As AI workloads surge, data centres and enterprise hardware must adopt next-gen cooling and power-efficiency strategies, while laptops and workstations push for Arm-based, low-energy platforms.

The energy challenge for AI Compute
Rapid growth of AI deployment—both in data centres and edge/desktop environments—is placing unprecedented strain on power and cooling infrastructure. Data centres running training/inference clusters consume ever more electricity, and traditional cooling methods struggle to keep pace. dayton-daily-news
Hardware vendors and data-centre operators are responding with two key trends: (1) energy-efficient components (Arm-based laptops/workstations, NPUs) and (2) advanced cooling solutions (liquid/direct-to-chip/immersion).

Cooling innovations for servers and high-density racks
As rack densities increase (multiple GPUs/NPUs per server), air cooling becomes inadequate. Direct liquid cooling (DLC) and immersion cooling are now mainstream considerations. Schneider Electric+2IEEE Spectrum
Specific innovations:

• Direct-to-chip liquid cooling: coolant is delivered directly to the chip or heat spreader rather than being carried by fans blowing air. This reduces thermal resistance significantly. ashb.com

• Two-phase immersion or evaporative membrane cooling: e.g., the record-breaking 800 W/cm² flux membrane evaporative cooling developed at UC San Diego for AI servers. Tom’s Hardware

• Rack-level rear-door heat exchangers, modular liquid cooling, and hybrid air/liquid systems to retrofit existing data centres. RCR Wireless News
  

For high-performance AI servers, these cooling innovations allow greater compute density, lower failure rates, longer lifespan, and lower total cost of ownership (TCO) via reduced power consumption and improved reliability.
For hardware evaluators (you working with clients on AI/IoT solutions), this means when specifying servers/workstations, you should explicitly quantify: cooling method (air vs direct liquid vs immersion), power draw (PUE, watts per rack), thermal headroom, and serviceability.

Energy-efficient laptops, workstations, and ARM rivalry
On the desktop/workstation/laptop side, the focus is equally strong: how to deliver AI performance while reducing power, heat, and form-factor constraints.

• Arm-based laptops (for example, those using Apple’s M-series, or upcoming Windows-on-Arm) deliver significantly lower power consumption compared to x86, making them attractive for mobile AI workloads, extended battery life, and lower cooling burden.

• Even within the x86 terrain, NPUs designed for inference/AI tasks help offload work from discrete GPUs and reduce energy draw.

• For enterprise purchasers, the shift means evaluating not only raw performance (e.g., CPU/GPU flop/s), but also power efficiency (inference per watt), thermal envelope, battery life (for laptops), and edge/off-grid readiness.
  In effect, as AI workloads proliferate on devices of all sizes, the energy and cooling story becomes a competitive specification axis alongside raw compute.

Strategic implications for systems integrators and OEMs

• Data-centre vendors: Cooling and power strategy must be at the heart of AI-server design. Without adequate cooling and efficiency, high-density racks can become bottlenecks or high-risk failure points.

• Workstation/laptop vendors: Need to integrate AI acceleration, power-efficient chips, cooling (especially thin/light laptops) and certification for AI workloads.

• Service providers and enterprise buyers: The total cost of ownership must include energy, cooling infrastructure, failure risk, and the scalability of the compute platform as AI demands grow.

• Sustainability and ESG: As energy use becomes a board-level focus, hardware and infrastructure choices must align with corporate sustainability goals. AI compute hardware that is energy-inefficient may become a liability.
  From your role with IBM and enterprise clients, it’s important to build hardware proposals that include energy efficiency metrics, cooling strategy, and lifecycle plans—not just “fastest machine”.

Challenges and cautionary notes

• Transitioning to liquid or immersion cooling demands infrastructure investment, skilled maintenance, and may require retrofit of existing data-centres. Forbes+1

• Power density increases risk of “hot spots,” service-disruption, cooling fluid leakage, and compatibility issues with existing racks.

• For laptops/workstations, balancing AI-performance, battery life, heat dissipation and cost remains a design trade-off; not all vendors have mature AI-optimized hardware stacks yet.

Closing Thoughts and Looking Forward

Energy efficiency and advanced cooling are no longer optional in the AI era—they’re integral. Whether you’re planning a multi-rack GPU/-NPU server farm for training or equipping agentic-AI desktops in the field, consider not just the compute specs but how you’ll keep it power-efficient, cool, reliable and sustainable. For your enterprise clients and in your role advising hardware vendors or system integrators, this is a critical differentiator. Over the next 12-24 months expect even deeper innovation: greater liquid cooling adoption, plastics-to-chip microfluidics in workstations, and hardware procurement criteria that include “watts per inference” and “kW/rack” as standard metrics.

Author: Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Miami, Florida

Reference sites

1. “AI’s ballooning energy consumption puts spotlight on data center efficiency” — Dayton Daily News / The Conversation https://www.daytondailynews.com/local/ais-ballooning-energy-consumption-puts-spotlight-on-data-center-efficiency/S2PMUNMRSFAOLJJZQZ6C5IHMU4/ dayton-daily-news

2. “Liquid cooling challenges in AI data centers” — Schneider Electric Insights https://www.se.com/ww/en/insights/ai-and-technology/artificial-intelligence/liquid-cooling-challenges-in-data-centers-navigating-the-future-of-cooling-efficiency/ Schneider Electric

3. “Data Center Liquid Cooling: The AI Heat Solution” — IEEE Spectrum https://spectrum.ieee.org/data-center-liquid-cooling IEEE Spectrum

4. “Why Liquid Cooling For AI Data Centers Is Harder Than It Looks” — Forbes Councils https://www.forbes.com/councils/forbestechcouncil/2025/06/30/why-liquid-cooling-for-ai-data-centers-is-harder-than-it-looks/ Forbes

5. “Liquid cooling becoming essential as AI servers proliferate” — Network World https://www.networkworld.com/article/3978673/liquid-cooling-becoming-essential-as-ai-servers-proliferate.html Network World