[colordrops]

I do not misunderstand.

Let's say you put 50-100 seasoned devs on the problem, and within 2-3 years, probably get ZLUDA to the point where most mainstream CUDA applications — ML training/inference, scientific computing, rendering — run correctly on AMD hardware at 70-80% of the performance you'd get from a native ROCm port. Even if its not optimal due to hardware differences, it would be genuinely transformative and commercially valuable.

This would give them runway for their parallel effort to build native greenfield libraries and toolkits and get adoption, and perhaps make some tweaks to future hardware iterations that make compatibility easier.

[zvr]

Before the "ZLUDA" project completion, they would be facing a lawsuit for IP infringement, since CUDA is owned by NVIDIA.

[colordrops]

They would win, compatibility layers are not illegal.

[bigyabai]

ROCm is supported by the minority of AMD GPUs, and is accelerated inconsistently across GPU models. 70-80% of ROCm's performance is an unclear target, to the point that a native ROCm port would be a more transparent choice for most projects. And even then, you'll still be outperformed by CUDA the moment tensor or convolution ops are called.

Those billions are much better-off being spent on new hardware designs, and ROCm integrations with preexisting projects that make sense. Translating CUDA to AMD hardware would only advertise why Nvidia is worth so much.

> it would be genuinely transformative and commercially valuable.

Bullshit. If I had a dime for every time someone told me "my favorite raster GPU will annihilate CUDA eventually!" then I could fund the next Nvidia competitor out of pocket. Apple didn't do it, Intel didn't do it, and AMD has tried three separate times and failed. This time isn't any different, there's no genuine transformation or commercial value to unlock with outdated raster-focused designs.