Mac mini M4 Local LLM Deep Dive: Build a Production RAG Pipeline in 2026
I ran two RAG stacks side by side on the same Mac mini M4 32GB for three weeks: LlamaIndex 0.14 with ChromaDB on one half, LangChain 0.3 + LangGraph with Qdrant on the other. Same documents (about 4,200 PDFs and Markdown notes), same embedding model, same chat model. I expected one to clearly dominate. It didn't, and the gap turned out to be in places I wasn't measuring. If you searched for this guide, you're probably past the "can Ollama run on my Mac" stage. You've already got a 7B or 8B model answering questions, and now you want it to answer questions about your own documents without leaking anything to a cloud API. That's the actual problem this article solves. What follows is the full Mac mini M4 RAG pipeline I'd build today in 2026, with the specific component versions, memory budgets, and chunking parameters that survived contact with real corpora. So if you want a setup that works on 32GB unified memory without thermal...