A minimal test with real chips
Google launched a prototype satellite on a SpaceX Falcon 9 rocket from Vandenberg Space Force Base in California on 1 October, carrying four of its own Tensor Processing Units (TPUs) into orbit. The mission, part of a project the company calls Suncatcher, is a first step towards running data centres in space. The satellite was developed with the Earth-imaging company Planet.
The TPUs run a version of Google's Gemma model for about 15 minutes at a time, limited by heat management. In a blog post, the company said the test will measure how the chips handle the physical stress of spaceflight, radiation and the wide temperature swings outside Earth's atmosphere.
Why compute is moving off the planet
AI training and inference need enormous amounts of electricity, and grids in the United States and Europe are straining to keep up. Solar arrays in orbit can collect sunlight continuously, without weather or night-time gaps, which is the main argument for building compute there.
Google plans to send two more satellites in 2027 to test laser links between them, a task the company compared to hitting a coin-sized target from miles away while both points are moving. The approach also raises hard problems: hardware is difficult and expensive to repair in orbit, and launches remain costly.
Competitors are already testing hardware in space
Google is not first. The startup Starcloud launched a spacecraft carrying an Nvidia H100 chip in November 2025 and showed it could run a lightweight AI model from orbit. SpaceX has said it expects to begin deploying orbital AI compute satellites as early as 2028, and Elon Musk has argued that space data centres will be the cheapest way to train AI within a few years.
None of the missions has yet shown that orbital computing can beat ground-based data centres on cost. Google's own engineers describe the path as long and not straightforward.
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