Into:Connito
MoE models, trained by a swarm
As of · Jun 14, 09:22 UTC
Most Bittensor run inference on models that already exist. Connito is trying to do the harder thing: train frontier-scale language models from scratch, by splitting them into expert pieces and handing each piece to a different .
What is Connito
Connito, registered as Subnet 102, is a network for training large language models without a central data center. Instead of one operator renting a giant GPU cluster, the model is broken into specialized "expert" modules, and independent miners around the network each train a piece. stitch the contributions together and score who did useful work.
The simple version: Picture a giant brain that is too big to fit in any single computer. Connito chops the brain into specialist sections, one good at code, one at math, one at language, and lets different people train each section on their own hardware. A router learns which specialist to ask for any given question.
Centralized equivalent: The frontier labs (OpenAI, Google, Meta) that train huge models on tightly controlled GPU clusters. Connito is attempting the decentralized version of that, where no single party owns the compute.
How it works:
- Miners train expert modules of a Mixture-of-Experts model on their own hardware and submit their contributions, per the subnet's repository.
- Validators coordinate the training rounds, aggregate the submitted contributions, and score miners. Those scores set the weights that drive how are distributed.
Why This Matters
Other research from the same neighborhood of the network.
