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But you can't run most models on the consumer AMD GPUs, so even though AMD "allows" it, nobody except supercomputer clusters uses AMD GPUs for compute, because

But you can't run most models on the consumer AMD GPUs, so even though AMD "allows" it, nobody except supercomputer clusters uses AMD GPUs for compute, because all the expensive data scientists you hired will bitch and moan until you get them something they can just run standard CUDA stuff on.

Const-me 3y ago
> expensive data scientists Different people estimate compute costs of ChatGPT to be between $100k and $700k per day. Compared to these numbers, data scientists aren't that expensive. > just run standard CUDA stuff I doubt data scientists have skills to write CUDA, or any other low-level GPGPU code. It's relatively hard to do, and takes years of software development experience to become proficient. Pretty sure most of these people only capable of using higher-level libraries like TensorFlow and PyTorch. For this reason, I don't think these data scientists need or care about standard CUDA stuff, they only need another backend to these Python libraries. Which is much easier problem to solve. And one more thing. It could be that most of ChatGPT costs are unrelated to data scientists, and caused by end users running inference. In that case, the data scientists won't even notice, because they will continue using these CUDA GPUs to develop new versions of their models.