Google Says Its AI Supercomputer is Faster, Greener Than Nvidia A100 Chip
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Alphabet’s Google released new details about the supercomputers it uses to train its artificial intelligence models, saying the systems are both faster and more power-efficient than comparable systems from Nvidia. From a report:
Google has designed its own custom chip called the Tensor Processing Unit, or TPU. It uses those chips for more than 90% of the company’s work on artificial intelligence training, the process of feeding data through models to make them useful at tasks such as responding to queries with human-like text or generating images. The Google TPU is now in its fourth generation. Google on Tuesday published a scientific paper detailing how it has strung more than 4,000 of the chips together into a supercomputer using its own custom-developed optical switches to help connect individual machines.
Improving these connections has become a key point of competition among companies that build AI supercomputers because so-called large language models that power technologies like Google’s Bard or OpenAI’s ChatGPT have exploded in size, meaning they are far too large to store on a single chip. The models must instead be split across thousands of chips, which must then work together for weeks or more to train the model. Google’s PaLM model – its largest publicly disclosed language model to date – was trained by splitting it across two of the 4,000-chip supercomputers over 50 days.