Zero evidence that the above statement is true, and weak evidence (authors' claims) that it is false. Have you read their papers even?
https://arxiv.org/html/2412.19437v1#abstract https://arxiv.org/pdf/2501.12948
This includes salary for ~130 ML people + rest of the staff, company is 2 years old. They have trained DeepSpeek V1, V2, R1, R1-Zero before finally training V3, as well as a bunch of other less known models.
The final run of V3 is ~6M$ (at least officially...[1]), but that does not factor the cost of all the other failed runs, ablations etc. that always happen when developing a new model.
You also can't get clusters of this size with a 3 weeks commitment just to do your training and then stop paying for it, there is always a multi-month (if not 1 year) commitment because of demand/supply. Or, if it's a private cluster they own it's already a $200M-300M+ investment just for the advertised 2000 GPUs for that run.
I don't know if it really is $1B, but it certainly isn't below $100M.
[1] I personally believe they used more GPUs than stated, but simply can't be forthcoming about this for obvious reason. I have of course not proof of that, my belief is just based on scaling laws we have seen so far + where the incentives are for stating the # of GPUs. But even if the 2k GPUs figure is accurate, it's still $100M+
Is that true? No idea. But there isn't zero evidence.