KAUST and Gulf Data Hub, along with Data Hub Tech and Ashi Bushnag Co., held a groundbreaking ceremony on September 18 to celebrate the development of a new data center on the KAUST campus. The data center will be built by Gulf Data Hub in partnership with the Saudi company Data Hub Tech and Jeddah-based design and contracting firm Ashi Bushnag Co. KAUST Interim President Nadhmi Al-Nasr and Gulf Data Hub CEO Tarek Al-Ashram exchanged gifts during the ceremony. Why it matters: This data center project signifies growing investment in Saudi Arabia's technological infrastructure and KAUST's role as a hub for innovation.
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.
Construction has begun on a 30 million square foot data center in Saudi Arabia for use by the Saudi government. The project was announced by the Saudi Ministry of Investment. No details were provided regarding the location, cost, or timeline for the build. Why it matters: This investment signals the Kingdom's intent to develop significant digital infrastructure to support its Vision 2030 goals and emerging AI sector.
Saudi Arabia's Vision 2030 is progressing with the launch of a new data center project by the Saudi Data and Artificial Intelligence Authority (SDAIA). This initiative aims to modernize the Kingdom's technological infrastructure to meet the demands of its growing digital economy. The data center will support advancements in AI, data analytics, and cloud computing within Saudi Arabia. Why it matters: The project signals Saudi Arabia's commitment to becoming a regional leader in AI and technology, attracting investment and fostering innovation.
A Duke University professor presented a data-centric approach to optimizing AI systems by addressing the memory capacity and bandwidth bottleneck. The presentation covered collaborative optimization across algorithms, systems, architecture, and circuit layers. It also explored compute-in-memory as a solution for integrating computation and memory. Why it matters: Optimizing AI systems through a data-centric approach can improve efficiency and performance, critical for advancing AI applications in the region.