Engineering notes, release reports, and protocol writing from the team building the AI-native post-quantum L1. Concrete, verifiable, honest about gaps.
We designed and built the stack to serve a 2.8T-parameter open MoE on ordinary 12GB consumer GPUs, paid per token on-chain, owned by no one and served by everyone. The architecture, the honest memory maths, and what is built versus what still needs a fleet.
For the first time, a fleet of ordinary 8GB GPUs trained an improvement into the model qbc.network serves, and it passed a full evaluation, went live, was attested on-chain, and its weights are public. Here is the milestone, and why the method points at something much larger.
Multi-worker decentralized AI training is live: three independent machines train one model, each signing work with a post-quantum key and earning QBC. One has no GPU at all, and it earns anyway.
The first on-chain reward for decentralized AI training is live on QBC: a model checkpoint committed, attested by validator quorum, finalized, and paid. Every step is checkable on-chain, fully automatic.
Decentralized AI training that pays: run the client, contribute your GPU, and every epoch that measurably improves AethersMind pays out in QBC. How the loop works, what is live today, and why the incentives hold as the network grows.