Full title & subtitle:
DeepSeek might not be as disruptive as claimed, firm reportedly has 50,000 Nvidia GPUs and spent $1.6 billion on buildouts
The fabled $6 million was just a portion of the total training cost.
DeepSeek might not be as disruptive as claimed, firm reportedly has 50,000 Nvidia GPUs and spent $1.6 billion on buildouts
The fabled $6 million was just a portion of the total training cost.
Besides, it's FOSS, isn't? Meaning anybody can see how much it takes to run and how much it takes to train?
>A recent claim that DeepSeek trained its latest model for just $6 million has fueled much of the hype. However, this figure refers only to a portion of the total training cost— specifically, the GPU time required for pre-training. It does not account for research, model refinement, data processing, or overall infrastructure expenses.
So the $6 million is correct - and the rest is irrelevant additional costs. Yes, they'd pay for research. They'll pay for data processing. They'll have overal infrastructure expenses. Duh!
Not defending it, but it's a much better situation than some of the non-commercial licensing from e.g. Meta, Stability.
There is Hugging Face's "Open-R1" project which is attempting to replicate the DeepSeek-R1 model given the innovations outlined in the paper(s). They need to replicate the training code, the datasets, etc. This part of the system wasn't published.
And you are right that with the open weights of the R1 model, it should be pretty easy to infer how much it costs to _run_ as well.