Everyone is talking about bigger AI clusters. What happens when those clusters need to span multiple data centers? At Cisco Live, I sat down with Rakesh Chopra, SVP & Fellow, Common Hardware Group at Cisco on The Ravit Show, to discuss one of the less talked about challenges in AI infrastructure: scaling AI beyond a single data center.

A few key themes from our conversation:

-- The industry is moving from scale-up and scale-out to scale-across architectures

-- Connecting GPUs across data centers is becoming a critical challenge as organizations build larger AI environments

-- Power efficiency is now as important as raw performance, driving innovation in silicon and optics

-- Network reliability and low-latency communication are essential as AI clusters stretch across geographic boundaries

-- Co-designing networking, silicon, and optics is becoming a requirement rather than an optimization

The AI conversation often focuses on models.

But the real story may be the infrastructure required to make those models work at scale.

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