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Once a model is downloaded, it runs with the Wi-Fi off
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).
Your 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. Every Apple Silicon Mac (M1 and newer) can run local AI models. Unified memory means even an 8 GB MacBook Air can run compact models such as Gemma 4 E4B (multimodal with audio), Qwen 3 4B or Phi-4 Mini, and more memory lets you run larger models such as Gemma 4 26B-A4B or Qwen 3.6-35B-A3B on a 24 GB Mac.
What is the best local LLM for Mac?
It depends on your unified memory. As of 17 October 2026, MiMo-V2.6-Flash ranks #1 (LLMCheck Score 82) at ~79 tok/s estimated on an M5 Ultra, but it needs a Mac Studio with at least 192 GB of unified memory. On a Mac with 32 GB or less, the best pick is Qwen 3.6-35B-A3B (Score 81; 64 tok/s community-measured on a Mac mini M6 32 GB; needs 24 GB). Best pick for each RAM tier (llmcheck.net/best-llm/by-ram/): 8 GB: Nanbeige4.2-3B; 16 GB: Gemma 4 12B; 24 GB: Qwen 3.6-35B-A3B; 32 GB: Qwen 3.6-35B-A3B; 64 GB: Qwen 3.6-35B-A3B; 128 GB: Qwen3.8-Flash-Next; 192–256 GB: MiMo-V2.6-Flash; 512 GB: MiMo-V2.6-Flash. The strongest dense model is Qwen3.8-27B. Use the checker above for a recommendation matched to your exact Mac.
What software do I need to run a local LLM?
For beginners, apps such as LM Studio or Jan give you a chat window: download, pick a model and start chatting, with no terminal or coding. For more control, Ollama runs in the background and, since version 0.40, runs supported models on Apple's MLX by default on Apple Silicon. Developers may prefer llama.cpp or Apple's MLX directly.
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?
According to the LLMCheck index, a model needs about 0.57 GB per billion parameters at 4-bit (Q4_K_M) plus about 2 GB for the KV cache and runtime, so an 8B model needs about 7 GB and a 27B dense model about 18 GB. A mixture-of-experts model must hold all of its parameters, not just the active ones. As a guide: 8 GB runs 3-8B models; 16 GB runs 12-14B models and GPT-oss 20B; 24-32 GB runs 27-35B models such as Qwen3.8-27B and Qwen 3.6-35B-A3B; 64 GB runs 70B dense models and GPT-oss 120B; 128 GB runs 120-230B mixture-of-experts models at 2-4 bits; 256-512 GB Mac Studios run 300B to 1T models at 2-4 bits. macOS lets the GPU use about 75% of unified memory by default.
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 the 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.