Reduce Data Center Costs By Boosting Energy Efficiency
Implementing Private AI requires significant design changes to data center infrastructure, including GPU cooling and power management, which requires specialized resources and skills.
Power & Cooling
Considerations
GPU designers push the physical limitations of silicon with core density requirements never seen before, breaking the limits of AI scale and performance. The result is immense power consumption and heat generation previously unseen in a data center.
The use of data-intensive technologies, including artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are spurring exponential growth for server space, placing ever greater power and thermal demands on modern data centers.
To prepare for the future requirements of AI Infrastructure, companies are implementing technologies that will enable them to introduce higher rack densities and higher performing GPUs to maximize data center performance while also assisting them with resource sustainability commitments and minimizing the environmental impact of their facilities.
Specific strategies include the adoption of renewable energy sources and the implementation of energy-efficient infrastructure, such as innovative cooling systems, including direct to chip, liquid cooling, and immersion—reducing energy costs and supporting your sustainability goals.
Power & Cooling Expertise
AI modeling comes with increasing GPU rack densities that are escalating rapidly with power requirements up to 50kW per rack and beyond. Specifically, a H100 Rack with only 4 Nodes requires 44kW. This stands in stark contrast to the industry average of 8.6-10kW per traditional rack for conventional data centers.
With this immense computing power within the modern data center, traditional air-cooling methods are hitting performance barriers as chip densities and thermal output continue climbing exponentially increasing heat loads generated by modern GPU processors.
This translates to inefficient energy usage, higher carbon emissions, and the need for sprawling data center footprints to dissipate the heat. Hotspots within these facilities further exacerbate the problem, leading to thermal inefficiencies and performance bottlenecks.
With power dictating everything in AI infrastructure design, Penguin Solutions plans the physical layout of the data center footprint with advanced cooling technologies such as liquid cooling and liquid immersion in mind.
Direct to chip
This data center cooling method directly cools servers by pumping coolant to a cold plate that contracts components directly.
Single-phase liquid immersion
Servers are immersed in a nonconductive, single-phase coolant fluid such as oils, fluorocarbons, or synthetic esters, which absorb heat.
Two-phase liquid immersion
Servers are immersed in a bath of dielectric fluid that boils off to remove heat.
25+
Years Experience
85,000+
GPUs Deployed & Managed
2+ Billion
Hours of GPU Runtime
Cooling Sustainably With
Immersion Cooling
With increases in compute-intensive workload power consumption—and the training and tuning requirements of AI models—systems cannot be cooled sustainably using conventional cooling methods.
Discover how Penguin Solutions partnered with AMD and Shell to boost performance with lower emissions at Shell’s Houston data center by implementing immersion-ready systems.
Talk to the Experts at Penguin Solutions
Reach out today and learn more how we can help you with your AI & HPC data center layout including your power and cooling requirements while meeting your sustainability objectives.