Comment by gorbot
3 months ago
I'm an idiot and I know nothing
But I wonder if there could be room for an ARM-like spec that Google could try and own and license but for AI chips. Arm is to risc-cpu as google-thing is to asic-aichip
Prolly a dumb idea, better to sell the chips or access to them?
I'm not sure the chip spec (or instruction set) is the level of abstraction here?
Something like DirectX (or OpenGL) might be the better level to target? In practice, CUDA is that level of abstraction, but it only really works for Nvidia cards.
It's not that it only works on Nvidia cards, it's only allowed to work on Nvidia cards. A non-clean room implementation of CUDA for other hardware has been done but is a violation of EULA (of the thing that was reverse engineered), copyright on the driver binary interface, and often patents. Nvidia aggressively sends cease-and-desist letters and threatens lawsuits (successfully killed ZLUDA, threatened others). It's an artificial (in a technical sense moat).
Spectral just did a thread on that.
https://x.com/SpectralCom/status/1993289178130661838
> successfully killed ZLUDA
Did they? Sounds like AMD did that[^1] and that the project is continuing based on the pre-AMD codebase[^2].
[^1]: https://www.phoronix.com/news/AMD-ZLUDA-CUDA-Taken-Down
[^2]: https://www.phoronix.com/news/ZLUDA-Third-Life
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I don't think you can make the EULA bite here?
To circumvent: you have someone (who might be bound by the EULA, and is otherwise not affiliated with you) dump the data on the internet, and someone else (from your company) can find it there, without being bound by the EULA. Nvidia could only sue the first guy for violating the EULA.
However you are right, that copyright and patents still bite.
> CUDA is that level of abstraction, but it only really works for Nvidia cards.
There are people actively working on that.
https://scale-lang.com/
Not really, because as usual people misunderstand what CUDA is.
CUDA is hardware designed according to the C++ memory model, with first tier support for C, C++, Fortran and Python GPGPU DSLs, with several languages also having a compiler backend for PTX.
Followed by IDE integration, a graphical debugger and profiler for GPU workloads, and an ecosystem of libraries and frameworks.
Saying just use DirectX, Vulkan, OpenGL instead, misses the tree from the forest that is CUDA, and why researchers rather use CUDA, than deal with yet another shading language or C99 dialect, without anything else.
they tried selling years ago, not much happened, coral
now they dont want to sell them - why power local inference when they can saubscribe forever and you get their juicy datas too