If I could justify wear and tear and electricity, I was willing to do something like this for batch processing. The batches would be a bunch of prompts whose outputs I'd look at the next day. Maybe common operations, like QA or refactoring, on whatever software I wrote.
If so, I could use a larger model than I have real-time hardware for. The largest, well-trained models can often get the output mostly right in one try. I also would be using AI's as a supplement to, not replacement for, my own brain. So, issues with the outputs wouldn't be a problem because I'm just keeping what's helpful.
If I still need to re-generate it all, it might still save money over time by avoiding cloud costs. Also, hardware that's already paid for is a sunk cost that doesn't inflate over time. Glitches in loading or destroying VM's might blow up into a big bill.
at 5 minutes per token, you could look at the results next week
That's way slower than I thought it would be. I struggle to imagine a use case.
If you had a deadly condition, and no diagnosis worked, and a specific model had the answer... past that I wouldn't use it.