Reuters
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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 updates.
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 R1 model 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.
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.
