Connito uses expert decentralization — contributors train specialized expert modules that are aggregated into powerful AI systems, without massive centralized compute.
A subnet of independent miners trains expert modules in parallel — no single operator, no monolithic GPU cluster, no central failure point.
Expert partitioning splits a frontier-scale Mixture-of-Experts model into pieces individual miners can actually fit and train, then routes traffic across them.
Each expert is optimized for a domain. The router learns which expert to ask — keeping the strengths of fine-tuning without paying for catastrophic forgetting.