OLMoE runs as a mixture-of-experts architecture, activating only 1 billion parameters per forward pass despite housing 7 billion total — making it notably efficient at inference time. It carries the transparency hallmark of Allen AI's research lineage, with open weights and open training details that let developers inspect what they're actually running.