arXiv:2609.23085v1 Announce Type: cross
Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajector...
By Muhammad Abdur Rab Siddiqui, Daniela Rojas, Chen Yang, Wenqi Cui, Yuanyuan Shi, Yize Chen
arXiv:2608. 13076v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands.
By Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar Hanawal
arXiv:2605. 23348v2 Announce Type: replace-cross Abstract: AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up.
By Tella Rajashekhar Reddy, Atharva Deshmukh, Liangcheng Yu, Chaojie Zhang, Mike Shepperd, Rohan Gandhi, Anjaly Parayil, Srinivasan Iyengar, Ajay Manchepalli, Debopam Bhattacherjee
arXiv:2608.28726v1 Announce Type: new
Abstract: The remarkable performance of multimodal large language models (MLLMs) comes at the cost of substantial computational overhead, posing significant chal...
By Xinyuan Gui, Shaowen Wang, Sheng Sun, Zijian Wang, Zishu Yu, Zheming Yang
The paper introduces CarbonRouter, an online framework that jointly selects the model, GPU type, and region for each query in multi‑turn LLM web services to minimize carbon emissions. It incorporates a cache‑aware carbon cost model that accounts for the history recomputation cost when a conversation is routed to a different deployment, and a lightweight gate that keeps a session on its cached deployment when a move back is likely. Experiments on two real‑world conversation datasets show that CarbonRouter achieves the lowest carbon footprint among evaluated policies, reducing CO₂ emissions by 16.1 % on average and up to 23.1 % for longer sessions, while also improving predicted answer quality and estimated latency.
By Xiaoyang Zhang, Yuejun Yan, Qingang Zhang, Yonggang Wen
arXiv:2608. 12915v1 Announce Type: cross Abstract: The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality.
By Nicoletta Tsiopani, Moysis Symeonides, George Pallis, Marios D. Dikaiakos