Modern enterprise applications are increasingly running AI inference on-device rather than sending data to a central cloud. Improvements in hardware and model optimization have shifted the balance of compute. As one analysis notes, advances in 5G and edge hardware have made edge AI “a crucial technology for enabling intelligent applications.” Gartner predicts that by 2025 roughly 75% of enterprise data will originate at the edge rather than in traditional data centers. This data gravity, combined with emerging requirements for real-time response, privacy, and resilience, is driving inference tasks out of the cloud.Edge AI Reduces Latency, Bandwidth Costs, and Privacy RisksRunning inference at the edge avoids the latency and network costs of cloud round-trips. For latency-critical use cases such as self-driving cars or augmented reality, even a few hundred milliseconds of delay is unacceptable. By processing sensor data locally, an edge device can make sub-10ms decisions for safety and interactivity.