BEIJING, Sept 18 (Reuters) - Chinese AI developer

DeepSeek said it spent $294,000 on training its R1 model, much

lower than figures reported for U.S. 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.

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 ( NVDA ).

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

$294,000 to train and used 512 Nvidia H800 chips. A previous

version of the article published in January did not contain this

information.

Sam Altman, CEO of U.S. AI giant OpenAI, said in 2023 that

what he called "foundational model training" 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.

Some of Deepseek's statements about its development

costs and the technology it used have

been questioned

by U.S. companies and officials.

The H800 chips it mentioned were designed by Nvidia ( NVDA ) for the

Chinese market after the U.S. in October 2022 made it illegal

for the company to export its more powerful H100 and A100 AI

chips to China.

U.S. officials told Reuters in June that DeepSeek has access

to "large volumes" of H100 chips that were procured after U.S.

export controls were implemented. Nvidia ( NVDA ) 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 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.

Reuters has previously reported that one reason DeepSeek was

able to attract the brightest minds in China was because it was

one of the few domestic companies to operate an A100

supercomputing cluster.