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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.
by pclmulqdq 1y 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.
- caycep 1y agothis is ROCm?
- fooblaster 1y agoYes, 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.
- fc417fc802 1y agoI'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.
- zombiwoof 1y agoJust look at the disaster of rocm or you need to spend 300k on software engineers to get anything so work
- tormeh 1y agoI 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.
- viewtransform 1y agoAMD is offering AMD Developer Cloud (https://www.amd.com/en/blogs/2025/introducing-the-amd-developer-cloud.html 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."
- archerx 1y agoIf they care about their future they should. I am a die hard AMD supporter and even I am getting over their mediocrity and what seems to be constant self sabotage in the GPU department.
- zombiwoof 1y agoIt’s the AMD management . They just are recycling 20 year VP lifers at AMD to take over key projects
- archerx 1y agoThey could have slapped 48gb of vram on their new Radeon cards and they would have instantly sold out but that would cut into cousins profit margin at nvidia so that’s obviously a no go.
- booder1 1y agoI 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...
- echelon 1y ago> AMD doesn't care about you being able to do computing on their consumer GPUs Makes it a little hard to develop for without consumer GPU support...
- stingraycharles 1y agoYes 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.
- cma 1y agoNvidia started removing nvlink with the 4000 series, they aren't heavily focused on it either anymore and want to sell the workstation cards for uses like training models at home.
- 7speter 1y agoAs a beginner, you can get a lot done on a 4060ti/5060ti 16gb, much less a 4090/5090. Heck I’ve been able to work through the early chapters of the FastAI book using a lowly Quadro p1000
- wmf 1y agoWhat if they understand that and they don't care? Getting one hyperscaler as a customer is worth more than the entire long tail.
- danielheath 1y agoWhy would a hyperscaler pick the technology that’s harder to hire for (because there’s no hobbyist-to-expert pipeline)?
- selectodude 1y agoThen they’re fools. Every AI maestro knows CUDA because they learned it at home.
- jiggawatts 1y agoIt’s the same reason there’s orders of magnitude more code written for Linux than for mainframes.
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- shmerl 1y agoAren't they addressing it with the unified UDNA architecture? That's going to be a thing in the future GPUs, making consumer and datacenter ones share the same arch. Different architectures was probably a big reason for the above issue.
- fooker 1y agoIt’s the same software stack.
- pjmlp 1y agoExcept they forget people get to adopt technologies by learning them on their consumer hardware.