DeepSeek-V3 costs only 2.788M GPU hours for its full training. Assuming the rental price of the H800 GPU is $2 per GPU hour, our total training costs amount to only $5.576M. Note that the aforementioned costs include only the official training of DeepSeek-V3, excluding the costs associated with prior research and ablation experiments on architectures, algorithms, or data.
Emphasis mine. Deepseek was very upfront that this 6m was training only. No other company includes r&d and salaries when they report model training costs, because those aren’t training costs
consider this paragraph from the Wall Street Journal:
DeepSeek said training one of its latest models cost $5.6 million, compared with the $100 million to $1 billion range cited last year by Dario Amodei, chief executive of the AI developer Anthropic, as the cost of building a model.
you’re arguing to me that they technically didn’t lie – but it’s pretty clear that some people walked away with a false impression of the cost of their product relative to their competitors’ products, and they financially benefitted from people believing in this false impression.
Okay I mean, I hate to somehow come to the defense of a slop company? But WSJ saying nonsense is really not their fault, like even that particular quote clearly says “DeepSeek said training one” cost $5.6M. That’s just a true statement. No one in their right mind includes the capital expenditure in that, the same way when you say “it took us 100h to train a model” that doesn’t include building a data center in those 100h.
Beside whether they actually lied or not, it’s still immensely funny to me that they could’ve just told a blatant lie nobody factchecked and it shook the market to the fucking core wiping off like billions in valuation. Very real market based on very real fundamentals run by very serious adults.
i can admit it’s possible i’m being overly cynical here and it is just sloppy journalism on Raffaele Huang/his editor/the WSJ’s part. but i still think that it’s a little suspect on the grounds that we have no idea how many times they had to restart training due to the model borking, other experiments and hidden costs, even before things like the necessary capex (which goes unmentioned in the original paper – though they note using a 2048-GPU cluster of H800’s that would put them down around $40m). i’m thinking in the mode of “the whitepaper exists to serve the company’s bottom line”
btw announcing my new V7 model that i trained for the $0.26 i found on the street just to watch the stock markets burn
but it’s pretty clear that some people walked away with a false impression of the cost of their product relative to their competitors’ products
Ask yourself why that may be, as you are the one who posted a link to a WSJ article that is repeating an absurd 100m-1b figure from a guy who has a vested interest in making the barrier of entry into the field seem as high as possible the increase the valuation of his company. Did WSJ make an attempt to verify the accuracy of these statements? Did it push for further clarification? Did it compare those statements to figures that have been made public by Meta and OpenAI? No on all counts - yet somehow “deepseek lied” because it explicitly stated their costs didn’t include capex, salaries, or R&D, but the media couldn’t be bothered to read to the end of the paragraph
“the media sucks at factchecking DeepSeek’s claims” is… an interesting attempt at refuting the idea that DeepSeek’s claims aren’t entirely factual. beyond that, intentionally presenting true statements that lead to false impressions is a kind of dishonesty regardless. if you mean to argue that DeepSeek wasn’t being underhanded at all and just very innocently presented their figures without proper context (that just so happened to spurn a media frenzy in their favor)… then i have a bridge to sell you.
besides that, OpenAI is very demonstrably pissing away at least that much money every time they add one to the number at the end of their slop generator
