Datacenters in the Desert: Feasibility and Sustainability of LLM Inference in the Middle East
It addresses the feasibility of sustainable AI infrastructure in the Middle East, which is incremental as it applies existing tools to new geographical contexts.
This paper analyzed the energy consumption and carbon footprint of LLM inference for code generation in desert regions like the UAE compared to other countries, finding both challenges and potential for sustainable datacenter deployment.
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.