Reducing carbon emissions faster at the MIT Sustainable Design Lab

MIT’s Sustainable Design Lab develops AI-driven urban building energy models through its UBEM.io platform to help cities plan retrofits, shape policy, and reduce carbon emissions. Previously, the lab’s sophisticated simulations were slowed by 36-hour training runs, difficult debugging, and onboarding processes that could take up to two weeks. By adopting Lightning AI Studios for on-demand cloud computing and PyTorch Lightning for simplified, scalable multi-GPU training, the team reduced model training time to six hours, cut onboarding time by 90 percent, and shortened prototype-to-production cycles from roughly three weeks to two. The improved workflow enables researchers to focus more on testing and refining city-scale geospatial models, supporting faster development of tools intended to help municipalities decarbonize.

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