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comparisons May 2, 2026

Best AI Models to Run on Mac Mini, MacBook Pro & Mac Studio in 2026

Which AI models can your Mac actually run? A practical guide to local LLMs on Apple Silicon — from Mac Mini M4 Pro to Mac Studio M4 Ultra, with specific model recommendations for each memory config.

Apple Silicon is the best consumer hardware for running AI models locally. The reason is simple: unified memory. On a discrete GPU, you’re limited to whatever VRAM is on the card — 8 GB, 12 GB, 24 GB. On a Mac, the GPU shares the full system memory pool. A Mac Mini with 48 GB of RAM has 48 GB of effective VRAM. No other consumer platform gives you that much GPU-accessible memory at that price point.

Combined with Metal GPU acceleration and Ollama’s native Apple Silicon support, Macs can run surprisingly large models at usable speeds. Here’s exactly what fits at each memory tier.

16 GB — MacBook Air, Mac Mini M4

At 16 GB, you can comfortably run 7B-8B parameter models at Q4 quantization. The OS and background processes take roughly 4-6 GB, leaving 10-12 GB for inference.

Best picks:

  • Qwen 3 8B at Q4_K_M (7.5 GB VRAM) — Strong general-purpose model with thinking mode. Top pick for this tier.
  • Llama 3.1 8B at Q4_K_M (6.3 GB VRAM) — Reliable and well-tested. Lighter than Qwen 3, leaving more headroom for context.
  • Gemma 3 12B at Q4_K_M — Pushes the limit but fits. Good for multimodal tasks (text + images).

At this tier, expect 40-80 tokens/sec on M4 chips. That’s faster than reading speed. The limiting factor isn’t speed — it’s which models fit.

24 GB — Mac Mini M4 Pro, MacBook Pro M4 Pro

The sweet spot for most users. With 24 GB, 14B models run comfortably at Q4 and some fit at Q8 for better quality.

Best picks:

  • Qwen 3 14B at Q4_K_M (12 GB VRAM) — Best all-around model for this tier. Handles coding, writing, reasoning.
  • Phi-4 14B at Q4_K_M (9.9 GB VRAM) — Microsoft’s model, excellent at math and STEM. Fits easily with room to spare.
  • Phi-4 14B at Q8_0 (16 GB VRAM) — Higher quality quantization. Tight fit, but it works.
  • Gemma 4 26B at Q4_K_M (20 GB VRAM) — MoE model from Google with only 4B active parameters. Squeezes in at 24 GB.

Check the full compatibility breakdown on the Mac Mini M4 Pro 24 GB page.

36 GB — MacBook Pro M3 Pro, Mac Studio M4 Max

At 36 GB you enter 27B-32B territory. These models are a significant step up from 14B — noticeably better at complex reasoning, coding, and following nuanced instructions.

Best picks:

  • Qwen 3.5 27B at Q4_K_M (19 GB VRAM) — Fits easily, leaving plenty of room for the OS.
  • Qwen 3 32B at Q4_K_M (23 GB VRAM) — Strong reasoning model. Comfortable fit.
  • DeepSeek R1 32B at Q4_K_M (20.7 GB VRAM) — Excellent chain-of-thought reasoning. Top pick for math and logic.
  • Gemma 4 31B at Q4_K_M (22 GB VRAM) — Google’s latest dense model with multimodal support.

See the full breakdown on the MacBook Pro M3 Pro 36 GB page.

48 GB — Mac Mini M4 Pro, MacBook Pro M4 Max

This is where 70B models become possible. At Q4 quantization, a 70B model needs around 43-44 GB of VRAM — a tight fit at 48 GB, but it works.

Best picks:

  • Llama 3.3 70B at Q4_K_M (43.5 GB VRAM) — Tight fit. Works, but leaves minimal headroom. Close other apps.
  • Cogito 70B at Q4_K_M (43 GB VRAM) — Similar fit to Llama 3.3, with hybrid reasoning capabilities.
  • Qwen 2.5 72B at Q4_K_M (44.7 GB VRAM) — Very tight. May need to reduce context length.
  • Qwen 3 32B at Q8_0 (34 GB VRAM) — If you want maximum quality from a 32B model instead of squeezing a 70B.

The Mac Mini M4 Pro 48 GB page has exact compatibility ratings for every model.

64 GB — MacBook Pro M4 Max, Mac Studio M4 Max

With 64 GB, 70B models run comfortably at Q4 with room for the OS, long context windows, and other apps running simultaneously. You can also push some 70B models to Q5 for better quality.

Best picks:

  • Llama 3.3 70B at Q4_K_M (43.5 GB VRAM) — Comfortable fit. No compromises needed.
  • DeepSeek R1 70B at Q4_K_M (43.5 GB VRAM) — Full reasoning capability, plenty of headroom.
  • Command A 111B at Q4_K_M (61 GB VRAM) — Cohere’s 111B model squeezed in at Q4. Tight but functional.

128-192 GB — Mac Studio M4 Ultra

This is the top end. A Mac Studio M4 Ultra with 192 GB can run models that would otherwise require multi-GPU server setups.

What opens up:

  • Qwen 3.5 122B at Q4_K_M (85 GB VRAM) — Fits on 128 GB. One of the most capable open models available.
  • Qwen 3.5 122B at Q8_0 (130 GB VRAM) — Full quality on 192 GB. No compromises.
  • Llama 3.1 405B at Q4_K_M (244.5 GB VRAM) — Needs the 512 GB Mac Studio M4 Ultra config.
  • Mistral Large 2 123B at Q4_K_M (67 GB VRAM) — Fits easily even on 128 GB.

At 192 GB, you can also run 70B models at full F16 precision, or keep multiple large models loaded simultaneously.

How to Check Your Specific Setup

Every Mac and model combination is different. Use the compatibility checker to see exactly what your hardware can run — it cross-references model VRAM requirements against your Mac’s available memory and tells you whether each quantization level will fit, run tight, or require CPU offloading.

The VRAM numbers above assume Q4_K_M quantization unless noted otherwise. Lower quantizations (Q3, Q2) will reduce memory usage at the cost of output quality. Higher quantizations (Q8, F16) improve quality but need more memory. See our quantization guide for the full breakdown on what those numbers mean.