This episode will be available to stream at 1pm ET.

The rapid growth of AI inference is transforming network requirements, making connectivity a critical factor in delivering performance, responsiveness, and scale. Unlike traditional application traffic, inference workloads depend on the efficient movement of data between users, models, storage, and compute resources, often across highly distributed environments. This episode explores how companies are evolving network architectures to support these demands, examining the role of high-performance fabrics, optical technologies, and modern data movement strategies in enabling low-latency AI services while maximising infrastructure efficiency. Key discussion points include:

• Network architectures for distributed AI inference
• Designing high-performance fabrics and next-generation interconnects
• Integrating optical networking into AI infrastructure
• Optimising data movement across distributed environments