If AMD is competitive performance per watt and roughly reliable in terms of software support which is what most folks outside of US prioritize above all else, since outside of China and US electricity tends to at a relative premium.
Maybe if they make smaller data centers viable at the right price, AMD could be part of the stack outside of US where ever Nvidia is more limited in supply. Though I have genuinely no idea what sourcing an AMD GPU looks like.
I have never seen a company use AMD outside of wafer and a couple others mostly in US.
Genuinely intriguing or maybe not really (could be this stuff is common knowledge) and I am just stuck in my Nvidia bubble here.
If you plan to run it straight for 8 years 100% max usage thats around 1 GWhr.
A gigawatt hour is a lot of energy but its not that much compared to the price of the actual machine. In Germany for example with its expensive energy thats about €100k worth, which spread over 8 years is pretty minor compared to the up front half mill.
The real issue with high power consumption is not really the cost of energy but the limited powersupply you can get for a datacenter. A more efficient setup is highly desirable because it means you can fit more in the limited power hookup.
There's a few using them, and even more starting to experiment with them. AMD has long been a source of disappointment around this side of things, so I'm hesitant to feel optimistic we'll finally get some competition. The market really needs viable competition to Nvidia, especially performance/watt.
Meta is using AMD: https://www.amd.com/en/newsroom/press-releases/2026-2-24-amd...
And OpenAI: https://www.amd.com/en/newsroom/press-releases/2025-10-6-amd...
Just because you haven't seen it doesn't mean it doesn't exist.
We've serviced over 700 customers on our MI300x.
[1] https://www.ycombinator.com/launches/Q9i-wafer-pass-flat-rat...
If I'm missing something, please let me know!
(Previously this comment said Rubin did native NVFP4, but Blackwell does too! Rubin just also trains with native NVFP4, which Blackwell does not.)
Nvidia says Rubin should have fewer stability problems training with FP4 because of hardware changes - "adaptive compression". There will still be outlier instability inherently, but something they're designing in reduces the cost of managing it.
But yeah, grain of salt - we haven't seen this in practice.
Of course there are techniques such as quantization aware training but I don't understand why a datatype would work for inference but not for that.
You can also abandon backprop entirely but that comes with a whole host of tradeoffs and again why would it work for inference but not for whatever alternative training regime you selected?
I guess you really do have to try it at least for some time to actually know
It's genuinely neat that AI can find the right optimization pathways in an AMD inference server to unlock this but at the same token (pun-intended) this is a classic case of benchmark hacking that doesn't stand up to real-world application.
So much compute is under utilized waiting for a savant or company to prioritize an architecture, and now all the other engineers can tackle this at any time if they get inspired on the right prompts
So I could see something like this where the neural chipset has an LLM that cant be so easily updated baked into it, until you get a new device