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.

翻译：随着中东地区成为人工智能（AI）基础设施的战略枢纽，在沙漠环境中部署可持续数据中心的可行性已成为日益重要的议题。本文通过实证研究，分析了在阿联酋、冰岛、德国和美国四个国家中，使用DeepSeek Coder 1.3B模型和HumanEval数据集进行代码生成任务时，大型语言模型（LLM）推理的能耗与碳足迹。我们采用CodeCarbon库追踪能源消耗与碳排放，并比较了不同地理位置在气候友好型AI部署中的权衡。研究结果既揭示了沙漠地区数据中心面临的挑战，也展现了其潜力，为全球AI扩张中此类数据中心的作用提供了平衡的展望。