Apple Silicon and MLX - Running ML Models Locally Without Cloud APIs

Most developers reach for OpenAI or Anthropic APIs by default when they need ML in their apps. It is the path of least resistance - send text, get response. But Apple Silicon is making local inference a real alternative, and MLX is the framework that makes it practical.

What MLX Actually Is

MLX is Apple's machine learning framework designed specifically for Apple Silicon. It uses unified memory - the same memory pool shared between CPU and GPU - which eliminates the data transfer bottleneck that makes local inference slow on traditional hardware. When you run a model through MLX on an M-series chip, the GPU can access model weights directly without copying them.

This matters more than it sounds. On a standard setup with a discrete GPU, loading a 7B parameter model means copying gigabytes of weights from system RAM to GPU memory. On Apple Silicon with MLX, those weights sit in unified memory and both CPU and GPU read from the same place.

The Privacy Angle

For certain use cases, local inference is not just convenient - it is necessary. If you are processing sensitive documents, analyzing private communications, or working with proprietary code, sending that data to a cloud API creates a compliance problem. Local models process everything on-device. Nothing leaves your machine.

This is especially relevant for AI agents that operate on your desktop. An agent that reads your screen, processes your files, and interacts with your apps should ideally keep all that data local. Running vision and language models through MLX means your desktop activity stays on your hardware.

What You Can Run Today

On an M2 Pro or better, you can comfortably run 7-8B parameter models with good token speeds. M3 Max and M4 Max machines handle 30-70B models. Whisper for speech recognition runs in real-time. Small vision models handle screenshot analysis locally.

The gap between local and cloud is shrinking with each generation of Apple Silicon. For many everyday tasks - summarization, classification, extraction, basic reasoning - local models on MLX are already good enough. The cost is zero after the initial hardware purchase, and the latency for short prompts is often better than a round trip to a cloud API.

Fazm is an open source macOS AI agent. Open source on GitHub.