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ARTICLE VIEW:
China’s DeepSeek shook the tech world. Its developer just revealed
the cost of training the AI model
Reuters
Updated:
4:33 AM EDT, Fri September 19, 2025
Source: Reuters
Chinese artificial intelligence developer DeepSeek spent just $294,000
on training its R1 model, much less than reported for US rivals, it
said in a paper that is likely to reignite debate over Beijing’s
place in the AI race.
The rare update from the Hangzhou-based company – the first estimate
it has released of R1’s training costs – appeared Wednesday in a
peer-reviewed article in the academic journal Nature.
DeepSeek’s release of what it said were lower-cost AI systems in
January prompted global investors to dump tech stocks as they worried
the new models could threaten the dominance of AI leaders including
Nvidia.
Since then, the company and its founder Liang Wenfeng have largely
disappeared from public view, apart from pushing out a few product .
Sam Altman, CEO of US AI giant OpenAI, said in 2023 that the training
of foundational models had cost “much more” than $100 million –
though his company has not given detailed figures for any of its
releases.
Training costs for the large language models powering AI chatbots refer
to the expenses incurred from running a cluster of powerful chips for
weeks or months to process vast amounts of text and code.
The Nature article, which listed Liang as one of the co-authors, said
DeepSeek’s reasoning-focused cost $294,000 to train and used 512
Nvidia H800 chips. A previous version of the article published in
January did not contain this information.
Some of DeepSeek’s statements about its development costs and the
technology it used have been questioned by US companies and officials.
The H800 chips it mentioned were designed by Nvidia for the Chinese
market after the United States made it illegal in October 2022 for the
company to export its more powerful H100 and A100 AI chips to China.
US officials told Reuters in June that DeepSeek had access to “large
volumes” of H100 chips procured after US export controls were
implemented. Nvidia told Reuters at the time that DeepSeek had used
lawfully acquired H800 chips, not H100s.
In a supplementary information document accompanying the Nature
article, the company acknowledged for the first time it owns A100 chips
and said it had used them in preparatory stages of development.
“Regarding our research on DeepSeek-R1, we utilized the A100 GPUs to
prepare for the experiments with a smaller model,” the researchers
wrote. After this initial phase, R1 was trained for a total of 80 hours
on the 512 chip cluster of H800 chips, they added.
Model distillation
DeepSeek also responded for the first time, though not directly, to
assertions from a top White House adviser and other US AI figures in
January that it had deliberately “distilled” OpenAI’s models into
its own.
The term refers to a technique whereby one AI system learns from
another, allowing the newer model to reap the benefits of the
investments of time and computing power that went into building the
earlier model, but without the associated costs.
DeepSeek has consistently defended distillation as yielding better
model performance while being far cheaper, enabling broader access to
AI-powered technologies.
DeepSeek said in January that it had used Meta’s open-source Llama AI
model for some distilled versions of its own models.
DeepSeek said in Nature that training data for its V3 model relied on
crawled web pages that contained a “significant number of
OpenAI-model-generated answers, which may lead the base model to
acquire knowledge from other powerful models indirectly.” But it said
this was not intentional but, rather, incidental.
OpenAI did not respond immediately to a request for comment.
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