Would you host part of an AI data center in your home?
Sunrun’s building a ‘nationwide compute network’ that will run AI from small compute nodes in customers’ homes.
Sunrun’s building a ‘nationwide compute network’ that will run AI from small compute nodes in customers’ homes.
by Stevie Bonifield
Jul 10, 2026, 1:20 PM UTC


Image: Sunrun
Stevie Bonifield
is a news writer covering all things consumer tech. Stevie started out at Laptop Mag writing news and reviews on hardware, gaming, and AI.
A solar and home energy storage company is expanding into AI data centers, but not by building one — instead, it’s offering to pay its customers to put its compute units in their homes. Sunrun is launching a pilot program for a new “distributed AI compute” program that will “place numerous compute nodes in homes equipped with Sunrun solar and battery storage systems.” Customers will be “compensated” for participating in the pilot program.
Sunrun plans to sell the distributed compute power from the nodes to “enterprise compute buyers,” like AI companies. It’s a new approach to finding resources and space for AI compute infrastructure, as data centers face increasing pushback across the country. A survey released in May showed that over 70 percent of Americans oppose construction of new data centers in their area, often because of concerns over pollution, noise, and water and electricity use.
Rather than consolidating computing power into one large data center, Sunrun’s program would spread it out in smaller compute units dotted all over the country. Sunrun says it previously ran a “successful” proof of concept for this program, but it’s not yet clear how well it will work. This is a totally new area for Sunrun, which mainly focuses on home energy storage. Sunrun’s 1.1 million customers can sign up for the pilot program waitlist if they’re open to hosting a compute node. The company says it “expects to complete the pilot over the coming months and will assess results” before rolling out the program more widely.
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- Stevie Bonifield
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