DT

Written and reviewed by DeviceterraDeviceterra editorial team · Updated August 2026

KEY TAKEAWAY

Apple Silicon uses unified memory shared by the CPU and GPU. This can help local AI, but macOS and other apps still need part of that memory.

TRY IT YOURSELF

Run a first model on an M-series Mac

Apple Silicon Macs use one shared pool of memory for the CPU and GPU. This helps local AI, but macOS and your open apps also need part of the same memory.

  1. 1

    Check the Mac

    Select the Apple menu in the top-left corner, then About This Mac. Write down the chip name and memory.

  2. 2

    Download Ollama

    Open Safari, visit ollama.com/download, select macOS, and open the downloaded file.

  3. 3

    Open Terminal

    Press Command and Space. Type Terminal, then press Enter.

  4. 4

    Run a small model

    Type this command and press Enter:

    ollama run qwen3:4b
  5. 5

    Watch memory pressure

    Press Command and Space, type Activity Monitor, and open it. Select Memory. Green memory pressure is a better sign than the used-memory number alone.

How to know it worked
  • The model answers and memory pressure stays stable.
  • You recorded the exact chip and memory size.
  • You tested a real task instead of choosing from model size alone.
UNDERSTAND THE DETAILS

Use the explanations below when you want to know why each step matters.

01

What unified memory changes

On an Apple Silicon Mac, the CPU and GPU can work with the same memory pool. The system does not need a separate copy in normal RAM and VRAM for every operation.

That is useful for local AI, but the full memory number is not free for the model. macOS, the engine, the context, and other apps all use the same pool.

02

Choose an engine

  • Use LM Studio when you want a visual model browser and chat app.
  • Use Ollama when you want simple commands and a local service for other apps.
  • Use MLX LM when you want an Apple-focused Python workflow.
  • Use llama.cpp when you need direct GGUF control and detailed settings.
03

Pick a model that leaves headroom

A Mac with 8 GB should stay with very small models. A 16 GB Mac can often test useful 3B to 8B quantized models. Larger memory options can test larger models, longer contexts, or more than one local service.

These are planning ranges, not promises. Measure memory pressure on the exact Mac before using the result for important work.

04

Test the real task

  • Close heavy apps for the first test.
  • Use the exact model file and engine you plan to keep.
  • Measure first-word delay and answer speed.
  • Watch memory pressure, not only total memory used.
  • Test after the laptop has warmed up.
  • Score answer quality with real examples.
05

When a Mac is a strong choice

A Mac can be a good local AI workstation for one person who values quiet operation, a simple desktop, and shared memory. MLX also gives developers an Apple-focused toolset.

For a service used by many people, compare server hardware, supported accelerators, monitoring, and upgrade options before deciding.

RESEARCH SOURCES

Official facts and real user evidence

Official documentation supports product and model facts. Community discussions show real setups, failures, and questions. A community result is supporting evidence, not a promise that another computer will perform the same way.

MAKE IT PRACTICAL

Find a model your computer can run.

MamiLens checks your hardware and shows a careful starting point.

Run the free compatibility check →

This guide is educational. Model software, licenses, and hardware support can change. Check official sources before an important deployment.