SAN FRANCISCO, March 27 (Reuters) - Artificial

intelligence benchmarking group MLCommons on Wednesday released

a fresh set of tests and results that rate the speed at which

top-of-the-line hardware can run AI applications and respond to

users.

The two new benchmarks added by MLCommons measure the speed

at which the AI chips and systems can generate responses from

the powerful AI models packed with data. The results roughly

demonstrate to how quickly an AI application such as ChatGPT can

deliver a response to a user query.

One of the new benchmarks added the capability to measure

the speediness of a question-and-answer scenario for large

language models. Called Llama 2, it includes 70 billion

parameters and was developed by Meta Platforms ( META ).

MLCommons officials also added a second text-to-image

generator to the suite of benchmarking tools, called MLPerf,

based on Stability AI's Stable Diffusion XL model.

Servers powered by Nvidia's ( NVDA ) H100 chips built by the likes of

Alphabet's Google, Supermicro and Nvidia ( NVDA )

itself handily won both new benchmarks on raw

performance. Several server builders submitted designs based on

the company's less powerful L40S chip.

Server builder Krai submitted a design for the image

generation benchmark with a Qualcomm AI chip that draws

significant less power than Nvidia's ( NVDA ) cutting edge processors.

Intel ( INTC ) also submitted a design based on its Gaudi2

accelerator chips. The company described the results as "solid."

Raw performance is not the only measure that is critical

when deploying AI applications. Advanced AI chips suck up

enormous amounts of energy and one of the most significant

challenges for AI companies is deploying chip that deliver an

optimal amount of performance for a minimal amount of energy.

MLCommons has a separate benchmark category for measuring

power consumption.