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Best AI models that run on Mac Mini M4 (16 GB)

$799 — the cheapest serious local LLM box.

Apple · mini desktop
Mac Mini M4 (16 GB)
16 GB 11 GB usable 120 GB/s $799
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📺 Reviews of Mac Mini M4 (16 GB)
▶ Mac Mini M4 vs M3 Pro, 6700XT, 3080Ti — LLM Ollama side by side ▶ Intel Macbook vs Apple Silicon (M1, M3 Pro, M4) running LLM Ollama ▶ Ollama Mac MLX is here — 2× faster t/s for Apple Silicon

What models fit this build

Mac Mini M4 (16 GB) has 11 GB of usable memory. Here's which open-weights model sizes fit at each quant, at ~8K context with an FP8 KV cache.

Largest comfortable fit: 13–14B at Q5 (~11 GB).

Model size Q2Q4Q5Q8
7–8B Fits Fits Fits Fits
13–14B Fits Fits Fits Won't fit
30–32B Won't fit Won't fit Won't fit Won't fit
70–72B Won't fit Won't fit Won't fit Won't fit
~120B MoE Won't fit Won't fit Won't fit Won't fit
~235B MoE Won't fit Won't fit Won't fit Won't fit
~670B MoE Won't fit Won't fit Won't fit Won't fit
1T+ MoE Won't fit Won't fit Won't fit Won't fit

✓ = the weights + FP8 KV cache fit within this build's usable memory at ~8K context. Longer context needs more — size any model in the picker →

★

Our picks for this build

Sourced from the State of Local AI snapshot — the model + quant + backend we'd actually deploy on this hardware today, with the recipe in the setup guide below.

Also runs well

Gemma 4 12B Unified (dense)

12 B Apache 2.0 🤗

Jun 3 2026. Google's encoder-free 12B dense — unified decoder-only transformer with no separate vision/audio encoder; raw patches + audio waveforms project directly into embedding space. 256K context, 140+ langs, native multimodal (text/image/audio/video), Apache 2.0. Runs on a 16GB laptop (~8-9GB Q4). Strong for its size: AIME 77.5, GPQA 78.8, MMLU-Pro 77.2, LCB 72.0.

≥8 GB Q4 12 t/s
  • HLE5.2%
  • LCB72.0%

Coding: Gemma 4 12B scores LCB 72.0, AIME 77.5, Codeforces 1659 — strongest sub-16B open-weights coder. Encoder-free design means image+audio inputs add zero encoder latency. Runs on a 16GB laptop (8-9GB Q4) — the practical 'AI laptop' model of the month.

Agent: TAU2 69.0 is strong for 12B — usable in Cline / Roo Code for short agent loops, but sub-60B caps it at single-session autonomous work. Not a multi-hour orchestrator.

Setup guide for Mac Mini M4 (16 GB)

Every known recipe for running a model on this build — sourced from the State of Local AI 2026-07-05 snapshot (2026-07-05). Pick the one that matches your model + quant, then follow the linked original write-up.

Recipes for Mac Mini M4 (16 GB)
ModelDecode tok/sPrompt processingRecipeRuns
qwen3-6-35b-a3b-moe@ MLX-4bit on MLX-LM~17@ 4K—maloyan.xyz (M4 16GB) ↗
gemma-4-12b@ MLX 4-bit on Ollama (MLX) / mlx-lm——Ollama model library (Apple-Silicon MLX build; runnable recipe). Fit corroborated by Gemma 4 launch HN thread (Q4_K_M ~6.6 GB). ↗

Want to compare this against other builds? Open the live picker (Q2 / Q4 / Q5 / Q8 toggles) or see best build by budget.

See all recipes

Every open-weights model that fits, ranked by composite score

Composite blends benchmark averages (60 %) with editorial 0-5 ratings (40 %). Closed-frontier references mix into the ranking and stay amber-tinted.

Model↕tg/s↕pp↕TTFT @ 100K↕HLE↕TB2↕SWE-Pro↕SWE-Ver↕Aider↕LCB↕GPQA↕MMLU-Pro↕Score↕
Qwen 3.5 9B9 B · dense🤗
20 t/s220 pp——————65.6%81.7%82.5%4623
12 t/s120 pp—5.2%————72.0%78.8%77.2%3759
Llama 3.1 8B8 B · dense🤗
22 t/s220 pp———————34.6%49.0%2527
Qwen 3 8B8 B · dense🤗
22 t/s220 pp—2.8%—————47.0%65.5%2325
Gemini 3.1 ProGoogle DeepMind · closed125 t/s—2.1 min44.7%80.2%54.2%80.6%—91.7%94.3%91.0%—
ChatGPT 5.5OpenAI · closed61 t/s—1.6 min52.2%82.0%58.6%88.7%88.0%—93.6%——
Claude Sonnet 5Anthropic · closed———57.4%80.4%63.2%——————
Claude Opus 4.8Anthropic · closed59 t/s—2.9 min57.9%—69.2%88.6%——93.6%——
Open in the live picker (Q2 / Q4 / Q5 / Q8 toggles) → Compare Mac Mini M4 (16 GB) with another build → Try other hardware → Submit a benchmark for Mac Mini M4 (16 GB) ↗

Last updated 2026-07-11