Research Article芯片与算力
Hyunsoo Lee、Panggah Prabawa、Dae-Hyun Choi、Joongheon Kim
Published 2026-07-03 · arXiv · Credibility S
Eco-friendly energy management for artificial intelligence data centers (AIDCs) is crucial because of the significant increase in energy consumption-induced carbon emissions from AIDCs resulting from the rapid expansion of AI applications. This paper proposes a hierarchical carbon-aware multi-agent reinforcement learning (CA-MARL) framework for robust and efficient operations of AIDCs under uncertainties while ensur…
Abstract, interpretation and reference
Abstract
Eco-friendly energy management for artificial intelligence data centers (AIDCs) is crucial because of the significant increase in energy consumption-induced carbon emissions from AIDCs resulting from the rapid expansion of AI applications. This paper proposes a hierarchical carbon-aware multi-agent reinforcement learning (CA-MARL) framework for robust and efficient operations of AIDCs under uncertainties while ensuring low-carbon operation of power distribution systems. The framework comprises a workload manager (WM) agent and multiple local AIDC agents trained using a multi-agent transformer method, corresponding to a global AIDC aggregator and a local AIDC operator, respectively. Leveraging AIDC operation data along with nodal carbon intensity (NCI) calculated from the carbon emission flow-integrated distribution system operator problem, the WM agent spatially allocates AI training and inference jobs among all AIDCs. Based on the jobs allocated from the WM agent and NCI information, each AIDC agent schedules economical and eco-friendly operations of the AIDC by performing the following tasks: i) temporal shifting of training jobs, ii) spatial allocation of training graphics processing unit (GPU) blocks and inference GPUs within the AIDC, and iii) control of the supply air temperature of the cooling system. The effectiveness of the proposed framework was assessed using an IEEE 33-node power distribution system.
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
Hyunsoo Lee, Panggah Prabawa, Dae-Hyun Choi, 等. Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems[J/OL]. (2026-07-03)[2026-07-09]. http://arxiv.org/abs/2607.03324v2.
Research Article算电协同
Xinyi Wu、Siyuan Liu、Ali Jadbabaie
Published 2026-07-09 · arXiv · Credibility S
Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly. We study what determines this frequency usage and propose a data-centered explanation: RoPE frequencies are selected to match the relative-distance structure of the training data. Viewing each frequency as a positional lens, we formalize a field-resolution…
Abstract, interpretation and reference
Abstract
Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly. We study what determines this frequency usage and propose a data-centered explanation: RoPE frequencies are selected to match the relative-distance structure of the training data. Viewing each frequency as a positional lens, we formalize a field-resolution tradeoff and show that, for a data-induced dependency profile of width $W$, the optimal frequency scales as $1/W$. This frequency-matching principle explains controlled observations on synthetic and text-based data, and suggests that the mid-low frequency bands observed in language models arise from the multi-scale dependency structure of natural language. We further connect frequency selection to position-interpolation-based length generalization: scaling frequencies down expands the effective field while reducing resolution. This helps when longer-context dependencies are approximate dilations of those seen during training, but can fail when relevant dependencies do not scale with context length. Empirically, we show that natural language exhibits approximate self-similarity across positional scales, explaining why test-time frequency scaling can support long-context generalization. Overall, our results identify a data-driven mechanism behind emergent RoPE frequency usage and show that long-context generalization depends on two forms of scale matching: between learned frequencies and training-time dependencies, and between frequency scaling and how those dependencies extend to longer contexts.
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：摘要显示作者采用仿真建模和情景分析，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
Xinyi Wu, Siyuan Liu, Ali Jadbabaie. How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization[J/OL]. (2026-07-09)[2026-07-09]. http://arxiv.org/abs/2607.07678v1.
