It’s a one-and-done process, then the normal Q&A usage has vastly lower requirements.

Local models generally means you download the result of all that , after it’s done, (the PT in ChatGPT stands for pre-trained) and then generate answers locally, but the training wasn’t done locally. The generating answers step is called “inference”.

Hardware cost to do inference with a top-level model is within reach of most small businesses. Hardware cost to do training of a top-level model is orders of magnitude higher.

So, there are open-source models that are trained by universities, and then used by affluent but not billionaire people at home.

There are open-source models that can do inference (answer questions) on a typical 8GB gaming graphics card, but they aren’t in the same order of magnitude of quality as the famous top-level models.

training a top-level model takes harvesting petabytes / exabytes of revelant data and then months of continuous 100% power on a large server farm costing many millions of dollars.

come on, I’ve trained toy size models too, you can’t seriously say they’re even vaguely approaching the capability of models that takes a whole massive server farm weeks or months to train.