Distributed Training Turns Idle GPUs Into Compute Power
bittingthembits · x · 2026-07-17
The post recaps progress on IOTA SN9's Project Orion, claiming they completed a **100B-parameter model pre-training** across ordinary GPUs on the "open internet"—one of the largest distributed LLM pre-training runs known to date. The author emphasizes that the significance of this **liquid training** goes beyond simply "buying more GPUs"; it transforms fragmented, interruptible, and traditionally inefficient compute into usable training capacity. This run reportedly achieved roughly **65%** of the speed of traditional data center solutions, yet internal testing indicates the final model quality remains on par with conventional methods. The post further suggests that if scaled, this could completely rewrite the cost structure of AI training and GPU economics.
Related event: Bittensor Pushes Liquid Compute and 100B Distributed Pretraining(2 posts)→
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