Yes and no. Read his notes about his goals and what he didn't implement, and note that his job when he wrote that was as part of a team that ports models to dif

Yes and no. Read his notes about his goals and what he didn't implement, and note that his job when he wrote that was as part of a team that ports models to different hardware. That's a smaller job than writing a shippable framework and Anthropic has a team of people doing it. You can read the cuDNN docs to get an idea of some of the other stuff you'd need, there's a lot there but generally that's the point. So in one sense, yes, if you have a strong performance engineering team and a specific workload already you can make use of a variety of hardware. In another sense, no, a small team isn't likely to be able to write a direct alternative for the CUDA ecosystem that reaches devex parity. Lots of people have a pretty clear idea of what it takes to do this but none of the major vendors seem to be doing the work. Knowing that reaching broad devex parity is very expensive I think the real win is figuring out what specific problem you have and building community and robust software support around that.