Quasar Models builds decentralized AI training marketplace on Bittensor

Training a competitive AI model typically requires the kind of GPU budget that makes venture capitalists weep. Quasar, operating as Subnet 24 on the Bittensor network, thinks it has a workaround: let a decentralized army of miners do it instead, at a fraction of the cost. The project, developed by SILX AI under the SILX Labs umbrella, has built an open marketplace where independent miners can operate and refine AI models through a competitive evaluation system. Miners who improve model performance get rewarded. Those who don’t, well, they get outcompeted. What Quasar is actually building At its core, Quasar is focused on a specific pain point in AI: long-context foundation models. Quasar is targeting context lengths of approximately 2 million tokens or more. To get there, the team is deploying what it calls Quasar Attention, a novel methodology paired with hybrid architecture approaches designed to push the boundaries of what decentralized training can produce. The Quasar-3B architecture launched in April 2026, following the release of the Quasar-Preview model as a Mixture-of-Experts checkpoint on Hugging Face. It’s computationally efficient, which matters a lot when your training ...