AI Computational Power Dictates Everything in Infrastructure Design
AI solutions demand a new class of compute power requirements. In the past 10 years, the average power consumption of data center processors and accelerators has nearly tripled and the demand continues to climb.
AI Computational Power
Scalability Pain Points
The increased power consumption associated with GPU clusters and AI infrastructure has significant implications for data center operations. Compared to traditional IT systems, AI clusters have demanding and complex power requirements. Today, high-density racks can be in the 40kW to 125kW range, while extreme density racks can reach 200kW or higher.
Strain on Power Grids
The need for constant, high-density power for AI operations burdens existing infrastructure and slows the transition to clean energy.
Environmental Concerns
Without advancements in energy-efficient technologies, AI's growing energy footprint could hinder climate-neutrality goals.
Rising Energy Costs
Training AI models can be extremely energy-intensive, raising concerns about sustainability and energy costs.
Compute Bottleneck
Memory can be a significant bottleneck to compute capacity as the processor needs to access data faster than it can be delivered.
AI Workloads Are Driving a Massive Increase in Power Consumption
Artificial Intelligence (AI) is revolutionizing industries, but its rapid growth comes with significant energy demands. As AI workloads expand, the power consumption associated with training and running models is skyrocketing, raising concerns about sustainability and climate impact.
By 2026, AI data centers alone are expected to consume 90 terawatt-hours annually, a tenfold increase from 2022 levels. This surge places immense pressure on electricity providers, with global data center energy consumption projected to exceed 1,300 TWh by 2030 if efficiency improvements do not materialize. Moreover, data centers which house servers essential for AI operations are responsible for over 1% of global electricity use and are projected to consume up to 12% of U.S. electricity by 2028.
Environmental Implications
The energy-intensive nature of AI workloads poses challenges to climate goals set by tech giants. Despite pledges for carbon neutrality by 2030, greenhouse gas emissions continue to rise significantly due to data center expansion.
Beyond emissions, data centers strain local resources. In water-scarce regions, data center facilities consume millions of gallons of drinking water annually. Communities near data centers face rezoning issues and concerns about electricity and water access as more real estate is allocated for technology data center hubs near population zones.
Innovative Sustainable Solutions
Power and heat are two of the biggest issues affecting data centers today. More power is needed for density higher racks, which cannot be cooled sustainably using traditional cooling methods.
Liquid immersion cooling requires considerably less power to run compared with air cooling systems, with potential for a 50% reduction in the energy used to cool a data center's server equipment. Liquid cooling reduces power consumption by eliminating fans and reduces space requirements by removing overhead for cooling infrastructure.
Reach out to Penguin Solutions today to learn more how we address the aforementioned memory compute bottleneck pain point and can assist with sustainability within your data center including waterless cooling via direct-to-chip cooling and two-phase liquid cooling.
You can also read how we boosted performance and lowered emissions at Shell's data center with data center immersion cooling.
Talk to the Experts at
Penguin Solutions
Reach out today and learn more how we can help you with your scalable AI computational power needs as you design your data center infrastructure for Private AI.