The next phase of AI will not only be defined by bigger models. It will be defined by where those models run.AI is moving out of centralized data centers to the edge: enterprise sites, regional clouds, cell towers, and mobile networks. Now that mobile devices account...
Introduction AI workloads are no longer confined to a single data center. They’re often distributed across multiple clouds and edge locations. Consequently, the need for flawless network synchronization has surged. Yet, one rarely monitored network performance...
Introduction Edge AI is no longer just a vision. As demand grows for real-time inference, adaptive services, and AI-driven automation, compute is moving closer to where data is generated and used — and cell towers are uniquely positioned to enable it. They already...
Introduction AI inference and training are rapidly becoming more distributed to enable low-latency, high-efficiency applications by processing data close to the source. As this trend accelerates, Gartner predicts that by 2027, 90% of organizations will adopt a hybrid...
Introduction Recent advances in Zero Trust and cloud-hosted cybersecurity have significantly enhanced data and network protection with their block by default, allow by exception approach. However, these security solutions are both negatively impacted by, and at the...
