
- 发布时间
- 2026-05-07
- 作者
- Viktor Danchev、Alex Dyer、Sebastian Grau、Guillaume Vazeille
- 主题
- 热管理与液冷
摘要
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.
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
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)[2026-06-04]. http://arxiv.org/abs/2605.08239v1.
DLS with Keren Bergmann: Scaling Energy-Efficient AI Systems Performance …
MPI for the Science of Light · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
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Enhanced geothermal for AI data centers: Devilish or divine? | James F. G…
TEDx Talks · 检索词：AI data center energy conference keynote。适合作为技术背景或研究趋势补充。
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IEEE Data & Storage Summit Day 1, November 18 2020
IEEE Bangalore section · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
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Panel Discussion: India’s Transition to Liquid Cooling for AI-Ready Data …
W.Media- South Asia & Middle East · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
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Realizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
IEEE Standards Association · 检索词：IEEE data center energy efficiency lecture。适合作为技术背景或研究趋势补充。
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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。
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电力并网与能源约束
本期命中 6 条，热度分 18。可作为论文检索、技术路线和后续研究跟踪关键词。
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AI 芯片供给与交付
本期命中 2 条，热度分 6。可作为论文检索、技术路线和后续研究跟踪关键词。
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PUE/WUE 与能效优化
本期命中 2 条，热度分 6。可作为论文检索、技术路线和后续研究跟踪关键词。
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产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
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。
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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。
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产业热度指数 6/10
产业热度指数为 6/10：本期自动化检索记录到 8 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。
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NVIDIA Blackwell/GB200/GB300
昨日热度高，今日暂无新增高可信条目
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AI 芯片供给与交付
今日延续上榜
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智算中心 CapEx/扩建
昨日热度高，今日暂无新增高可信条目
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4. 最新视频观察
DLS with Keren Bergmann: Scaling Energy-Efficient AI Systems Performance with Photonic Connectivity
学术讲座 · MPI for the Science of Light · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Enhanced geothermal for AI data centers: Devilish or divine? | James F. Groves | TEDxChantilly HS
学术会议报告 · TEDx Talks · 检索词：AI data center energy conference keynote
在 YouTube 打开IEEE Data & Storage Summit Day 1, November 18 2020
学术讲座 · IEEE Bangalore section · 检索词：IEEE data center energy efficiency lecture
在 YouTube 打开Panel Discussion: India’s Transition to Liquid Cooling for AI-Ready Data Centers
学术会议报告 · W.Media- South Asia & Middle East · 检索词：data center liquid cooling conference presentation
在 YouTube 打开Realizing Asymmetric Datarates via Energy Efficient Ethernet (EEE)
学术讲座 · IEEE Standards Association · 检索词：IEEE data center energy efficiency lecture
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。
- Semantic Scholar：未配置 SEMANTIC_SCHOLAR_API_KEY，本期未调用。
- Semantic Scholar：未返回符合条件论文，已回退到 arXiv 公共接口。
- 公开 RSS/Atom：Data Center Dynamics：检索失败，原因：fetch failed
- 公开 RSS/Atom：The Register：检索失败，原因：fetch failed
- 公开 RSS/Atom：ServeTheHome：检索失败，原因：fetch failed
- 公开 RSS/Atom：Data Center Knowledge：检索失败，原因：fetch failed
- 公开 RSS/Atom：HPCwire：检索失败，原因：fetch failed
- 公开 RSS/Atom：NVIDIA Blog：检索失败，原因：fetch failed
- YouTube：检索失败，原因：fetch failed
- arXiv：检索失败，原因：fetch failed
- 论文推荐：当日未形成新候选，按上一日排序池顺延补位。
- 视频推荐：当日未形成新候选，按上一日排序池顺延补位。