Scaling the Intelligence Frontier: From GPU Scarcity to Production Sovereignty

As AI workloads evolve from massive foundation model training to distributed inference and specialized fine-tuning, the underlying infrastructure must adapt or become a bottleneck. Traditional hyperscalers often struggle with the bespoke power, cooling, and data orchestration requirements of the modern AI stack.

Join Adam Jones (VAST Data) and Konstantinos Mouzakitis (Nscale) as they break down the architectural requirements for the next generation of AI. This session explores the transition from raw GPU capacity to a holistic "AI Cloud" strategy. We will dive into the physical realities of liquid cooling and rail-optimized networking, the rise of edge cloud deployments for data sovereignty, and the operational talent required to move models from experimental labs into global production environments.

What you will learn:

• How to navigate the shift from large-scale training to distributed enterprise inference

• Architecting for performance: Overcoming power, cooling, and networking hurdles at the thousand-GPU scale

• Why specialized AI Clouds are outperforming hyperscalers through bespoke software layers and data-first orchestration

• The operational realities of "AI at scale" and the talent gap in production AI engineering