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AI is often treated as a single high-density challenge, yet the reality is far more nuanced. Training and inference workloads behave fundamentally differently, creating distinct thermal profiles, utilization patterns, and infrastructure demands. As data centers increasingly support both simultaneously, understanding these differences is becoming critical to designing cooling strategies that are efficient, resilient, and ready for the next generation of AI. This episode will explore:
- Why "AI = high density" is an oversimplification
- The thermal and utilization differences between training and inference workloads
- What changing AI workloads mean for cooling strategy in mixed environments
- How duty cycles, workload variability, and utilization influence cooling design and future infrastructure planning
