Cooling, Power, and Running AI in Production - Six Five On The Road

Once AI leaves the lab, power, heat, and efficiency decide what can actually run.

From Lenovo Tech World in Las Vegas, Six Five On The Road turns its attention to what changes when AI moves from experimentation into sustained production. As compute density rises and AI workloads become persistent, the conversation shifts toward the physical demands required to support them, including power delivery, thermal management, and operational efficiency.

Patrick Moorhead and Daniel Newman are joined by Lenovo’s Scott Tease, Vice President, Product Group, ISG to examine how power availability, heat dissipation, and energy efficiency are now shaping enterprise AI deployment decisions. As higher-density systems push traditional air-cooled environments to their limits, organizations are rethinking how AI infrastructure is designed, what it costs to operate, and where it can realistically scale, with liquid cooling increasingly entering the conversation as a practical requirement rather than an edge case.

Key Takeaways Include:

🔷 Production AI introduces physical constraints: Once AI runs continuously, power availability, heat, and efficiency shape what can be deployed and sustained.

🔷 Higher density changes system design: As AI workloads concentrate more compute in less space, traditional cooling approaches face growing limitations.

🔷 Energy efficiency impacts economics: Power and cooling are now major contributors to the total cost of ownership (TCO) for AI systems in production.

🔷 Liquid cooling is becoming a practical option: What was once limited to hyperscalers is increasingly relevant to enterprises planning for long-term AI growth.

🔷 Operational planning determines scalability: Organizations that account for power and thermal requirements early are better positioned to expand AI without disruption.

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Listen to the audio here:

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