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Running AI Local with Ollama on Apple Silicon

Comprehensive guide to running LLM locally on Mac Apple Silicon (M1/M2/M3/M4) with Ollama and MLX. From initial installation to 3x acceleration with the MLX framework, managing multiple models, integrating APIs into applications, and optimizing GPU/RAM performance. All hands-on, privacy-first, no internet required.

Introducing the series

Do you have an M-chip Mac but are still paying for ChatGPT or Anthropic every month?

This series will teach you to run AI completely locally on your machine: no internet, no API keys, no monthly fees, and data never leaves the machine.

Apple Silicon is one of the best local inference platforms today thanks to Unified Memory Architecture. A MacBook with 16GB or 32GB of RAM can run many 7B to 30B models more smoothly than many people think.

What will you learn?

  • Install and operate Ollama to run LLM locally
  • Integrates Apple's MLX to accelerate inference
  • Call models from Python, JavaScript, or any language via REST API
  • Build chatbot, vision app, embedding pipeline running locally
  • Optimize memory, concurrency and custom AI assistant

Prerequisites

  • Mac with Apple Silicon chip (M1 or later)
  • RAM 16GB or more, 32GB recommended if running large model
  • macOS Ventura 13.3+ or later
  • Know basic Terminal
  • Basic Python to follow the exercises

Why should you study this series?

In 2026, the ability to run AI locally is a very practical skill for developers:

  1. Privacy: code, data and chat do not leave the device
  2. Cost: almost $0 instead of monthly API rental
  3. Speed: local latency is often lower than cloud for repetitive tasks
  4. Offline: can still work when there is no network
  5. Customization: easy to create your own workflow, your own modelfile, your own stack