I quoted the 4090 because this is the Mac Pro on the second gen of Apple Silicon in a desktop/workstation. In a few years Apple has nearly caught up to the flag

I quoted the 4090 because this is the Mac Pro on the second gen of Apple Silicon in a desktop/workstation. In a few years Apple has nearly caught up to the flagship desktop GPU from Nvidia - who has been at this former niche for 15 years. 192GB is already enough RAM to finetune very large LLMs and we will be seeing more memory from Apple down the road. 192GB is 4 RTX A6000s or 2 A100/H110s ($20k just in GPUs)... A completely maxed out Mac Pro (with wheels and fancy mouse) is under $13k. Oh yeah and you can actually buy them. The M3, M4, and beyond will have TFLOPS and RAM jumping by leaps and bounds. CoreML will start chipping at CUDA developer mindshare from academia on up - you can already run LLMs at more than reasonable speed on a $2000 Mac Book you already have laying around. Expect to see Mac Pros showing up in AI labs at universities and course work where every student just buys a Mac. Of course at this level it's mostly higher level frameworks anyway so transferrable knowledge to CUDA powered whatever. As I noted, the big stuff from Nvidia will own the very high end (especially for training) for the foreseeable future and there's plenty of money to be made there as you noted. But Apple is going to take big chunks out of Nvidia on the hardware side and a host of startups from the software/SaaS side. For hardware less demand on the low-mid side and less demand on the inference hosting side even for the big players. For software leveraging foundational Apple models, dev tools, etc will make it so that any app developer can drop data in Xcode to tune/build a model for their app. Apps will be able to further finetune with user data on device. See the links I provided - this is here (in beta) today.