2026. január 5. · MI Történik? · 3 perc olvasás
Could a decentralized AI training run ever rival the compute of a frontier training run? Probably not. But could decentralized training runs get far larger and support the development of more capable models developed by a much larger collective than just the frontier AI companies of today? Yes. That’s the conclusion of a nice research analysis from Epoch AI which has analyzed about 100+ research technical papers on decentralized training.
The most important takeaway is that decentralized training is growing quickly relative to frontier AI training, with decentralized training runs growing their compute by 20X a year versus 5X a year for frontier training runs. But the other important takeaway is that the sizes of these things are completely different - today’s decentralized training runs are still about 1000X smaller than frontier ones. “While technically feasible, reaching the frontier of compute requires an astounding amount of resources”, Epoch writes. The largest decentralized runs to date have spanned the 6e22-6e23 FLOP range, which they estimate to be 1000x less compute than what was used for Grok 4, a large-scale frontier model. When we look at decentralized training networks, it seems like there’s a capacity issue in terms of compute supply: “The largest such active network we’ve found is Covenant AI’s Templar, which is currently achieving an effective throughput of 9e17 FLOP/s respectively. This is about 300x smaller than frontier AI datacenters today, which have a theoretical training throughput of about 3e20 effective FLOP/s”.
But as readers of this newsletter will know, decentralized training has been going through a rich, fast evolutionary period in recent years. “Since 2020, we have seen a 600,000x increase in the computational scale of decentralized training projects, for an implied growth rate of about 20x/year.”. This is very significant - frontier AI training runs have grown by more than 5x a year. There’s room to grow - if you look at the compute used in the folding@home project (a decentralized attempt to do protein folding), and Bitcoin, you have examples of prior decentralized projects that utilized far more compute, suggesting today’s decentralized runs “could be expanded 30-3,000x in scale, enough to train models on 50-5,000x more compute than today”.
Miért fontos?
Fundamentally, decentralized training is a political technology that will alter the politics of compute at the frontier. Today, the frontier of AI is determined by basically 5 companies, maybe 10 in coming years, which can throw enough compute to train a competitive model in any given 6 month period. These companies are all American today and, with the recent relaxation of export controls on Chinese companies, may also be Chinese in the future. But there aren’t any frontier training runs happening from academic, government, independent, or non-tech-industry actors. Decentralized training gives a way for these and other interest groups to pool their compute to change this dynamic, so following its development is very important. Though it may never truly match the frontier, the closer it gets, the bigger the implications. “Decentralized training could still be a very important part of AI. To the extent that decentralized networks remain associated with open weights, they could lead to larger open models to exist trailing the frontier.”