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.
This is the actual argument for fault tolerance: you don't get to assume every machine behaves, all the time.
Upon resuming, we rolled back to an earlier checkpoint to rule out any corrupted optimizer state or local weights from the pause.
What's notable is what didn't happen. No machine needed to be restarted.
A backend infrastructure incident interrupted the run. For a period, the entire system sat in a holding pattern while we fixed it.
Our distributed training runs use compute that iota doesn't own and can't fully control. Machines join, leave, stall, and occasionally fail – these ar
What @IOTA_SN9 is proving with Orion’s 16B run: with the right tuning, a FLOP is a FLOP.
Critically, the system has to keep working when things don't go to plan:
RT @macrocrux: We just kicked off our first ever external competition on SN1 -- we've partnered with @AureliusAligned to create an LLM ste…
Hash Rate - Ep. 176: Macrocosmos IOTA (SN9) + APEX (SN1) $TAO
Aurelius will be welcoming a new community of miners and AI researchers in just a few days. We could not be more excited about this collaboration with