The simplest way to picture it: imagine emptying an Olympic swimming pool with a bucket.
A real example from the 16B run. Our monitoring picked up that a specific set of GPUs from one provider had a networking fault, bandwidth on those mac
Here's the counterintuitive part: a machine doesn't have to be online 100% of the time to be useful for training.
In a distributed run, this matters more, because machines depend on each other.
There is a quiet assumption when it comes to most AI training: that the compute you're using is stable, uniform, and always there.
Here's what that looks like live: a miner dropped offline mid-round.
The single hardest piece is the merge: reconciling thousands of independently trained copies of the model while some of the machines that held them ar
What handles them:
Three engineering problems dominate everything else:
The thing that makes this survivable over long-distance links is the training loop itself.
The thesis it's grounded in real runs and research, not whiteboard claims:
It's not a wishlist.
Distributed training is possible, that question is settled.