alt Hacker NewsBut people have been saying this about CUDA for twenty years, and we are not any closer to a replacement GPGPU paradigm today.
The root comment in this thread was about Nvidia hedging their bet on lost AI market share. They recognize that a reduced pace in training and inference competition will undercut their business, but CUDA isn't a one-trick pony for LLMs alone. TPUs are - you can't even reuse the same architecture for training and inference, they're separate ASICs unlike CUDA cores/ALUs. Veterans of crypto mining will tell you that the ASICs lost in the end, as Nvidia was evolving their hardware faster than the ASIC manufacturers could iterate. When the crypto acceleration landscape diversified away from ETH/BTC into altcoins, Nvidia was still there making money hand-over-fist from mining hardware.
I guess you could argue that robotics, world models or computer vision won't be a trillion-dollar market. But Nvidia is positioned to be the first mover in all of these markets, and none of their competitors are even coming close to the integrated stack that they sell consumers.
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> you can't even reuse the same architecture for training and inference
AWS begs to differ. They originally split between `Trainium` and `Inferentia` but now support both with `Trainium`
> But people have been saying this about CUDA for twenty years, and we are not any closer to a replacement GPGPU paradigm today.
How much money was in it for the first decade or so? I think AMD was asleep at the switch but e.g. Apple just did their own thing for the parts which they prioritized.
My understanding is also that Anthropic and OpenAI have also worked to decouple themselves so I think it’s likely that the CUDA moat is going to be less of a barrier than it used to be from the perspective of guaranteeing Nvidia profits.