The artificial intelligence benchmarking group MLCommons published new test results and findings on Wednesday, rating how quickly the latest technology can execute AI apps and react to user input.

The two new standards introduced by MLCommons gauge how quickly AI chips and systems can provide replies from robust, data-rich AI models. The findings essentially show how fast an AI programme like ChatGPT can respond to a user’s inquiry.

Measuring the speed of a question-and-answer scenario for large language models was added to one of the new benchmarks. Named Llama 2, it was created by Meta Platforms and has 70 billion parameters.

Officials from MLCommons have also included MLPerf, a second text-to-image generator based on Stability AI’s Stable Diffusion XL model, in the collection of benchmarking tools.

Nvidia’s H100 chips were used in servers manufactured by companies like Google, Supermicro, and Nvidia, which easily set new performance records. Many server builders that used the company’s less potent L40S processor submitted designs.

A design for the image generation benchmark using a Qualcomm AI chip—which uses a lot less energy than Nvidia’s state-of-the-art processors—was submitted by server builder Krai.

A design based on Intel’s Gaudi2 accelerator chips was also submitted. The business called the outcomes “solid.”

When implementing AI systems, there are other metrics that are just as important as raw performance.

The deployment of chips that provide optimal performance while consuming the least amount of energy is a major challenge for AI firms, as advanced AI chips consume vast quantities of energy.

(Adapted from EconomiocTimes.com)

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