Reduce Data Center Costs By Boosting Energy Efficiency
Implementing AI requires significant design changes to data center infrastructure including GPU cooling requirements and power management which demand specialized solutions.
Power & Cooling
Considerations
Graphics processing unit (GPU) designers push the physical limitations of silicon with previously unheard-of core density requirements that are critical for artificial intelligence (AI) scale and performance. The result is immense power consumption and heat generation at levels previously unseen in data centers.
The use of data-intensive technologies such as AI, high-performance computing (HPC), machine learning (ML), and the Internet of Things (IoT) are spurring exponential server space growth, which in turn creates greater power and thermal demands for today's data centers.
To lay the foundation for their AI infrastructures, companies are implementing technologies that support higher rack densities and higher performing GPUs that maximize data center performance while maintaining sustainability commitments, reducing resource requirements, and minimizing the environmental impact of their data center operations.
By adopting renewable energy sources and implementing energy-efficient infrastructure such as innovative direct-to-chip, liquid cooling, and immersion cooling systems, these organizations are reducing their energy costs and advancing their sustainability goals.
Power & Cooling Expertise
AI modeling requires rapidly escalating GPU rack densities with increasing power requirements of up to 50kW per rack or more. For example, an H100 rack with just four nodes requires 44kW—a stark contrast to the 8.6-10kW industry average per traditional rack for conventional data centers.
The immense computing power in AI data centers far exceeds the performance capacity of traditional air-cooling methods. As chip densities and thermal output climb exponentially, so do the heat loads generated by GPU processors resulting in inefficient energy usage, higher carbon emissions, and sprawling data center footprints that dissipate the heat. Hotspots within these facilities exacerbate the situation, leading to thermal inefficiencies and performance bottlenecks.
When it comes to AI infrastructure design, power dictates everything. That's why Penguin Solutions plans the physical layout design of the data center footprint with advanced cooling technologies such as liquid cooling and liquid immersion in mind.
Direct-to-Chip
This cooling method directly cools servers by pumping coolant through an absorbing cold plate or heat sink that is in direct contact with the chip.
Single-Phase Liquid Immersion
With this approach, servers are immersed in a non-conductive, single-phase coolant fluid such as oils, fluorocarbons, or synthetic esters that absorbs heat.
Two-Phase Liquid Immersion
This two-step process leverages evaporation and condensation cycling by immersing servers in a bath of specialized dielectric fluid that then boils off to dissipate heat.
25+
Years Experience
85,000+
GPUs Deployed & Managed
2+ Billion
Hours of GPU Runtime
Cooling Sustainably With
Immersion Cooling
As compute-intensive workload power consumption—and the training and tuning requirements of AI models—continues its rapid growth, conventional cooling methods are increasingly unable to cool systems sustainably.
Explore how Penguin Solutions boosted performance and lowered emissions at Shell's Houston data center by partnering with AMD and Shell to implement immersion-ready systems.
AI infrastructure is cooled using advanced systems such as direct‑to‑chip liquid cooling, single‑phase or two‑phase immersion cooling, high efficiency air cooling, or hybrid combinations of these cooling strategies.
AI and HPC centers benefit most from direct‑to‑chip liquid cooling, rear‑door heat exchangers, and single‑phase or two‑phase immersion cooling to handle escalating thermal loads and rack densities sustainably.
Cooling systems remove heat from IT equipment via air handlers, chilled water systems, liquid pumped directly to cold plates, or by submerging servers in dielectric fluids. Immersion and direct liquid methods eliminate onboard server fans and reduce infrastructure overhead, efficiently managing high‑power CPU/GPU environments.
AI data centers combine power‑efficient hardware, intelligent rack‑level layout planning, renewable energy use, and cooling systems like immersion and direct liquid solutions. This integrated design minimizes energy consumption, supports high‑density racks, improves carbon footprint, and lowers power usage effectiveness (PUE) while maintaining performance.
Key considerations include compute density (kW per rack), thermal load, facility layout, energy costs, environmental impact like carbon reduction, and ongoing scalability. Cooling strategies should align with sustainability goals and operational complexity.
Industries with high compute or thermal demands, such as oil & gas, scientific research, finance, automotive, and large‑scale AI applications in discrete and process manufacturing and healthcare, see direct gains through optimized power and thermal management strategies.
Talk to the Experts at Penguin Solutions
Reach out today and learn how we can help you address the power and cooling requirements of your AI and HPC data center layout while also achieving your organization's sustainability goals.