I'm really excited by the concept of local LLMs, we give to much of our data to the cloud. We should embrace local first principles with all these new AI tools.

I'm really excited by the concept of local LLMs, we give to much of our data to the cloud. We should embrace local first principles with all these new AI tools. Sure local inference if harder than in the cloud, but hardware is getting better all the time, and we are still early on the optimisation curve when it comes to LLMs. I'm looking forward to hopefully seeing smaller less resource intensive models that are easer to run locally, even on mobile. Does anyone who of any research info "trimming" or "stripping" less used parts of a LLM so that you can take a trained weights and make them smaller (obviously with loss of some sort)?