Running a large language model (LLM) on your own machine used to be a distant dream — now it’s possible and surprisingly simple thanks to Apple’s MLX framework.
MLX is Apple’s machine learning library optimized for Apple Silicon, allowing you to run and fine-tune powerful models locally — without needing a GPU cluster or internet connection.
This post will walk you through setting up MLX and running your first model (like Mistral 7B) locally on macOS.
What You’ll Need
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A Mac with Apple Silicon (M1, M2, or M3)
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Python 3.10 or later
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Basic Terminal familiarity
Step 1: Set Up a Project Folder
Start by opening your Terminal and creating a folder for your MLX project.
cd ~/dev/mlx-ui If the folder doesn’t exist yet:
mkdir -p ~/dev/mlx-ui
cd ~/dev/mlx-uiThis will be your working directory for everything MLX-related.
Step 2: Create a Python Virtual Environment
A virtual environment keeps your dependencies clean and separate from your system Python.
python3 -m venv .venv
Activate it:
source .venv/bin/activate
Once activated, your Terminal prompt should look something like this:
(.venv) aman@Mac mlx-ui % This means you’re now working inside your isolated Python environment.
Step 3: Install MLX and Dependencies
Install the MLX library for running and managing local language models:
pip install mlx-lm That’s it — you now have MLX installed on your system.
Step 4: Download a Model from the MLX Community
MLX supports a variety of models hosted on Hugging Face under the mlx-community organization.
For example, to try Mistral 7B Instruct (4-bit) — a strong open-weight model — run:
mlx_lm.generate --model mlx-community/Mistral-7B-Instruct-v0.3-4bit --prompt "hello" MLX will automatically:
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Download the model files to your local Hugging Face cache
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Run an inference on your machine
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Return a response to your prompt
If you see a response like “Hi there! How can I help you today?” — your model is live and local 🎉
Step 5: Chat Directly in the Terminal
You can launch a conversational session directly from your terminal:
mlx_lm.chat
This opens an interactive shell where you can type back and forth with your model.
Try a few questions like:
> What’s the capital of India? > Write a short poem about the ocean. 💡 Tip: The terminal interface is great for quick tests, but not ideal for longer conversations or file-based Q&A — that’s where Streamlit comes in (we’ll cover that in the next post).
Step 6: Deactivate When You’re Done
When finished, simply deactivate the virtual environment:
deactivate You can always reactivate it later with:
source .venv/bin/activate
Summary
| Step | Task | Command |
|---|---|---|
| 1 | Create project folder | mkdir -p ~/dev/mlx-ui |
| 2 | Create virtual environment | python3 -m venv .venv |
| 3 | Activate environment | source .venv/bin/activate |
| 4 | Install MLX | pip install mlx-lm |
| 5 | Download and test model | mlx_lm.generate --model mlx-community/Mistral-7B-Instruct-v0.3-4bit --prompt "hello" |
| 6 | Chat in terminal | mlx_lm.chat |
| 7 | Deactivate | deactivate |
Coming Up Next
In the next post, we’ll go beyond the terminal and build a Streamlit WebUI — a sleek, ChatGPT-style interface that lets you chat with your local MLX LLM right from your browser.
Stay tuned for Part 2: Building a Streamlit Web Interface for Your Local MLX Model.