Pioneer a More Sustainable Future for Your AI Data Center
Cutting-edge solutions tackle the surging power demands of advanced computing while supporting lower emissions.
AI Data Center Energy
Consumption Pain Points
Sustainability has emerged as a corporate top priority, and corporate data centers and IT systems are a key area of focus given the heavy amounts of resource consumption that are involved in powering and cooling increasingly energy-hungry processing environments.
Increased Energy Costs
AI training, which requires massive computational resources, significantly boosts power consumption, leading to higher energy operational expenses.
Environment Concerns
The surge in data center energy consumption contributes to an infrastructure's larger carbon footprint, raising environmental sustainability concerns.
Capacity Constraints
The high power demands of AI training and inference can strain data center infrastructure, leading to capacity limitations and potential bottlenecks.
Cooling Challenges
AI workloads generate substantial heat, requiring efficient, next-gen liquid or immersion cooling systems to maintain optimal operating temperatures.
Energy Consumption and Data Center Sustainability for AI, HPC and ML
As organizations move for faster and more powerful computing environments, the newer GPU processors and solid-state storage all require significantly more power to operate than their technological predecessors. In some cases, the power requirements of these large data centers can rival that of a small city, and this is driving organizations of all sizes to pursue alternatives that can offset this growth in consumption.
The focus on energy efficiency has driven new interest in a power-related metric called power usage effectiveness (PUE) that has long been associated with high-performance computing (HPC) workloads run by some of the largest users of compute resources—such as the US Department of Energy.
Data Center Energy Effectiveness
PUE measures the energy effectiveness of a data center by measuring the amount of raw power entering the data center and dividing it by the power used to run the IT equipment within it. A perfectly efficient data center would have a PUE of 1.0 – indicating that 100% of the power entering the data center was being used to power the equipment needed, with no waste.
In reality, PUE calculations need to consider the power used for cooling and power conversion. They also need to show measurements taken as a year-round average that includes the hot summer months when cooling requirements will drive up the power requirements for operations.
According to the Uptime Institute, the average data center PUE is 1.58, and if your facility exceeds this, you should consider efforts to reduce it. Continuously monitoring PUE helps data center managers identify efficiency changes over time, such as during peak loads or the aforementioned seasonal shifts, allowing them to set goals, compare sites, and make the most informed energy decisions.
Developing Environmentally Friendly Data Centers
When data centers are tasked with handling larger and increasingly complex workloads, everything from weather forecasting to cancer research, adopting HPC, artificial intelligence (AI), and machine learning (ML) technologies creates tension between the innovation they enable and their environmental impact placing a strain on water usage, power consumption, and carbon footprint.
Implementing sustainable practices such as waterless cooling and energy-efficient servers, minimizes the strain on your data center infrastructure; and these efforts not only help to improve sustainability, but also help reduce operating costs.
Reach out to Penguin Solutions today to learn our approach to AI infrastructure design for energy efficiency, integration of emerging cooling technologies, and how we help maximize data center performance while supporting sustainability goals.
Talk to the Experts at Penguin Solutions
Reach out today and learn more how we help you reach your sustainability goals, with concerns focusing on your data centers' use of key resources such as energy and water, as well as their wider environmental impacts.