That’s why we built OlmoEarth v1.1: a new family of models that cuts compute costs by up to 3x while maintaining OlmoEarth v1's performance on a mix of research benchmarks and tasks we’ve constructed with partners.

The OlmoEarth models are transformer-based models, one of the dominant architectures in machine learning today. To process remote sensing data, we first convert it into a sequence of tokens the model can ingest.

Two important levers control efficiency in transformer-based models: model size (this is why we release a family of models, so users can pick the size that fits their compute budget) and token sequence length. Compute costs scale quadratically with the token sequence length, so even small reductions can meaningfully cut the cost of running the model.

MACs, or multiply-accumulate operations, estimate the computation needed for one model forward pass; lower MACs generally mean cheaper, faster inference. The y-axis is inverted because lower average rank is better. Labels show model family and size. All plotted points use the pasted MAC/rank values.