Research ArticleAI 运维优化
Abanish Tiwari、Chandan Chaudhary、Yansong Pei、Mohammed Ben-Idris、Joydeep Mitra
Published 2026-07-06 · arXiv · Credibility S
Hyperscale data centers and other large concentrated loads can impose substantial new demand on existing transmission networks. If import corridors lack sufficient transfer capability, operators may need to curtail load, delay interconnection, or reinforce the network to maintain reliable service. An energy storage system (ESS) deployed as a storage-as-transmission asset (SATA) offers a non-wires alternative by prov…
Abstract, interpretation and reference
Abstract
Hyperscale data centers and other large concentrated loads can impose substantial new demand on existing transmission networks. If import corridors lack sufficient transfer capability, operators may need to curtail load, delay interconnection, or reinforce the network to maintain reliable service. An energy storage system (ESS) deployed as a storage-as-transmission asset (SATA) offers a non-wires alternative by providing operator-directed support to constrained import corridors. However, the operating-level reliability value of SATA dispatch remains insufficiently quantified. This paper evaluates operator-directed SATA using a day-ahead DC optimal power flow that co-optimizes generation, ESS dispatch, and load curtailment across Monte Carlo scenarios of demand and generator availability. Operating reliability is assessed using expected energy not served (EENS), loss-of-load hours (LOLH), and the conditional value at risk (CVaR) of daily unserved energy. Congestion-price and flow-sensitivity metrics are used to identify the limiting corridor and storage location. The interconnection is then screened to determine whether SATA is suitable, reinforcement is required, or storage would provide little transmission value. Results show that operator-directed SATA reduces average unserved energy, loss-of-load exposure, and tail risk compared with deploying the same ESS for pure arbitrage. These results demonstrate that the operating designation of storage is a primary driver of its transmission value.
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：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向能效评价口径、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
Abanish Tiwari, Chandan Chaudhary, Yansong Pei, 等. Storage as a Transmission Asset (SATA) for Large-Load Congestion Relief[J/OL]. (2026-07-06)[2026-07-09]. http://arxiv.org/abs/2607.04545v1.
Research Article热管理与液冷
Kai-Hsin Hung、Sumaya Nur Adan、Krupa Suchak、Armita Sadeghian Barzoki、Kofi Yeboah、Mohammad Amir Anwar
Published 2026-06-24 · arXiv · Credibility S
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 betwe…
Abstract, interpretation and reference
Abstract
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)[2026-07-09]. http://arxiv.org/abs/2606.28404v1.
Research Article芯片与算力
Mauricio Fadel Argerich、Jonathan Fürst、Marta Patiño-Martínez
Published 2026-07-03 · arXiv · Credibility S
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination. While some predictive models exist, they still require profiling data and struggle to generalize to hardware unseen d…
Abstract, interpretation and reference
Abstract
Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination. While some predictive models exist, they still require profiling data and struggle to generalize to hardware unseen during training. To address this, we introduce \textit{WattGPU}, featuring two predictive models for mean GPU power draw and Inter-Token Latency (ITL). Our approach leverages only publicly available LLM metadata and GPU specifications, eliminating the need for hardware access or profiling while enabling generalization to unseen NVIDIA server-grade GPUs and LLMs. We evaluate our models using rigorous leave-one-GPU-out and leave-one-LLM-out cross-validation on a dataset of 42 open-source LLMs (0.1B--27B parameters) and 8 GPUs under both offline and server scenarios. The mean power draw model achieves a median absolute percentage error of $\leq3.4\%$ for offline and $\leq13.5\%$ for server scenarios on unseen GPUs, while the latency model achieves $\leq8.5\%$ in server mode, both maintaining strong GPU ranking correlations for server scenarios (Kendall $τ\geq0.76$). Compared to standard physically grounded baselines -- Load-Scaled Thermal Design Power (TDP) for power draw and roofline for latency -- our models reduce median absolute percentage error by approximately 4$\times$ on unseen LLM-GPU combinations for server scenarios or approximately 2$\times$ for completely unseen GPUs. WattGPU's data and code are publicly available at https://github.com/maufadel/wattgpu.
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：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向跨地域数据中心负载与电力资源之间的调度关系。意义：对日报读者而言，它可用于判断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
Mauricio Fadel Argerich, Jonathan Fürst, Marta Patiño-Martínez. WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs[J/OL]. (2026-07-03)[2026-07-09]. http://arxiv.org/abs/2607.02391v1.
