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Even if we assume the high-end of your range, and say that for the foreseeable future training a near-state-of-the-art deep learning model from scratch will cost around half a million dollars, that's still cheap enough to be considered fairly democratic. A lot of people and organisations have that sort of money, many of which exist outside of the Cathedral. And as you say, you can do a lot by tuning an existing model, which is feasible for hobbyists.
I think for the sort of controls you're worried about, it's not just a matter of who can afford to buy it, but also who can afford to sell it. Not just that there's a few limited companies doing this stuff, but in the sense that if you come up to the sorta companies that have and resell these resources, they demonstrably will start poking around at what you're doing, how you're paying them, and what you're doing.
((Not... uh, very effectively in an anti-fraud sense, given Amazon. But very effectively in a not-doing-things-they-don't-like, given Amazon.))
Eventually that stops being a problem as used past-generation tensor core GPUs trickle out into the used market (uh, assuming ITAR doesn't get involved), or as resellers are able to more heavily obscure stuff at larger scales, or as the relative scales decrease due to performance and efficiency gains.
But it is worth keeping in mind as a limit to the democratization of the space.
It's also possible that manufacturers could nerf GPUs for the purpose of ML except for customers with whom they have a special relationship. See e.g. the rate limiting NVIDIA did for crypto mining while still selling a higher priced card without the nerfing.
Yeah, that's another risk. It didn't work for the anti-mining stuff, but given politics and economics around ML that may have stronger incentives.
Crippling GPUs works very well in one context I've seen: FP64. Games don't use it so manufacturers don't get dinged for having lousy performance with it, and engineers/mathematicians/scientists won't flinch at paying through the nose for "professional" GPGPU cards, so with a few exceptions (Titan Black, Radeon VII, and even those were high-end) you get a pittance of FP64 support on consumer cards.
But there's a very well-delineated difference between 32-bit and 64-bit floats. What's the clear technical difference between "bad ML models, which we want to keep away from hobbyists" and "good ML models, which everybody's going to be throwing into their game engines as fast as studios can train them"? The difficulty of slowing down "bad" algorithms but not "good" ones was effectively the problem with crypto rate limiting, (which only brought the cards down to 50% speed and only worked on some crypto types and was quickly foiled via driver or BIOS changes), not any special societal support for cryptocurrency. Compare DRM, which despite massive political and economic support gets broken over and over again because from a technical standpoint the problem statement is almost a self-contradiction.
Yeah, that's plausible. So far, it's been possible to prune down to 16 or even 8-bytes-per, but it's definitely something that takes some tweaking to do right, and may not be possible for all or even most useful models.
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