Best Mac for Local LLMs

Start with usable unified memory

Local inference fails as capacity planning. At Q4, a rough planning rule is parameters × bits ÷ 8, plus headroom for context and macOS. A 70B-class model is a different machine from a 7B–13B daily driver.

Treat these as planning buckets, not rankings:

• 32GB — comfortable for smaller chat and coding models; painful if every useful checkpoint spills.
• 64GB — the practical sweet spot for many builders who want mid-size open weights without multi-GPU drama.
• 96GB+ — where larger models and longer context stop being a science project on Apple Silicon.

Always leave OS and app headroom. Marketing “unified memory” is not the same as free VRAM on a discrete card.

Then pick the chip you will keep on the desk

M-series Max and Ultra configurations matter when you already know the memory floor. Buy the chip for sustained throughput and the ports/thermals you will live with — not because a launch SKU sounds newer.

A lower-chip Mac with enough memory usually beats a higher-chip Mac that is memory-starved for the models you actually run.

When a Mac is the wrong local-AI buy

• You need day-one CUDA for random GitHub READMEs
• Gaming performance is a co-equal requirement
• You want the cheapest GB of discrete VRAM on the used market

In those cases, start with the GPU for local AI guide and the /gpus tables instead.

What to judge before spending

• Largest model (parameters + quantization + context) you will keep loaded weekly
• Whether Apple’s stack matches the tools you already use
• Laptop portability versus a Studio-class desk machine
• Whether cloud burst capacity is cheaper than buying the next memory tier — see buy GPU vs cloud GPU

The bottom line

Buy memory for the model, then buy the Mac form factor you will keep. Use the local AI model fit finder and the /macs capacity notes before you commit to a configuration.