Learn about our early test to scale AI compute in space. In a new video series, the Project Suncatcher team explores the science behind this moonshot, what they've found, and the engineering challenges that remain to be solved.

After years of research, Project Suncatcher is scheduled to embark on its first test in orbit, launching a prototype satellite to evaluate how Google Tensor Processing Units (TPUs) perform in space. Announced last year, Project Suncatcher is a long-term, research moonshot exploring whether space could one day host scalable machine learning infrastructure. In low Earth orbit, satellites can access near-constant sunlight, generating up to eight times more solar power than on Earth. This initial mission onboard the upcoming Transporter-18 rideshare mission with Space X was developed in partnership with Planet.

Hardware survival: A rocket trip into low Earth orbit lasts about 10 minutes, during which the spacecraft experiences intense vibration and acceleration loads up to 10 times the force of gravity. The team conducted vibration testing by intensely shaking the satellite on all three axes. Once in space, radiation presents another challenge. Our team tested TPUs in a proton beam facility at UC Davis’s Crocker Nuclear Laboratory while running AI workloads. Initial results have shown that our Trillium TPUs hold up remarkably well, and can survive a radiation total ionizing dose greater than what they would receive during a five-year space mission.

Cooling in space: TPUs generate a large amount of heat, but in a vacuum, you can only diffuse heat via radiators. We’re working on a number of different approaches, including a combination of heat pipes and radiators. As we prepare for an early test launch and work toward our next milestone in 2027, the Project Suncatcher team discussed what we hope to learn and the engineering hurdles ahead.