Jan 13 (Reuters) - The U.S. government said on Monday it

would issue a new regulation designed to control access to

U.S.-designed artificial intelligence chips and technology by

other countries around the world.

The rule regulates the flow of American AI chips and

technology needed for the most sophisticated AI applications.

Here are more details on the U.S. action:

WHICH CHIPS ARE RESTRICTED?

The rule restricts the export of chips known as graphics

processing units (GPUs), specialized processors originally

created to accelerate graphics rendering.

Although known for their role in gaming, the ability of GPUs

such as those made by U.S.-based industry leader Nvidia ( NVDA )

to process different pieces of data simultaneously has made them

valuable for training and running AI models.

OpenAI's ChatGPT, for example, is trained and improved on

tens of thousands of GPUs.

The number of GPUs needed for an AI model depends on how

advanced the GPU is, how much data is being used to train the

model, the size of the model itself and the time the developer

wants to spend training it.

WHAT IS THE U.S. DOING?

To control global access to AI, the U.S. is expanding

restrictions on advanced GPUs needed to build the clusters used

to train advanced AI models.

The limits on GPUs for most countries in the new rule are

set by compute power, to account for differences in individual

chips.

Total processing performance (TPP) is a metric used to

measure the computational power of a chip. Under the regulation,

countries with caps on compute power are restricted to a total

of 790 million TPP through 2027.

The cap translates into the equivalent of nearly 50,000 H100

Nvidia GPUs, according to Divyansh Kaushik, an AI expert at

Beacon Global Strategies, a Washington-based advisory firm.

"Fifty thousand H100s is an enormous amount of power -

enough to fuel cutting-edge research, run entire AI companies or

support the most demanding AI applications on the planet," he

said.

Those could include running a global-scale chatbot

service or managing advanced real-time systems like fraud

detection or personalized recommendations for massive companies

like Amazon ( AMZN ) or Netflix ( NFLX ), Kaushik added.

But the caps do not reflect the true limit on the number of

H100 chips in a country. Companies like Amazon Web Services or

Microsoft's ( MSFT ) Azure cloud unit that meet the requirements

for special authorizations - also known as "Universal Verified

End User" status - are exempt from the caps.

National authorizations also are available to companies

headquartered in any destination that is not a "country of

concern." Those with national Verified End User status have caps

of roughly 320,000 advanced GPUs over the next two years.

"The country caps are specifically designed to encourage

companies to secure Verified End User status," Kaushik said,

providing greater visibility to U.S. authorities about who is

using them and helping to prevent GPUs from being smuggled into

China.

ARE THERE OTHER EXCEPTIONS TO THE LICENSING?

Yes. If a buyer orders small quantities of GPUs - the

equivalent of up to some 1,700 H100 chips - they will not count

toward the caps, and only require government notification, not a

license.

Most chip orders fall below the limit, especially those

placed by universities, medical institutions, and research

organizations, the U.S. said. This exception is designed to

accelerate low-risk shipments of U.S. chips globally.

There also are exceptions for GPUs for gaming.

WHICH PLACES CAN GET UNLIMITED AI CHIPS?

Eighteen destinations are exempt from country caps on

advanced GPUs, according to a senior administration official.

Those are Australia, Belgium, Britain, Canada, Denmark,

Finland, France, Germany, Ireland, Italy, Japan, the

Netherlands, New Zealand, Norway, South Korea, Spain, Sweden and

Taiwan plus the United States.

WHAT IS BEING DONE WITH 'MODEL WEIGHTS'?

Another item being controlled by the U.S. is known as "model

weights." AI models are trained to produce meaningful material

by being fed large quantities of data. At the same time,

algorithms evaluate the outputs to improve the model's

performance.

The algorithms adjust numerical parameters that weigh the

results of certain operations more than others to better

complete tasks. Those parameters are model weights. The rule

sets security standards to protect the weights of advanced

"closed-weight", or non-public, models.

Overall, Kaushik said, the restrictions are aimed at

ensuring the most advanced AI is developed and deployed in

trusted and secure environments.