The paper reviews 194 papers on using artificial intelligence to optimize data center energy use, coding 63 of them. It finds that most control studies validate only in simulation, none consider water withdrawal or embodied carbon, and savings estimates overlap across methods, preventing ranking. The authors propose CLEAR‑DC, a framework that links control and workload demand through elasticity, reports net benefits, and records energy, carbon, water, embodied share, and validation venue.
By Mohammed Basharath Ullah, Summaiya Unnisa Begum, Mohammed Nadeem Ullah
The paper presents an LLM-based predictive scheduling system that forecasts execution time and energy consumption from source code, aiming to improve data center sustainability. By integrating these predictions into a real-time GPU allocation algorithm, the system reduces both energy use and queuing delays. In a collaboration with a data center, the approach achieved a 32% drop in energy consumption and a 30% reduction in waiting time.
By Hanzhao Wang, Jingxuan Wu, Yumeng Li, Yu Pan, Guanting Chen
arXiv:2608. 08761v1 Announce Type: cross Abstract: In an era defined by escalating climate change and the pervasive deployment of edge intelligence, the environmental cost of semiconductor manufacturing and operation has reached a critical threshold.
By Jatin Chopra
The MIT-developed tool "CrysVCD" uses AI to design new materials that are chemically stable in real-world conditions. By predicting stability early, it can significantly reduce the time and cost associated with testing and discarding unstable material designs.
By Zach Winn | MIT News
arXiv:2607. 05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energy and disclose little about manufacturing (embodied) emissions, water consumption, or the underlying highperformance computing (HPC) infrastructure.
By Marc L\'eobet, Pierre-Fran\c{c}ois Lavall\'ee, Jean-Pierre Lorr\'e
arXiv:2608. 14614v1 Announce Type: cross Abstract: As AI datacenters retire functional GPUs, vast quantities of still capable accelerators enter secondary markets.
By Zeyu Cao, Xuan Guo, Cheng Zhang, Cheuk Hang Lau, Ilia Shumailov, Yiren Zhao
arXiv:2607. 02531v1 Announce Type: cross Abstract: Water use by data centers is routinely reported as a single footprint, but water is consumed through two physically distinct pathways: at the site for cooling and in the power system that generates electricity.
By Gianluca Guidi, Francesca Dominici
What data teams need to build with AI to make self-healing data architecture a practical reality The post 7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture appeared first on Towards Data Science .
By Hugo Lu
The hidden cost of asynchronous systems, how tiny CPU tasks quietly became our biggest bottleneck while scaling hundreds of LLM agents. The post Why Adding More AI Agents Made Our System Slower appeared first on Towards Data Science .
By Uri Peled
OpenAI and Foxconn are collaborating to design and manufacture next-generation AI infrastructure hardware in the U. S.
OpenAI and SoftBank Group partner with SB Energy to develop multi-gigawatt AI data center campuses, including a 1. 2 GW Texas facility supporting the Stargate initiative.
OpenAI and the U. S.