AI Infrastructure & Self-Driving Networks: How HPE Juniper Is Powering the AI Era | Ep: 77
In this episode of the Deep Edge Podcast, host Ray Mota sits down with AE from HPE Juniper to break down the massive shift happening in networking and AI infrastructure. From multi-data center connectivity to edge computing and self-driving networks, they dive deep into how modern networks are evolving to support the heavy demands of AI workloads.
In this episode, we cover:
Scale-Up, Scale-Out & Scale-Across: Why AI is inherently a distributed application and how backend networks (DCI, GPU fabrics) are adapting.
AI On-Ramps & Edge Computing: Connecting enterprise users to AI clusters for high-performance inference and real-time consumption.
Silicon Strategy: How HPE Juniper leverages custom silicon (PTX and Trio chips) to optimize power efficiency, space, latency, and GPU duty cycles.
Non-Traditional Fabrics: Navigating satellite/LEO connectivity and data center expansion in space.
Self-Driving Networks: Moving from telemetry and AIOps concept to real-world deployments that reduce support tickets and automate operations.
Enterprise Networking Advice: Why traditional oversubscription model architectures are failing in the age of agentic AI and symmetric traffic flows.
#AINetworking #HPEJuniper #DeepEdgePodcast #AIInfrastructure #SelfDrivingNetworks #EdgeComputing #DataCenter #NetworkingStrategy
Originally published on Ray Mota's Substack. Subscribe to get new analysis by email.
