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According to the LLMCheck index, the best local LLM you can run on a Mac is Alibaba's Qwen 4.1 32B-A3B — 80% on SWE-Verified at an estimated ~62 tok/s on a 24 GB Mac. The fastest is Gemma 4 E2B (est. ~155 tok/s). All speeds are index estimates. Choose your Mac below for a recommendation matched to your exact hardware.
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 hardware. For 8 GB Macs: Gemma 4 E4B (multimodal, ~125 tok/s), Qwen 3.5 9B, or Phi-4 Mini. For 16-24 GB: Gemma 4 26B-A4B (Arena #6, ~48 tok/s) or Qwen 3.5 27B. For 24-32 GB: Gemma 4 31B (Arena #3), Qwen 3.5 35B, or DeepSeek R1 32B. For 64 GB+: Llama 3.3 70B or Qwen 2.5 72B. 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.