Skip to content
GCC AI Research

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

arXiv · · Significant research

Research LLM Infrastructure Ethics Arabic AI

Summary

This paper analyzes the energy consumption and carbon footprint of LLM inference in the UAE compared to Iceland, Germany, and the USA. The study uses DeepSeek Coder 1.3B and the HumanEval dataset to evaluate code generation. It provides a comparative analysis of geographical trade-offs for climate-aware AI deployment, specifically addressing the challenges and potential of datacenters in desert regions.

Keywords

LLM · Datacenter · Carbon Footprint · UAE · Sustainability

Read original article →

Get the weekly digest

Top AI stories from the GCC region, every week.

Related

Vicuna, Altman, and the importance of green AI

MBZUAI ·

MBZUAI President Eric Xing led a global collaboration to develop Vicuna, an LLM alternative to GPT-3 addressing the unsustainable costs of training LLMs. OpenAI CEO Sam Altman acknowledged Abu Dhabi's role in the global AI conversation, building off of achievements like Vicuna. Xing and colleagues are publishing research at MLSys 2023 on "cross-mesh resharding" to improve computer communication in deep learning, aiming for low-carbon, affordable, and miniaturized AI. Why it matters: This research signals a push towards sustainable AI development in the region, emphasizing efficiency and reduced environmental impact.