The prevailing trajectory of artificial intelligence infrastructure has, over the past decade, converged on a single architectural assumption: that meaningful AI workloads require large-scale centralized datacenters. These facilities, increasingly measured in tens to hundreds of megawatts, aggregate GPU resources into tightly controlled environments optimized for throughput, utilization efficiency...
The Neighborhood AI Cloud presents a promising shift in AI infrastructure, moving towards a cooperative compute topology that leverages underutilized resources in residential areas. This model addresses concerns related to the environmental impact and community resistance associated with large-scale centralized datacenters. However, it introduces new challenges such as scheduling efficiency, secure workload isolation, incentive alignment, and failure-tolerant execution models. Moreover, the dist...