Connito Introduces Decentralized MoE Training Network on Bittensor
shibshib89 · x · 2026-08-05
Connito has launched a new decentralized training network architecture on Bittensor, designed to make distributed training economically viable.
- Core Concept: Based on Mixture-of-Experts (MoE) models, contributors only need to independently train compact, task-relevant "expert subnetworks".
- Composition Mechanism: The learned updates are transferred back into the full model, evaluated in a real routing environment, and composed across domains.
- Sparse Activation: Using Kimi K3 (3.7% activation rate) and GLM-4.5 (9% activation rate) as examples, the post highlights how MoE architectures significantly increase total model capacity while controlling per-token compute overhead.
Related event: Connito Launches Decentralized MoE Training Network(2 posts)→
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