> It seems pretty simple to me if we're talking about compute. The MI-cards are AMD's line of compute GPUs. Buy an MI-card if you want to use ROCm with full support. That's MI25, MI50, or MI100.
For tasks which require that much GPU, I'm using cloud machines. My dev desktop would like a working GPU 24/7, but it doesn't need to be nearly that big.
If I had my druthers, I would have bought an NVidia 3050, since it has adequate compute, and will run once available <$300. Of course, anything from the NVidia consumer line is impossible to buy right now, except at scalper prices.
I just did a web search. The only card from that series I can find for sale, new, was the MI100, which runs $13k. The MI50 doesn't exist, and the MI25 can only be bought used on eBay. Corporate won't do eBay. Even the MI100 would require an exception, since it's an unauthorized vendor (Amazon doesn't have it).
Combine that with poor software support, and an unknown EOL, and it's a pretty bad deal.
> I think you're right, but the #1 use of these devices is running video games (aka: DirectX and Vulkan). Compute capabilities are quite secondary at the moment.
Companies should maximize shareholder value. Right now:
- NVidia is building an insurmountable moat. I already bought an NVidia card, and our software already has CUDA dependencies. I started with ROCm. I dropped it. I'm building developer tools, and if they pick up, they'll carry a lot of people.
- It will be years before I'm interested in trying ROCm again. I was oversold, and AMD underdelivered.
- Broad adoption is limited by lack of standards and mature software.
It's fine to day compute capabilities are secondary right now, but I think that will limit AMD in the long term. And I think lack of standards is to NVidia's advantage right now, but it will hinder long-term adoption.
If I were NVidia, I'd make:
- A reference CUDA open-source implementation which makes CUDA coda 100% compatible with Xe and Radeon
- License it under GPL with a CLA, so any Intel and AMD enhancements are open and flow back
- Have nominal optimizations in the open-source reference implementation, while keeping the high-performance proprietary optimizations NVidia proprietary (and only for NVidia GPUs)
This would encourage broad adoption of GPGPU, since any code I wrote would work on any customer machine, Intel, AMD, or NVidia. On the other hand, it would create an unlevel playing field for NVidia, since as the copyright holder, only NVidia could have proprietary optimizations. HPC would go to NVidia, as would markets like video editing or CAD.