Both companies will leapfrog each other with new releases. Anyone who believes that there should only be a single vendor for all AI compute will quickly find themselves on the wrong side of history
This reminds me of those "192GB is fantastic" people that bought maxed-out M2 Ultras for AI inference. It can be awesome, but you need a substantial amount of interconnect bandwidth and powerful enough local compute before it's competitive. In products where AI is an afterthought, you're fighting against much different constraints than just having a lot of high-bandwidth memory.
I've always rooted for Team Red when they made an effort to do things open-source and transparently. They're a good role-model for the rest of the industry, in a certain sense. But I have to make peace with the fact that client-side AI running on my AMD machines isn't happening. Meanwhile, I've been using CUDA, CUDNN, CUBLAS, DLSS, on my Nvidia machine for years. On Linux!
8x AMD MI300X (192GB, 750W)
8x H100 SXM5 (80GB, 700W)
What the customer does not see is how AMD must spend 2 times more money to produce a chip that is competitive against architecture that is soon 2 years old.
Probably because they aren't widely available yet. It is also a dual card to get that much memory, which is still less than 192GB and far less than 288GB.
https://www.anandtech.com/show/18780/nvidia-announces-h100-n...
> What the customer does not see is how AMD must spend 8-10 times more money to produce a chip that is competitive against architecture that is soon 2 years old.
Source?
8x H100 NVL (94GB, 800W)