对比四国数据中心部署大模型的能耗与碳排放，评估沙漠地区建站可行性。
Datacenters in the Desert: Feasibility and Sustainability of LLM Inference in the Middle East
- 用CodeCarbon工具实测不同国家大模型推理能耗与碳排。
- 沙漠地区能源成本低但散热挑战大，需权衡气候与基建条件。
- 为全球AI布局提供气候友好型选址参考，适合关注可持续性的研究者。
随着中东逐渐成为人工智能基础设施的战略枢纽，将可持续数据中心部署于沙漠环境的可行性日益受到关注。本文通过实证研究，分析了阿联酋、冰岛、德国和美国四个国家在代码生成任务中使用DeepSeek Coder 1.3B模型与HumanEval数据集进行大语言模型推理时的能耗与碳足迹。采用CodeCarbon库追踪能源消耗与碳排放，比较不同地理位置在气候友好型AI部署中的权衡关系。研究结果揭示了沙漠地区数据中心在能效方面的潜力与挑战，为全球人工智能扩张中可持续基础设施建设提供了平衡视角。
原文摘要 · Abstract (English)
As the Middle East emerges as a strategic hub for artificial intelligence (AI) infrastructure, the feasibility of deploying sustainable datacenters in desert environments has become a topic of growing relevance. This paper presents an empirical study analyzing the energy consumption and carbon footprint of large language model (LLM) inference across four countries: the United Arab Emirates, Iceland, Germany, and the United States of America using DeepSeek Coder 1.3B and the HumanEval dataset on the task of code generation. We use the CodeCarbon library to track energy and carbon emissions andcompare geographical trade-offs for climate-aware AI deployment. Our findings highlight both the challenges and potential of datacenters in desert regions and provide a balanced outlook on their role in global AI expansion.
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