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What Mac do you have?
Select your Mac model. Not sure? Check → About This Mac.
Open Terminal on your Mac and run this command:
system_profiler SPHardwareDataType
Then paste the output below:
Which chip does it have?
Select your Apple Silicon or Intel chip.
How much memory?
Select your unified memory (shown in → About This Mac).
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Recommended Local AI Models
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Frequently Asked Questions
Can I run an LLM locally on my Mac?
Yes — all Apple Silicon Macs (M1 and newer) can run local AI models. The unified memory architecture means even an 8 GB MacBook Air can run compact models like Gemma 4 E4B (multimodal with audio), Qwen 3 4B, or Phi-4 Mini. More memory lets you run larger models like Gemma 4 26B-A4B (Arena AI #6) on 24 GB Macs.
What is the best local LLM for Mac?
It depends on your unified memory. For 8 GB Macs: Maple Preview 20B-A1B (~200 tok/s, vendor-reported), LFM2.5-2.6B, or Bonsai 27B 1-bit. For 16 GB: Mistral Small 3.2 24B or Phi-4 14B. For 24–32 GB: Qwen3.8-27B — the current LLMCheck #1 (Apache 2.0, native vision, ~29 tok/s estimated) — or Qwen 3.6-27B just behind it, still the strongest verified result at 77.2% SWE-bench Verified. For 64 GB: Qwen3-Coder-Next or GLM-4.5-Air. For 128 GB: DeepSeek V4 Flash (~39 tok/s, community-reported). Use the checker above to get a personalized recommendation.
What software do I need to run a local LLM?
For beginners, apps like LM Studio, Jan, or GPT4All provide a familiar chat interface — just download, pick a model, and start chatting. No terminal or coding required. For more control, Ollama is a lightweight tool that runs in the background. Developers may prefer llama.cpp or Apple's MLX framework for maximum performance.
Is running AI locally on Mac free?
Yes, completely free. All the models and software listed here are open-source or free to use. There are no subscriptions, no per-message fees, and no usage limits. The only cost is the Mac hardware you already own.
How much RAM do I need for a local LLM?
As a rule of thumb, you need about 75% of a model's parameter count in GB of RAM (for Q4 quantized models). An 8 GB Mac can run models up to ~3-4B parameters comfortably. 16 GB handles 7-8B models. 32 GB handles 14-27B models. 64 GB+ unlocks the largest 70B models.
Can Intel Macs run local LLMs?
Yes, but with limitations. Intel Macs lack the Neural Engine and unified memory of Apple Silicon, so models run on CPU only, which is significantly slower. Compact models (1-3B parameters) are still usable. For the best experience, Apple Silicon (M1 or later) is strongly recommended.