For me, a HUGE benefit to running local models is that my data stays mine, on my computers only.

Even if Google, OpenAI, Anthropic whatever promise not to use it, how sure can I be of that? Data is gold anyway. And we're in the middle of a massive gold rush. And they've already been caught scraping sites they had no business to, and pirating books. Clearly their promises and the law mean nothing to them. It's just something you pay off in a settlement if you get caught, a cost of doing business.

With local models besides the speed you also lose a lot of inference quality but it helps to mitigate that. For example making sure your RAG inputs are properly prepared and categorised so the model can find them easily without having to wade through a bunch of misdirected crap.

For example what I do with my bookmarks and chats (the latter are recorded per day), is before I enter them into a RAG corpus I run a small LLM over it to summarise what's being discussed or what the webpage is about. That really helped retrieval quality, and doing this is a batch task that can run asynchronously so speed is not very relevant. This way I get a lot closer to SOTA-model retrieval quality (like with Office Copilot 365 looking for conversations in Teams).

PS: I wouldn't be surprised if Microsoft runs something similar on their end :)

But yes the Mac Studio is outrageously priced especially for running such a small model as qwen 27b. For that price you can use something much much cheaper. It only shines for models that are much bigger, because there simply is not much hardware that can address fast 512GB banks.