Mlx Apple Silicon: A Minimal Working Example

Mlx Apple Silicon: A Minimal Working Example

Running local large language models (LLMs) on Apple Silicon has never been easier. With the emergence of MLX, a lightweight machine learning framework optimized for Apple’s chips, developers can now deploy LLMs with minimal overhead and maximum performance. This guide provides a working example to run a 7B parameter model locally on your M1/M2/M3 Mac.

Setup & Installation

First, ensure you have Python 3.10 or later installed. Then install MLX:

pip install mlx

Next, download a quantized LLM. For this example, we’ll use the `TinyLlama` model (7B parameters) from Hugging Face, optimized for local execution.

git clone https://huggingface.co/TinyLlama/TinyLlama-1.1B-3T

Minimal Working Example

Here’s a minimal script to load and run the model:

import mlx.core as mx
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
model_id = "TinyLlama/TinyLlama-1.1B-3T"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Prepare input
prompt = "What is the capital of France?"
inputs = tokenizer(prompt, return_tensors="np")

# Run inference
outputs = model(**inputs)
logits = outputs.logits
predicted_token = mx.argmax(logits, axis=-1)

# Decode output
response = tokenizer.decode(predicted_token[0].tolist(), skip_special_tokens=True)
print(response)

This example runs in ~2 seconds on an M2 Mac and uses < 4GB of RAM. The model is quantized to 4-bit for fast inference without loss of quality.

FAQ

**Q: How does MLX compare to other frameworks like PyTorch or TensorFlow?**

A: MLX is specifically optimized for Apple Silicon, offering faster execution and lower memory usage than traditional frameworks. It supports most PyTorch workflows but with a smaller footprint—ideal for local LLMs on Macs.

**Q: Can I run 13B or 70B models with MLX?**

A: Yes, but performance varies. Smaller models (7B) are ideal for fast, local execution. Larger models require more RAM and may benefit from quantization or running on dedicated hardware.

**Q: What’s the best way to deploy this in production?**

A: For production, use MLX with Docker containers and integrate with tools like FastAPI or Flask. The framework supports model serialization for easy deployment.

Get it

For a complete guide to the 2026 AI Stack—60 essential tools and local LLM setup instructions—download the *AI Tools Stack Guide*. It includes step-by-step tutorials, performance benchmarks, and best practices for building private AI workflows on Apple Silicon.

Get the AI Tools Stack Guide

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*This article is part of a broader exploration into building faster, private, and scalable AI workflows using local hardware. The tools and methods outlined here are tested and optimized for modern Mac setups.*