New AI take a look at measures how briskly robots can reply to consumer instructions


  • A synthetic intelligence group has launched a brand new set of outcomes assessing the velocity of {hardware} in operating AI purposes.
  • Two new benchmarks measure the velocity of AI chips and techniques in producing responses from data-packed AI fashions.
  • One new benchmark additionally evaluates the velocity of question-and-answer eventualities for giant language fashions.

Synthetic intelligence benchmarking group MLCommons on Wednesday launched a contemporary set of assessments and outcomes that price the velocity at which top-of-the-line {hardware} can run AI purposes and reply to customers.

The 2 new benchmarks added by MLCommons measure the velocity at which the AI chips and techniques can generate responses from the highly effective AI fashions full of knowledge. The outcomes roughly exhibit to how rapidly an AI utility reminiscent of ChatGPT can ship a response to a consumer question.

One of many new benchmarks added the aptitude to measure the speediness of a question-and-answer situation for giant language fashions. Referred to as Llama 2, it contains 70 billion parameters and was developed by Meta Platforms.


MLCommons officers additionally added a second text-to-image generator to the suite of benchmarking instruments, referred to as MLPerf, based mostly on Stability AI’s Secure Diffusion XL mannequin.

AI robot

Guests have a look at a robotic by Unitree Robotics throughout the World Synthetic Intelligence Cannes Pageant on Feb. 10, 2023, in Cannes, France. (REUTERS/Eric Gaillard/File picture)

Servers powered by Nvidia’s H100 chips constructed by the likes of Alphabet’s Google, Supermicro and Nvidia itself handily received each new benchmarks on uncooked efficiency. A number of server builders submitted designs based mostly on the corporate’s much less highly effective L40S chip.

Server builder Krai submitted a design for the picture era benchmark with a Qualcomm AI chip that pulls important much less energy than Nvidia’s innovative processors.


Intel additionally submitted a design based mostly on its Gaudi2 accelerator chips. The corporate described the outcomes as “strong.”

Uncooked efficiency shouldn’t be the one measure that’s vital when deploying AI purposes. Superior AI chips suck up huge quantities of vitality and one of the important challenges for AI corporations is deploying chip that ship an optimum quantity of efficiency for a minimal quantity of vitality.

MLCommons has a separate benchmark class for measuring energy consumption.


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