  In the last decade, Deep Learning has rapidly infiltrated the consumer end,
mainly thanks to hardware acceleration across devices. However, as we look
towards the future, it is evident that isolated hardware will be insufficient.
Increasingly complex AI tasks demand shared resources, cross-device
collaboration, and multiple data types, all without compromising user privacy
or quality of experience. To address this, we introduce a novel paradigm
centered around EdgeAI-Hub devices, designed to reorganise and optimise compute
resources and data access at the consumer edge. To this end, we lay a holistic
foundation for the transition from on-device to Edge-AI serving systems in
consumer environments, detailing their components, structure, challenges and
opportunities.
