I admittedly have not done this myself, but the hardware choices are less complex than you might think. The only truly important part is the GPU, and the number of GPUs on the consumer market that can handle a local LLM is presently quite small. It's pretty much dual-wielding 3090's or 4090's or bust. If you're running but not training a (relatively) small one, you can do well with just one. But if you want to run inference _and_ training on one large enough to be a consistent daily assistant (read: 70b+), cloud hardware for the training is just far more practical and economical. You're generally not gonna wanna drop the $$ for an A100 or two just to do a small training run every couple of weeks on a dataset made up of the amount of RLHF'd conversations one person might have with an LLM in that time.
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I admittedly have not done this myself, but the hardware choices are less complex than you might think. The only truly important part is the GPU, and the number of GPUs on the consumer market that can handle a local LLM is presently quite small. It's pretty much dual-wielding 3090's or 4090's or bust. If you're running but not training a (relatively) small one, you can do well with just one. But if you want to run inference _and_ training on one large enough to be a consistent daily assistant (read: 70b+), cloud hardware for the training is just far more practical and economical. You're generally not gonna wanna drop the $$ for an A100 or two just to do a small training run every couple of weeks on a dataset made up of the amount of RLHF'd conversations one person might have with an LLM in that time.