Solving the Latency Gap: Optimizing Edge Inference for Decentralized Generative World Models
Introduction Generative world models—neural networks that can simulate, predict, or create realistic environments—are the backbone of many emerging technologies: autonomous drones, augmented reality (AR) glasses, smart surveillance cameras, and collaborative robotics. Historically, these models have been trained in massive data centers and executed on powerful GPUs. Moving inference to the edge (e.g., a drone’s onboard processor or an AR headset) promises lower bandwidth usage, stronger privacy guarantees, and faster reaction times. ...