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New Macbook Pros and workstations are now coming with powerful GPU's for ML work.

StableDiffusion alone was trained on 256 x Nvidia A100 GPUs.




Correct, MBP's can run stable diffusion and other ML workloads on non-nvidia hardware. I clearly see this becoming a trend. GPT-J, Neo and NeoX run really well on Colab TPU's, again these are not made by Nvidia.

Training is dominated by Nvidia, I will not question that as most papers I have seen say something similar. I will say that I do not believe training will always be dominated by Nvidia's datacenter options. Two things that will hasten the withdraw from Nvidia; Cuda and hardware advances around the motherboard (ASICs, RAM proximity, PCIe lanes, data transfer planes, etc).

Think about this... what if a company released an ML training/Inference ASIC that used regular DDR4/NVMe, performed like 4 x A100's and cost $8000? Would you be interested? I would! I don't think this is too far off, there has to be someone working on this outside of Google, Apple and Meta.


We've had several generations of ASICs already, if TPUs etc aren't much superior to GPUs why would future ASICs be any better.




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