I like this approach: use a bunch of LLMs on various machines. SomeOddCodeGuy in Reddit shared his very cool multi LLM setup [1]. I quote: > I have 2 "assista

I like this approach: use a bunch of LLMs on various machines. SomeOddCodeGuy in Reddit shared his very cool multi LLM setup [1]. I quote: > I have 2 "assistants" set up, with the intention to later add a third. Each assistant is essentially built to be an advanced "rubber duck" (as in the rubber duck programming method where you talk through a problem to an inanimate object and it helps you solve this problem). Each assistant is built entirely to talk through problems with me, of any kind, and help me solve them by challenging me, answering my questions, or using a specific set of instructions on how to think through issues in unique ways. Each assistant is built to be different, and thus solve things differently. Each assistant is made up of multiple LLMs. Some examples would be: A responder model, which does the talking A RAG model, which I use for pulling data from the offline wikipedia api for factual questions A reasoning model, for thinking through a response before the responder answers A coding model, for handle code issues and math issues. See his post at https://www.reddit.com/r/LocalLLaMA/comments/1i1kz1c/sharing_my_unorthodox_home_setup_and_how_i_use/ https://www.reddit.com/r/LocalLLaMA/comments/1i1kz1c/sharing...