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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.

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.

EDITORIAL SOURCES

Official references used

These primary sources support the technical concepts in this guide. Product behavior changes, so verify the current documentation before deployment.

MAKE IT PRACTICAL

Find a model your computer can run.

The LocalLens advisor applies conservative memory rules and tells you when the evidence is insufficient.

Run the free compatibility check →

This guide is educational and reviewed for practical accuracy. Model software, licenses and hardware support change; verify official sources before a production deployment.