The startup Span, in partnership with Nvidia and PulteGroup, is testing the installation of mini data center nodes within U.S. residential homes [1, 2].
This initiative seeks to decentralize AI processing by leveraging idle electricity in the residential grid. By distributing the computational load, the companies aim to meet the surging demand for AI workloads while bypassing the massive infrastructure requirements of traditional data centers [1, 2].
These small nodes are designed to operate using unused grid capacity [1, 2]. This approach allows the partners to utilize existing electrical infrastructure rather than building new, large-scale facilities that often face significant regulatory hurdles and community opposition [1, 2].
Reducing the physical footprint of AI infrastructure is a primary goal of the project. Traditional data centers often trigger community pushback due to their size, noise, and energy consumption, issues that are mitigated when the hardware is integrated into existing homes [1, 2].
Beyond community relations, the strategy is intended to lower the overall costs associated with AI growth [1, 2]. By distributing the hardware across a network of homes, the companies can potentially reduce the capital expenditure required for land acquisition and massive cooling systems typical of centralized hubs [1, 2].
The testing is ongoing as of May 2026 [1, 2]. The collaboration brings together Span's electrical panel technology, Nvidia's processing power, and PulteGroup's residential development expertise to create a distributed computing network [1, 2].
“The startup Span, in partnership with Nvidia and PulteGroup, is testing the installation of mini data center nodes within U.S. residential homes.”
This shift toward 'edge computing' at a residential scale suggests that AI growth may outpace the ability of the current power grid to support centralized data centers. By turning homes into micro-hubs, the industry is attempting to monetize residential electrical overhead and reduce the political and environmental friction associated with industrial-scale AI infrastructure.
