Access to Nvidia graphics processing units (GPUs), often through a cloud computing provider, to train and deploy large AI models used for applications such as OpenAI’s ChatGPT It can be difficult to obtain and expensive to execute, a process developers refer to as inference.
“We’re delivering performance that you can’t get with a GPU,” Cerebras CEO Andrew Feldman told Reuters in an interview. “We’re doing it with the highest precision and offering it at the lowest price.”
The inference part of the AI market is expected to grow rapidly and become attractive, ultimately worth tens of billions of dollars if consumers and businesses adopt AI tools.
The Sunnyvale, California-based company plans to offer several types of inference products through a developer key and its cloud. The company will also sell its AI systems to customers who prefer to operate their own data centers.
Cerebras’ chips (each about the size of a dinner plate and called Wafer Scale Engines) avoid one of the problems with AI data processing: The data processed by large models that power AI applications typically can’t fit on a single chip and can require hundreds or thousands of chips connected together.
That means Cerebras chips can achieve faster throughputs, Feldman said.
Users are planned to be charged just 10 cents per million tokens, which is one way companies can measure the amount of data output from a large model.
Cerebras plans to go public and filed a confidential prospectus with the Securities and Exchange Commission this month, the company said.
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