The data center’s role in delivering sustainable AI

With global AI usage skyrocketing, sustainability is at the forefront of industry discussions and decision-maker’s considerations. In this piece, Verne digs deeper into the evolving AI sustainability landscape.

As AI technologies advance, AI data centers’ environmental impact is being met with more scrutiny, and industry providers are under increasing pressure to manage their environmental footprint. 

This article explores the challenges of AI’s environmental impact and how liquid cooling, renewable energy, and workload optimization are pivotal in promoting sustainable AI data center solutions.

Understanding AI's Environmental Impact

At the intersection of AI and sustainability, data centers serve as the backbone for hosting and processing AI workloads. These facilities are tasked with handling the immense computational demands of AI algorithms and applications, which often require vast amounts of energy. As a result, AI data centers have emerged as focal points for addressing AI's environmental footprint and promoting sustainable practices within the industry. 

One of the primary challenges associated with data centers is their substantial energy consumption. AI workloads, particularly those involving deep learning and training large neural networks, demand significant computational power, leading to heightened energy requirements. These requirements have made headlines around the world as governments begin to realize the impact AI’s energy consumption will have on local power grids, with the conversation around methods of power generation - including nuclear - evolving alongside.  

There is also growing concern about carbon emissions and environmental degradation. To combat the environmental impact of AI workloads in the data center, industry leaders are increasingly implementing strategies to enhance energy efficiency and reduce carbon emissions. These efforts encompass various initiatives, ranging from optimizing hardware utilization to adopting renewable energy sources. 

Meeting & Reducing Energy Consumption of AI Workloads

Optimizing hardware usage involves increasing the efficiency of computing resources within AI data centers, and can be achieved through techniques such as: 

• Workload consolidation: combining multiple computing tasks onto fewer physical or virtual machines to improve efficiency, reduce energy consumption, and lower operational costs.
• Resource scheduling: allocating compute, storage, and networking resources dynamically based on workload demand. 
• Hardware acceleration: using of specialized hardware components (e.g., GPUs, TPUs, FPGAs) to offload compute-intensive tasks from general-purpose processors. 

By streamlining computational processes, AI data centers can achieve higher performance levels while consuming fewer resources, thereby reducing their environmental footprint. 

Another key aspect of promoting sustainability in AI data centers is the adoption of renewable energy sources. By transitioning to clean energy sources such as solar, wind, and hydroelectric power, data centers can significantly reduce their carbon emissions and reliance on fossil fuels. The Nordics, with their abundant renewable energy resources, have become attractive locations for AI workloads due to their commitment to sustainability and environmental responsibility. 

Innovative AI Data Center Cooling Solutions

The next wave of innovation will focus on cooling technologies. Traditional air-cooled systems account for up to 40% of a data center’s energy consumption. While the Nordics have easily been able to harness free cooling to improve energy efficiency and reduce environmental impact, rising rack densities will require even more cooling capability. 

Liquid cooling plays a role, too. This method involves using fluids to absorb and dissipate heat from servers, making it more than 1,000x more efficient than traditional air-based methods - reductions in cooling energy use of 30-50%. Google’s data centers used over 20 billion liters of fresh water for cooling in 2022, and the technology will continue to enable data center operators to cool up to 200kW per rack to accommodate the most advanced AI workloads in the future.

Why the Nordics Are Ideal for AI Data Centers

With this surge in AI, machine learning and high-performance computing amplifying the need for robust data center cooling solutions, Verne is already enabling the potential of liquid cooling technologies at our Pori data center campus in Finland.  

Here’s why this region is perfect for AI data centers:

• 100% renewable energy availability (hydro, geothermal, wind)
• Cold climate reduces the need for energy-intensive cooling
• Low-latency connectivity to major European markets

Verne Global’s Commitment to Sustainable AI

As organizations continue to navigate the complexities of AI sustainability and carbon footprint reduction, Verne is here to help. Sustainability and efficiency have been at the heart of Verne’s mission since we first opened our doors more than a decade ago, and we are proud to deliver sustainable data center solutions with 100% renewable power in our Finland and Iceland data centers.  

With multiple sites delivering unmatched specialized data center services for AI workloads, we’re helping organisations – like Hugging Face, Peptone, and Wirth Research - cost-effectively scale their digital infrastructure while reducing their environmental impact. 

Discover how Verne can support your AI initiatives sustainably. Contact us to learn more about AI data center cooling, and about our energy-efficient data center services.