Comment by pclmulqdq
Comment by pclmulqdq 2 days ago
AMD doesn't care about you being able to do computing on their consumer GPUs. The datacenter GPUs have a pretty good software stack and great support.
Comment by pclmulqdq 2 days ago
AMD doesn't care about you being able to do computing on their consumer GPUs. The datacenter GPUs have a pretty good software stack and great support.
I wouldn't say so. Nvidia bet on machine learning a decade or so before AMD got the memo. That was a good bet on Nvidia's part. In 2015 you just had to have an Nvidia card if you wanted to do ML research. Sure, Nvidia did hand them out in some cases, but even if you bought an AMD card it just wouldn't work. It was Nvidia or go home. Even if AMD now did everything right (and they don't), there's a decade+ of momentum in Nvidia's favor.
Yes but then they fail to understand a lot of “long tail” home projects, opensource stuff etc is done on consumer GPUs at home, which is tremendously important for ecosystem support.
What if they understand that and they don't care? Getting one hyperscaler as a customer is worth more than the entire long tail.
On the corp side you have FB w/ PyTorch, xformers (still pretty iffy on AMD support tbt) and MS w/ DeepSpeed. But let's see about some others:
Flash Attention: academia, 2y behind for AMD support
bitsandbytes: academia, 2y behind for AMD support
Marlin: academia, no AMD support
FlashInfer: acadedmia/startup, no AMD
ThunderKittens: academia, no AMD support
DeepGEMM, DeepEP, FlashMLA: ofc, nothing from China supports AMD
Without the long tail AMD will continue to always be in a position where they have to scramble to try to add second tier support years later themselves, while Nvidia continues to get all the latest and greatest for free.
This is just off the top of my head on the LLM side where I'm focused on, btw. Whenever I look at image/video it's even more grim.
Modular says Max/Mojo will change this and make refactoring between different vendors (and different lines of the same vendor) less of a showstopper but tbd for now
The problem is that this is short-term thinking. You need students and professionals playing around with your tools at home and/or on their work computers to drive hyperscale demand in the long term.
This is why it’s so important AMD gets their act together quickly, as the benefits of these kind of things are measured in years, not months.
Then they’re fools. Every AI maestro knows CUDA because they learned it at home.
It’s the same reason there’s orders of magnitude more code written for Linux than for mainframes.
Why would a hyperscaler pick the technology that’s harder to hire for (because there’s no hobbyist-to-expert pipeline)?
Then they will stay irrelevant in the GPU space like they have been so far.
Why should we care about them if they don't care?
I mean of they want to stay at a fraction of the market value and profit of their direct competitor, good for them.
I want a competitive market so I can have cheaper gpus.
It's Nvidia, AMD, and maybe Intel.
AMD is offering AMD Developer Cloud (https://www.amd.com/en/blogs/2025/introducing-the-amd-develo...)
"25 complimentary GPU hours (approximately $50 US of credit for a single MI300X GPU instance), available for 10 days. If you need additional hours, we've made it easy to request additional credits."
I have had trained on both large AMD and Nvidia clusters and your right AMD support is good. I never had to talk to Nvidia support. That was better.
They should care about the availability of their hardware so large customers don't have to find and fix their bugs. Let consumers do that...
Yes, the mi300x/mi250 are best supported as they directly compete with data center gpus from Nvidia which actually make money. Desktop is a rounding error by comparison.
I'm inclined to believe it but that difference is exactly how nvidia got so far ahead of them in this space. They've consistently gone out of their way to put their GPGPU hardware and software in the hands of the average student and professional and the results speak for themselves.