Quasar Commits to a 10-Trillion-Token Decentralized Training Run
The subnet built around Bittensor's "long-context barrier" is betting that token volume and data quality matter more than parameter count.
BitExplorer · Jul 26, 2026
Quasar, which bills itself as tackling Bittensor's long-context barrier, announced what it's calling its next chapter: a push toward a 10-trillion-token decentralized training run.
The team's stated priority is explicit: "Quasar Models needs more useful training, not just bigger parameter counts." The argument is that real model quality comes from token volume and data quality, not simply scaling up parameters — a different axis than the "bigger model" framing that dominates most AI infrastructure coverage.
It's a genuinely large commitment relative to most Bittensor subnet announcements, and one worth watching alongside how other compute-focused subnets are approaching training economics — Chutes optimized for cost per training run, Quasar is optimizing for scale of data instead.
Check the Quasar project page →
Source: @QuasarModels · Jun 11, 2026