XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms
The paper introduces XWind, a cross-site router designed for large language model inference at renewable energy farms. It addresses the growing demand for AI power and proposes a model called AI Greenferencing to optimize energy use from wind sources. The evaluation shows significant improvements in latency and efficiency compared to traditional methods.
- ▪AI power demand is increasing rapidly, while power grids struggle to meet this demand.
- ▪The proposed AI Greenferencing model aims to deploy modular AI compute at renewable energy sources, particularly wind.
- ▪XWind reduces end-to-end latency by up to 52% compared to the strongest competitor and by up to 98% over traditional baselines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23348 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | GreAa-kqTk6K |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2605.23348 (cs) [Submitted on 22 May 2026] Title:XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms Authors:Tella Rajashekhar Reddy, Atharva Deshmukh, Liangcheng Yu, Chaojie Zhang, Mike Shepperd, Rohan Gandhi, Anjaly Parayil, Srinivasan Iyengar, Ajay Manchepalli, Debopam Bhattacherjee View a PDF of the paper titled XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms, by Tella Rajashekhar Reddy and 9 other authors View PDF HTML (experimental) Abstract:AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up.
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