Cerebras breaks record for largest AI models trained on a single device

Cerebras said this can reduce the engineering time to run large natural language processing models from months to minutes, making it more cost-effective.

Cerebras Systems claims it has achieved a new feat, by training an AI model with up to 20bn parameters on a single CS-2 system.

The AI ​​company said using a single system can reduce the engineering time and effort when training natural language processing (NLP) models from months to minutes.

NLP can enable computers to process human language in either text or voice data and understand its full meaning.

Cerebras CEO Andrew Feldman said bigger NLP models are shown to be more accurate, but this means only select companies have the resources and expertise to do the “painstaking work” required.

“As a result, only very few companies could train large NLP models – it was too expensive, time-consuming and inaccessible for the rest of the industry,” Feldman said.

Cerebras said its latest result will help eliminate one of the “most painful aspects” of training large NLP models, which usually involves spreading it across hundreds or thousands of different GPUs.

The company added that the process of partitioning a model across GPUs is unique to each network compute cluster pair, so the work cannot be ported to different clusters or across neural networks.

Cerebras said the ability to train a large model on a single device is possible thanks to its WSE-2 processor, which the company said is the largest processor ever built. It is 56 times larger, has 2.55trn more transistors and has 100 times as many compute cores as the largest GPU.

Last year, Cerebras said it was using the WSE-2 processor to power a new chip cluster that it said can “unlock brain-scale neural networks”.

The AI ​​company said that this processor means that a single CS-2 could support models with hundreds of billions or even trillions of parameters. Intersect360 Research CRO Dan Olds said this could give organizations an “easy and inexpensive on-ramp to major league NLP”.

“Cerebras’ ability to bring large language models to the masses with cost-efficient, easy access opens up an exciting new era in AI,” Olds said. “It will be interesting to see the new applications and discoveries CS-2 customers make as they train GPT-3 and GPT-J class models on massive datasets.”

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