Dylan Patel, founder of SemiAnalysis, discusses how hyperscalers optimize inference workloads for non-NVIDIA chips like AMD by investing massive engineering effort on a small set of fixed models.
“Microsoft has deployed GPT-style models on On other competitors' hardware, such as AMD they've, and some of their own, but mostly AMD, and so they can wring that out with software because they can spend hundreds of engineers, dozens of engineers' hours hundreds of engineer hours, or thousands of engineer hours on working this out, because it's such a unified sort of workload, right?”
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More from Dylan Patel
Opinion
Patel: Every semiconductor company except NVIDIA is terrible at software
“I would say every semiconductor company in the world sucks at software except for NVIDIA, right?”
Patel: AMD lacks software talent and refuses to fund internal GPU clusters
“AMD is really good, but they're missing software. AMD has no clue how to do software, I think. They've got very few developers on it. They won't spend the money to build a GPU cluster for themselves so that they can develop software.”
“Google's brought in a level of reliability that NVIDIA GPUs don't have. You know, the dirty secret is to go ask people what the reliability rate of GPUs is in the cloud or in a deployment. It's like, oh God, it is not, they're reliable-ish, like, but like, esp…”
Patel: NVIDIA's Jensen Huang only plans 12 to 18 months ahead
“Well, the funny thing is a lot of people at NVIDIA will say Jensen doesn't plan more than a year or year and a half out. Because they change things and they'll deploy them out that fast, right? No semi, every other semiconductor company takes years to deploy, …”
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