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I'd be interested to hear more about that, it's not something I've seen claimed before. I expect that when you're already running clusters of thousands of GPUs, going to tens or hundreds of thousands doesn't require too much effort.
It genuinely is impressive how xAI managed to extremely rapidly acquire and set up 100k H100s, and then add another 100k in 92 days. That's probably the one place where Elon's supervision paid dividends, he looked at the power issues in the way and cut the Gordian knot by having portable diesel generators be brought in to power the clusters until more permanent solutions were available.
Going from non-existent as competition to releasing a SOTA model in about less than a year is nigh-miraculous, even if it's a very temporary lead. I found Grok 2 to be a rather underwhelming model, so I'm pleasantly surprised that 3 did better, even if it's not a major leap ahead.
I wouldn't say DeepSeek is ahead. Their model didn't beat o1 pro, nor the upcoming o3. They did absolutely light a fire under the existing incumbent's asses by releasing a nearly as good model for the low, low price of free.
It's coordinating them that is the issue. You don't give them a big queue of work and let them churn through it independently for a month. Each step of training has to happen at the same time, with all GPUs in the cluster dependent on the work of others. It's much more like one giant computer than it is like a bunch of computers working together.
In those conditions you have lots of things that get more and more painful as you scale up. I specialize in storage. Where for most applications we might optimize for tail latencies, like ensuring the 99.9th percentile of requests complete within a certain target, for AI we optimize for max(). If one request out of a million is slow it slows down literally everything else in the cluster. It's not just the one GPU waiting on data that ends up idling, the other 99,999 will too.
You also have the problem that if one computer breaks during the training run you need to go recompute all the work that it did on the step that it broke on. Folks are coming up with tricks to get around this, but it introduces a weird tradeoff of reliability and model quality.
And of course constructing and maintaining the network that lets them all share the same storage layer at ultra high bandwidth and ultra low latencies is nontrivial.
Also at least in my corner of AI folks are skeptical that xAI actually operated their 100k GPUs as a single cluster. They probably had them split up. 5 20K GPU clusters is a different beast than 1 100K GPU cluster.
Each step of training = each mini batch?
I've wondered why everything has to be synchronized. You could imagine each GPU computes its gradients, updates them locally, and then dispatches them to other GPUs asynchronously. Which presumably doesn't actually work, since synchronization is such a problem, but that's surprising to me. Why does it matter if each local model is working on slightly stale parameters? I'd expect there to be a cost, but one less than those you describe.
I have pretty rudimentary knowledge of the training itself outside of storage needs, but my understanding is that each node in the neural net is linked to all the other nodes in the next stage of processing. So when you’re training you need to adjust the weights on all the nodes in the net, run the data through the adjusted weights, see if it’s a better result, rinse and repeat.
There isn’t a local model, the model is distributed across the cluster.
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