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Alternative(s) to run CUDA on non-Nvidia hardware

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Re: Alternative(s) to run CUDA on non-Nvidia hardware

#41
post #15

Earlier quoted context omitted.

Peanut-gallery is happily using CUDA, and needs actual sound reasons to move.

Then the peanut gallery has nothing to complain when Nvidia jacks up prices.

Do you see me complaining?

Here is a tip, you don't always need to suffer from FOMO and get the very latest model card.

In fact, contrary to the competition, one can play with CUDA even on laptops, go figure.

Re: Alternative(s) to run CUDA on non-Nvidia hardware

#44
i'm also interested in tenstorrent. they're building GPUs with cheap GDDR6 using a fast SRAM cache, and writing their own compiler stack (used instead of CUDA) that pipelines data to the SRAM ahead-of-time so you (in theory) never need to suffer the slow speed of GDDR6 for AI workloads. also they've got built-in SFP cages where the video ports would normally be.

Re: Alternative(s) to run CUDA on non-Nvidia hardware

#45

There's nothing wrong to run CUDA on non-Nvidia hardware. CUDA has an interface that is reasonably well-designed, well-documented/reverse-engineered, and battle-tested for decades. What we need is not to invent another interface just under the name of 'open standard', but to implement the same interface. ROCm is exactly doing this, and so are other hardware SDKs such as MooreThread and Alibaba T-Head.

[flagged]

Re: Alternative(s) to run CUDA on non-Nvidia hardware

#47

There's nothing wrong to run CUDA on non-Nvidia hardware. CUDA has an interface that is reasonably well-designed, well-documented/reverse-engineered, and battle-tested for decades. What we need is not to invent another interface just under the name of 'open standard', but to implement the same interface. ROCm is exactly doing this, and so are other hardware SDKs such as MooreThread and Alibaba T-Head.

Someone needs to stand up a benchmark suite for ROCM, this, and everyone else attempting it to really get the ball rolling here. SemiAnalysis could have a blast with this.

Re: Alternative(s) to run CUDA on non-Nvidia hardware

#48

i'm also interested in tenstorrent. they're building GPUs with cheap GDDR6 using a fast SRAM cache, and writing their own compiler stack (used instead of CUDA) that pipelines data to the SRAM ahead-of-time so you (in theory) never need to suffer the slow speed of GDDR6 for AI workloads. also they've got built-in SFP cages where the video ports would normally be.

Is tenstorrent building GPUs now, not just tensor processors?

Re: Alternative(s) to run CUDA on non-Nvidia hardware

#49

Earlier quoted context omitted.

Yep, there are inference stacks where it just does not work without cuda in any meaningful performance

Weird, since the most used open source inference engine is faster on Vulkan on platforms that offer multiple options, with the sole exception being Nvidia, due to poor Nvidia driver quality (which I am forced to assume is intentional, Nvidia wishes to maintain their moat after all).

There's nothing stopping any of us from writing a better Nvidia driver btw. LLMs are very helpful with reverse engineering.

Re: Alternative(s) to run CUDA on non-Nvidia hardware

#50
These efforts to support CUDA on non-Nvidia hardware seem to me misguided. If all you want is to be able to easily use non-NVidia hardware then high level tools like PyTorch already let you do that (and torch.compile uses Triton for target-specific optimization). OTOH if you want to be programming close to the metal to achieve top performance then you are probably not using CUDA in the first place, and using some CUDA translation layer on non-NVidia hardware would be an even worse idea.
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