Your comment, pre-edit, had something of a severe tone given that consideration.

Having said that, I've trained/finetuned image models just fine on an RTX 2070 Super with 8 GB of VRAM. This was back when doing so was more fruitful than simply training a more robust model in the first place. Given that is the current status quo - I'm curious what sort of training you're doing whole-network that actually produces results that are noticeably better than doing something few-shot during inference or doing LoRA finetuning? The latter brings you back into the realm of tuning on low-VRAM configs.

In general, a single GPU's memory constraints are one of many when training a model _from scratch_. In that case, you're bottlenecked by data and data parallelism. You don't need one or a few GPU's, you need more than would fit in a consumer setup in the first place.