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Best Local AI Models for Mac (2026)

Apple Silicon’s unified memory means your RAM is effectively your VRAM, which makes Macs surprisingly good at running AI locally. Here’s what’s worth pulling in April 2026.

16 GB Mac

Stick to 7B to 14B models at Q4 quantization.

  • llama3.1:8b (4.7 GB). Reliable general purpose default.
  • qwen3.5:7b (4.7 GB). Newer generalist. Matches or beats GPT-OSS-120B on several benchmarks (81.7 on GPQA Diamond).
  • phi4 (9 GB). Microsoft’s 14B model. Beats larger models on math and logic, scoring 80% on MATH versus 68% for Llama 3.3 8B.
  • qwen2.5-coder:7b (4.7 GB). Best small coding model.
  • gemma4:e4b (3 GB). Google’s latest small model. Natively multimodal, handles images out of the box.

32 GB Mac

Comfortably runs 14B to 30B models.

  • qwen3.5:9b (6 GB). Best generalist in this class, faster than older 14B models.
  • deepseek-r1:32b (20 GB). Strong reasoning, shows its chain of thought.
  • qwen2.5-coder:32b (20 GB). Matches GPT-4o on coding (92.7% HumanEval).
  • gemma4:e12b (8 GB). Google’s mid-tier Gemma 4, multimodal, Apache 2.0 licensed.

64 GB Mac

Flagship territory.

  • qwen3-coder:30b (20 GB). Top open source coding model.
  • gemma4:e27b (17 GB). Google’s largest Gemma 4, multimodal, great for vision tasks.
  • llama4:scout (67 GB). Meta’s newest. Natively multimodal, 109B MoE with 17B active.
  • deepseek-r1:70b (43 GB). Best open source reasoning model.
  • devstral:24b (14 GB). Purpose-built coding agent.

Mac Studio (128 GB)

  • llama4:maverick (about 64 GB at Q2). Multimodal, 400B MoE.
  • deepseek-v3 (72 GB at Q4). Close to GPT-4 on most tasks.

Picking by use case

Writing and chat: llama3.1:8b, qwen3.5:9b, llama4:scout.

Coding: qwen2.5-coder:7b, qwen2.5-coder:32b, qwen3-coder:30b.

Reasoning and math: phi4, deepseek-r1:32b, deepseek-r1:70b.

Vision: gemma4:e4b, gemma4:e27b, llama4:scout.

On speed

Ollama 0.19 switched to MLX on Apple Silicon in March, making local inference 30 to 50% faster. Update if you haven’t.

Memory bandwidth matters more than core count. An M4 Max (546 GB/s) outperforms an M4 Pro at the same model size because inference is bandwidth bound.

Gemma 4 running in Elvean

Gemma 4 (e4b) answering multimodal prompts locally on Apple Silicon, no internet required.

Running any of these in Elvean

ollama pull qwen3.5:9b

Open Elvean, pick the model from the model picker, and chat. See the Ollama setup guide if you haven’t configured Ollama yet.