I would disagree that 30B is not good enough. It heavily depends on which model, and what you're trying to use it for.

30B is plenty if you have a local DB of all of your files and wiki/stackechange/other important databases places in a embedding vectordb.

This is typically what is done when people make these models for their home, and it works quite well while saving a ton of money.

While llama-7B systems on their own may not be able to construct a novel ML algorithm to discover a new analytical expression via symbolic regression for many-body physics, you can still get a great linguistic interface with them to a world of data.

You're not thinking like a real software engineer here - there are a lot of great ways to use this semantic compression tool.

If I just wanted a fuzzy search engine for local data, I'd use a vector DB - there's no need for an LLM on top of that.