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How much of the big expensive training jobs are CUDA specific? If it’s billions of dollars of compute, rewriting the software to use whatever hardware is cheape

• vineyardmike 3y ago
  It takes time to re-engineer an entire ecosystem of tools. The whole 9-pregnant ladies in 1 month analogy comes to mind. If you’re trying to accomplish a goal, how long are you willing to wait for your entire dependency tree to be engineered in-house. It’s happening slowly, but teams have to ship, and can’t wait for other teams to build fresh tools. Additionally, the compute hardware is rented, and if there’s no alternatives available for rent it doesn’t matter. Data centers aren’t full of NVidia and AMD GPUs and TPUs (because the support isn’t there). It’s a crazy chicken/egg situation where everyone benefits but no one makes the move. It’s slowly happening, but it’s not there yet to totally replace them.
  • delfinom 3y ago
    That's the thing, they can be working on multiple paths in parallel. They can be building on nvidia and have a semiconductor team in another corner experimenting for alternate in the future. When profit margins are insane, there is always competition quietly fomenting. We just won't know until they release it because it's also a competitive advantage to keep your own plans underwrap until it's ready. Or else it may start an arms race that'll only drive up costs to get done faster.