Datacenters in the Desert: Feasibility and Sustainability of LLM Inference in the Middle East

TL;DR

This paper empirically evaluates the energy and carbon footprint of deploying large language model inference in desert regions of the Middle East, comparing it with other countries to assess sustainability and feasibility.

Contribution

It provides the first empirical analysis of LLM inference energy consumption and carbon emissions in Middle Eastern desert environments, highlighting geographical trade-offs.

Findings

Desert datacenters face unique energy challenges.

Geographical location impacts carbon footprint significantly.

Sustainable deployment requires climate-aware strategies.

Abstract

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.