Quantifying Energy and Cost Benefits of Hybrid Edge Cloud: Analysis of Traditional and Agentic Workloads

The proliferation of IoT devices, AI agents, and robotics has redefined the nature of workloads in modern computing systems. With the emergence of optimized AI models and ongoing hardware advancements, most smart devices including smartphones, PCs, and IoT devices are already capable of running narrow AI models efficiently. While upcoming device upgrades will further enhance AI capabilities, current devices are sufficient for handling most inference workloads, making a device-first approach not only feasible but highly relevant for agentic workflows [2], [3]. These workloads are often Pareto-distributed [4], [5], [6], [7], [8], [9], [10] where a small percentage of high-resource tasks dominate computational resources, while most tasks are lightweight. Centralized cloud systems, originally designed for web browsing and app-based transactions, struggle to meet the demands of dynamic, context-aware applications. This paper explores the implications of HEC, which can process tasks locally on end devices when possible and offloads only high-resource tasks to the cloud or dedicated cloud gateways. To provide a comprehensive view, we analyze both traditional workloads which reflect typical smart devices with less intelligence and agentic workloads emerging in AI-driven systems like autonomous vehicles and robotics.