Research Article算电协同
Chandan Chaudhary、Mohammed Ben-Idris、Joydeep Mitra
Published 2026-07-06 · arXiv · Credibility S
Water electrolysis plants, hyperscale data centers, and aluminum potlines represent gigawatts of demand-side flexibility for bulk power system balancing, operational planning, and procurement services. Such loads are scheduled through per-interval power bounds and horizon energy windows, whereas co-located battery energy storage systems (BESS) operate under state-of-charge dynamics. The two formulations share no com…
Abstract, interpretation and reference
Abstract
Water electrolysis plants, hyperscale data centers, and aluminum potlines represent gigawatts of demand-side flexibility for bulk power system balancing, operational planning, and procurement services. Such loads are scheduled through per-interval power bounds and horizon energy windows, whereas co-located battery energy storage systems (BESS) operate under state-of-charge dynamics. The two formulations share no common mathematical structure, and the joint procurement value of co-located loads and storage goes unrealized as a result. This paper establishes the connection between the two formulations through a virtual storage (VS) equivalence. Every feasible large-load trajectory under power-bound and energy-window constraints is a valid charge trajectory of a VS device that operates at unity accounting efficiency in the grid power balance. Production and service-level costs lie outside this abstraction and enter the dispatch through curtailment opportunity costs. For a portfolio co-located with a BESS, aggregation reduces the constraint count from O(NT) to O(T) and yields a co-dispatch price for both resources. Validation on the IEEE RTS-GMLC with three representative load classes shows that virtual storage delivers the dominant share of joint procurement savings. In the tested case, savings are additive because the two resources dispatch to non-overlapping intervals, and the curtailment shadow price tracks the peak-price band onset rather than the daily peak price.
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：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Chandan Chaudhary, Mohammed Ben-Idris, Joydeep Mitra. Large-Load Demand Flexibility as Virtual Storage[J/OL]. (2026-07-06)[2026-07-09]. http://arxiv.org/abs/2607.04564v1.
Research Article热管理与液冷
Basit A. Akinade、Amobichukwu C. Amanambu、Jonathan M. Frame、Shaolei Ren
Published 2026-06-20 · arXiv · Credibility S
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, f…
Abstract, interpretation and reference
Abstract
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)[2026-07-09]. http://arxiv.org/abs/2606.21760v1.
Research Article算电协同
Jiachen Shen、Jian Shi、Yijie Yang、Chenye Wu、Dan Wang、Ju Bin Song、Zhu Han
Published 2026-07-01 · arXiv · Credibility S
Thermal management in AI data centers is increasingly challenged by bursty workloads and uncertain heat generation. To prevent thermal violations, existing cooling strategies either enforce conservative, rigid bounds that severely limit grid responsiveness, or rely on forecast-driven controllers that perform poorly under AI workload uncertainty and distribution shifts. To overcome the above challenges, this paper pr…
Abstract, interpretation and reference
Abstract
Thermal management in AI data centers is increasingly challenged by bursty workloads and uncertain heat generation. To prevent thermal violations, existing cooling strategies either enforce conservative, rigid bounds that severely limit grid responsiveness, or rely on forecast-driven controllers that perform poorly under AI workload uncertainty and distribution shifts. To overcome the above challenges, this paper proposes a Contextual Distributionally Robust Optimization (CDRO) framework for grid-interactive cooling control. Unlike standard DRO with fixed ambiguity sets, the proposed approach dynamically adapts the Wasserstein radius using real-time AI and grid context. This safely shrinks uncertainty bounds during stable regimes, unlocking deep demand-side flexibility. Theoretically, we formulate the control as an infinite-dimensional inf-sup problem, derive an exact tractable reformulation for the Wasserstein worst-case expected-cost term, and then derive a tractable conservative deterministic counterpart for the Distributionally Robust Conditional Value at Risk (DR-CVaR) thermal safety constraint. Solved via a scalable nested Alternating Direction Method of Multipliers (ADMM) algorithm, the CDRO controller achieves near-zero thermal violations under extreme workload spikes in high-fidelity EnergyPlus co-simulations. Simultaneously, it reduces the operational cost premium of robustness by approximately 13.7 percentage points relative to standard Min-Max Model Predictive Control (MPC).
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
Jiachen Shen, Jian Shi, Yijie Yang, 等. Grid-Interactive Thermal Management of AI Data Centers via Contextual Distributionally Robust Optimization[J/OL]. (2026-07-01)[2026-07-09]. http://arxiv.org/abs/2607.00099v1.
