Following the enormous success of OpenAI's ChatGPT last year, tech giant Amazon was one of the last major tech companies to jump into the generative AI gold rush, introducing its own Titan large language model in April, after Google and Facebook revealed Bard and LLaMA, respectively.
With the pricey and highly sought-after bespoke microchips that drive generative AI models, Amazon is now trying to catch up.
The CNBC reports:
In an unmarked office building in Austin, Texas, two small rooms contain a handful of Amazon employees designing two types of microchips for training and accelerating generative AI. These custom chips, Inferentia and Trainium, offer AWS customers an alternative to training their large language models on Nvidia GPUs, which have been getting difficult and expensive to procure.
“The entire world would like more chips for doing generative AI, whether that’s GPUs or whether that’s Amazon’s own chips that we’re designing,” Amazon Web Services CEO Adam Selipsky told CNBC in an interview in June. “I think that we’re in a better position than anybody else on Earth to supply the capacity that our customers collectively are going to want.”
Amazon thinks that its efforts in the manufacture of customized silicon will give them an advantage when building chips that are AI-optimized.
AWS quietly started production of custom silicon back in 2013 with a piece of specialized hardware called Nitro. It’s now the highest-volume AWS chip. Amazon told CNBC there is at least one in every AWS server, with a total of more than 20 million in use.
Currently, the majority of businesses using generative AI solutions rely on the A100 GPUs from market leader Nvidia, which sell for about $10,000 each. Nvidia has joined the exclusive club of $1 trillion corporations thanks to the surge in massive language models and other AI technologies.
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