AI and generative AI dominate virtually every enterprise IT strategy discussion these days. Long the solution looking for a problem, the use cases for AI have started to emerge and will likely unlock countless opportunities for businesses to drive efficiencies, healthcare providers to deliver better healthcare and governments to serve their constituencies better. While this may sound a little like the typical hyperbole from AI industry pundits, it’s also true.
Part of the infrastructure challenge is tied directly to what we’ve experienced with Nvidia’s meteoric rise: product availability. When I speak to customers, it’s not unusual to hear about 12-month wait times for Nvidia H100s. This is for both enterprise IT and cloud providers alike. When the product is finally received, it’s feasible that some of these organizations will lose most of the product’s value as next-generation parts become available in the market.
It is important to provide these challenges as a backdrop because that sets the tone for Intel's value proposition with Gaudi 3.As mentioned, Gaudi 3 is an AI accelerator, not a GPU. It is designed from the ground up for one thing—to make AI tasks of training, tuning and inference run faster. From my perspective, Gaudi 3 is an accelerator that seems to have been built with the enterprise datacenter in mind.
The company’s claims for inference are equally impressive, showing Llama2 7B inferencing slightly faster than on Nvidia, while Llama2 70B will infer up to 1.7x faster. Most impressively, the Falcon 180B model inferences up to 4x faster. While performance-per-dollar metrics are not yet available because pricing hasn't been published, I think we can safely say that Gaudi will not be priced at the $30,000 or so where Nvidia is pricing its GPUs. I will bet on this one.
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