BEIJING: Chinese AI developer DeepSeek said it spent US$294,000 on training its R1 model, much lower than figures reported for US rivals, in a paper that is likely to reignite debate over Beijing’s place in the race to develop artificial intelligence.
The rare update from the Hangzhou-based company – the first estimate it has released of R1’s training costs – appeared in a peer-reviewed article in the academic journal Nature published on Wednesday (Sep 17).
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 founder Liang Wenfeng have largely disappeared from public view, apart from pushing out a few new product updates.
The Nature article, which listed Liang as one of the co-authors, said DeepSeek’s reasoning-focused R1 model cost US$294,000 to train and used 512 Nvidia H800 chips. A previous version of the article published in January did not contain this information.
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.
Sam Altman, CEO of US AI giant OpenAI, said in 2023 that the training of foundational models had cost “much more” than US$100 million – though his company has not given detailed figures for any of its releases.
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 US in October 2022 made it illegal for the company to export its more powerful H100 and A100 AI chips to China.
US officials told Reuters in June that DeepSeek has access to “large volumes” of H100 chips that were procured after US export controls were implemented. Nvidia told Reuters at the time that DeepSeek has used lawfully acquired H800 chips, not H100s.
In a supplementary information document accompanying the Nature article, the company acknowledged for the first time it does own A100 chips and said it had used them in preparatory stages of development.
“Regarding our research on DeepSeek-R1, we utilised the A100 GPUs to prepare for the experiments with a smaller model,” the researchers wrote.