Language Model70B

Run Llama 3.3 70B on Mac Mini M4 Pro

Complete guide to running Llama 3.3 70B (Q4) locally on the Mac Mini M4 Pro with 24 GB unified memory using Ollama.

Speed

8–12 tok/s

Min Memory

24 GB

Software

Ollama, macOS 14+

Hardware Used in This Guide

Apple Mac Mini (M4 Pro, 2024)

mini-pc · Check Price on Amazon

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Step-by-Step Setup

  1. 01

    Install Ollama

    Download and install Ollama for macOS. The installer creates a menu-bar icon and background inference server.

    ollama --version
  2. 02

    Pull Llama 3.3 70B

    The Q4_K_M quantized model is about 40 GB. With 24 GB unified memory the model is partially offloaded to system RAM — performance is still excellent versus a pure CPU machine.

    ollama pull llama3.3:70b
  3. 03

    Verify GPU layers

    Check that Ollama is loading layers onto the Metal GPU rather than running pure CPU.

    ollama run llama3.3:70b "" --verbose 2>&1 | grep "gpu layers"
  4. 04

    Run your first prompt

    Start inference. Expect 8–12 tokens/s — comparable to a single RTX 4090 at this parameter count.

    ollama run llama3.3:70b "Write a haiku about silicon"
  5. 05

    Point any OpenAI-compatible app at Ollama

    Use base URL http://localhost:11434/v1 and any model name. No API key required.

    export OPENAI_BASE_URL=http://localhost:11434/v1
    export OPENAI_API_KEY=ollama

Optimization Tips

  • The M4 Pro's 273 GB/s memory bandwidth is why 70B is viable — bandwidth matters more than raw FLOPS for LLM inference.

  • If you hit memory pressure, close Safari and other heavy apps; macOS will reclaim RAM for Ollama automatically.

  • Llama 3.3 70B outperforms Llama 3.1 405B on many benchmarks — the 70B sweet spot is real.

  • Set `OLLAMA_NUM_PARALLEL=1` to dedicate all memory to one request for maximum single-session speed.

Other Hardware for Llama 3.3 70B

Apple Mac Mini (M4, 2024)

mini-pc · Check Price on Amazon · 16 GB Unified

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