Model training and inference spread across the devices taking part. The data never leaves the phone: what travels is the trained adapter, with the proof of each contribution written to the chain.
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Model training and inference spread across the devices taking part. The data never leaves the phone: what travels is the trained adapter, with the proof of each contribution written to the chain.

Models trained and served from our own pipeline, from 4.3M to 113M parameters. The scaling roadmap is a document with its dates, not a promise.

Where the datasets live: whoever contributes data keeps the record of having contributed it, and the training that uses it points back at that record.
pallet-ai-consensus and pallet-ai-subnets record which model answered, with which weights and when, so an inference can be audited instead of trusted.
The assistant built into Aephoron, the ecosystem's browser, with its own engine. It answers from the device and asks the network only when it needs to.






Empoorio has dozens of turnkey products for AI.

How AiloOS splits a model and records each contribution on chain

Which model answered, with which weights, written to the chain