Original on Medium

I recently upgraded to a Mac Studio (M4 Max) — 16-core CPU, 40-core GPU, 126GB unified memory, and 2TB of storage. What a performance monster! 🚀 Benefit by the huge unified memory, I wanted to turn this machine into something more powerful: a fully offline AI assistant that can run local LLMs, experiment with prompts, and reference my own documents.

In this article, I’ll walk you through how I set up:

• Ollama to serve LLMs locally
• Cherry Studio for interactive prompt testing and knowledge base setup
• RAGFlow to build a robust, retrievable AI system powered by my own content

🧠 Why Local LLMs?

Running an LLM stack locally gives you:

• 🔐 Privacy: No internet dependency, no cloud APIs
• 🚀 Speed: Instant responses using Apple Silicon’s hardware acceleration
• ⚙️ Customizability: Your models, your data, your rules
• 🌐 Network access: Serve LLMs across your LAN or to browser apps

Let’s break it all down.

🛠 Step 1: Running Ollama on Mac Studio

Ollama is the fastest way to get started with local LLMs.

🔧 Install and Launch

brew install ollama
ollama run llama3

That’s all you need to run a base model locally. But we’re just getting started.

🌐 Enable Network Access

If you want other devices or services to connect to Ollama, you need to expose the API.

✅ Using the GUI

Ollama now lets you toggle LAN access in the settings panel:

⚙️ Go to Settings → Enable “Expose Ollama to the network”

This lets you connect to the Ollama API from other devices, RAG pipelines, or browser-based apps.

🔓 Enable CORS (Cross-Origin Requests)

If you’re working with browser-based tools like Cherry Studio or RAGFlow:

launchctl setenv OLLAMA_ORIGINS "*"

This enables cross-origin access so frontends can talk to your locally hosted Ollama backend.

🤖 Try These Open Source LLMs

Here are a few high-performing models I’ve tested on the M4 Max:

Launch any with:

ollama run qwen3-coder

Pair them with Cherry Studio to compare outputs side-by-side.

🎨 Step 2: Cherry Studio for Prompting + Knowledge Base

Cherry Studio is more than a playground — it includes apps for building full workflows, including code generation, translations, and a visual knowledge base manager.

📚 Creating a Knowledge Base in Cherry Studio

As shown in your screenshots:

1. Go to the Knowledge Base app
2. Create a base (knowledge base1, for example)
3. Add content:

• 📄 Files (TXT, MD, PDF, DOCX, etc.)
• 🔗 URLs and websites
• 📝 Notes and directories

4. Choose an embedding model (e.g., mxbai-embed-large)

This is visually intuitive, no setup needed, and great for quick experiments or smaller knowledge bases.

🔁 Cherry Studio vs. RAGFlow: Knowledge Base Comparison

Here’s how they stack up:

If you want something quick and visual, Cherry Studio wins.
If you need advanced RAG pipelines and expansion, go with RAGFlow.

🧠 What’s a Knowledge Base?

A knowledge base is a curated collection of your documents — technical notes, blog drafts, PDFs, emails, even Notion exports.

RAG tools split these into chunks and turn them into embeddings — numeric representations of meaning.

📌 What’s an Embedding Model?

An embedding model converts chunks of your content into vector representations. These vectors are then used to search for semantically similar results when you ask a question.

Popular choices:

• mxbai-embed-large
• e5-large
• bge-small-en
• nomic-embed-text

You can select these easily in Cherry Studio or customize them in RAGFlow.

🐳 Step 3: Deploy RAGFlow via Docker

RAGFlow gives you a local, production-ready retrieval system. It’s built with LangChain and supports custom pipelines.

🏗️ Quick Setup

git clone https://github.com/ragflow/ragflow.git
cd ragflow

Edit your .env file:

OLLAMA_BASE_URL=http://host.docker.internal:11434

Launch it:

docker compose up --build

Now visit http://127.0.0.1 (the local address in terms of what you set up) and:

• Setup Ollama’s models in Model Providers
• Upload documents (PDFs, TXT, HTML, DOCX, etc.)
• Use Ollama for generation
• Add search capabilities across your files

🧩 Full Local Stack Summary

Here’s what my system looks like now — all running on a single Mac Studio:

Mac Studio (M4 Max)
│
├── Ollama (local LLM server) – LAN + CORS enabled
│     └── Models: qwen3, qwen3-coder, gpt-oss
│
├── Cherry Studio (prompting + knowledge base)
│     └── GUI KB builder, embedding model picker
│
└── RAGFlow (Docker)
      └── Structured RAG pipeline, vector search, docs indexing

✅ Final Thoughts

With a Mac Studio like this, there’s no excuse to stay cloud-dependent. I now have:

• 🔐 Private local LLMs
• 🧠 A smart knowledge base from my own files
• 🧪 Tools for prompt testing and evaluation
• 🌐 Network access to serve apps and teammates

And all of it runs offline, leveraging my machine’s power instead of external APIs.