It depends on your use case. There's a lot of hype around machines like the DGX spark (I'm assuming this is the type of device you're referring to) because they look awesome, and are priced reasonably well. However all of these have notoriously low memory bandwidth despite the high ram.

These devices, especially the DGX line, are fantastic if you are interested in low-level CUDA programming. The DGX spark can be used to prototype CUDA code/libraries for GPUs that most of us couldn't think about affording. If you want to learn how to program for datacenter level GPUs then these are the best way to get that at home. Sure your code will run very slow compared to the real thing, but you can take that code and, theoretically, run it on the real thing. For anything else though, I feel there are better options.

If you're interested in pure inference I'm pretty partial to Apple devices. The M4 Max gets you 546 GB/s, the M5 MAX 614 GB/s, and the M3 ultra (you'd have to buy used at this point) 819 GB/s. Plus you have a very useful computer even if you realize you don't want a full time home inference server. Additionally these devices require very low power (if you're running high end consumer GPUs you do have to think about what your energy costs are per hour and how warm you like your room).

If you're interested inference and training, or already have a pretty beefy desktop PC, or simply demand the most token/s you can get, then GPUs are the way to go. The downside is they're still pretty memory restricted (but honestly the options for what you can run on any RTX N090 are pretty good). You'll get blazing inference and prefill speeds on these devices. The only down side is, if you are using them heavily, you will see it on your energy bill and feel it in your room.

The "should I wait" question is also potentially applicable. The world of consumer hardware is looking increasingly bleak (and expensive) but if Apple does release a new "Ultra" model we could be looking at inference speeds very close to GPUs (there's still limitations to these devices that makes training preferable on GPU)