Inference was once expected to be the easier side of AI infrastructure. Training needed the specialized clusters, extreme compute, and unusual power and cooling profiles. Inference, by contrast, was expected to fit more comfortably into traditional data center environments. But that assumption is changing. In this episode of Data Center Dialogues, Alison Matte is joined by Wendy Torell, Senior Research Analyst at Schneider Electric, to discuss why generative AI inference is becoming more complex, more power-intensive, and more important to physical infrastructure strategy. They explore the varying classes of inference workloads, the decision factors behind cloud, colocation, and on-premises deployment, and why IT and facilities teams need to plan together from the start. Key insights

• Why inference can no longer be treated as “business as usual” for every AI workload.
• How different classes of inference workloads create very different requirements for rack density, power, cooling, and scalability.
• Why agentic AI can drive higher compute demand, sustained power draw, and more complex infrastructure needs.
• How leaders should evaluate cloud, colocation, and on-premises deployment based on latency, security, compliance, business model, and control.
• Why future-ready infrastructure depends on flexibility, modular design, cooling evolution, high-density power distribution, and software visibility.
• Four practical steps CIOs and infrastructure teams can take now to prepare for AI inference at scale.

Read next

• Executive report: Generative AI Inferencing Ramp-up: A CIO’s Guide to Physical Infrastructure Considerations.
• White paper: 10 Ways to Harness the Energy and Water Efficiencies of Direct Liquid Cooling
• Visit the Insights Portal: Preparing for generative AI inferencing: A strategic approach to infrastructure
• Take a Schneider Electric University class: Generative AI Inferencing Ramp-Up: A CIOs Guide to Physical Infrastructure Considerations