SAN FRANCISCO, June 4 (Reuters) - Nvidia's ( NVDA )

newest chips have made gains in training large artificial

intelligence systems, new data released on Wednesday showed,

with the number of chips required to train large language models

dropping dramatically.

MLCommons, a nonprofit group that publishes benchmark

performance results for AI systems, released new data about

chips from Nvidia ( NVDA ) and Advanced Micro Devices ( AMD ), among

others, for training, in which AI systems are fed large amounts

of data to learn from. While much of the stock market's

attention has shifted to a larger market for AI inference, in

which AI systems handle questions from users, the number of

chips needed to train the systems is still a key competitive

concern. China's DeepSeek claims to create a competitive chatbot

using far fewer chips than U.S. rivals.

The results were the first that MLCommons has released about

how chips fared at training AI systems such as Llama 3.1 405B,

an open-source AI model released by Meta Platforms ( META ) that

has a large enough number of what are known as "parameters" to

give an indication of how the chips would perform at some of the

most complex training tasks in the world, which can involve

trillions of parameters.

Nvidia ( NVDA ) and its partners were the only entrants that submitted

data about training that large model, and the data showed that

Nvidia's ( NVDA ) new Blackwell chips are, on a per-chip basis, more than

twice as fast as the previous generation of Hopper chips.

In the fastest results for Nvidia's ( NVDA ) new chips, 2,496

Blackwell chips completed the training test in 27 minutes. It

took more than three times that many of Nvidia's ( NVDA ) previous

generation of chips to get a faster time, according to the data.

In a press conference, Chetan Kapoor, chief product officer

for CoreWeave ( CRWV ), which collaborated with Nvidia ( NVDA ) to produce some of

the results, said there has been a trend in the AI industry

toward stringing together smaller groups of chips into

subsystems for separate AI training tasks, rather than creating

homogenous groups of 100,000 chips or more.

"Using a methodology like that, they're able to continue to

accelerate or reduce the time to train some of these crazy,

multi-trillion parameter model sizes," Kapoor said.