Best AI Models for Mac Mini M4 (32 GB)
32 GB unified − 3.5 GB OS overhead = 28.5 GB available for AI models
32 GB positions this hardware in the professional tier for local AI. Most popular open-source models run comfortably, and even large 70B parameter models are accessible at lower quantization levels.
This memory amount is a sweet spot for enthusiasts and professionals. You can run 13B–30B models like DeepSeek R1 Distill at Q5 or Q6 quality with smooth token generation, and 7B models at near-lossless precision. The 70B class of models (Llama 3 70B, Qwen 72B) becomes possible at Q2–Q3 quantization, though with some quality trade-off. For day-to-day use with coding assistants, chat models, and reasoning tasks, this tier delivers an excellent experience.
Runs Well
- 7B–13B models at Q6–Q8 quality
- 14B–30B models at Q4–Q5 quality
- Small models (3B–7B) at FP16 precision
- Vision-language models at good quality
Challenging
- 70B models only at Q2–Q3 (noticeable quality loss)
- Large context windows with 30B+ models
What LLMs Can Mac Mini M4 (32 GB) Run?
254 models · 29 excellent · 63 good
Showing compatibility for Mac Mini M4 (32 GB)
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·28.0 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 16.1 GB | 28.0 t/s | 262K | RUNS WELL | A69 |
Q4_K_M·4.8 t/s tok/s·262K ctx·BARELY RUNS | Q4_K_M | 17.4 GB | 4.8 t/s | 262K | BARELY RUNS | C42 |
Q4_K_M·4.6 t/s tok/s·131K ctx·BARELY RUNS | Q4_K_M | 18.3 GB | 4.6 t/s | 131K | BARELY RUNS | C41 |
Q4_K_M·27.5 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 21.4 GB | 27.5 t/s | 262K | RUNS WELL | A68 |
Q4_K_M·31.0 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 19.3 GB | 31.0 t/s | 262K | RUNS WELL | A71 |
Q4_K_M·30.2 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 21.2 GB | 30.2 t/s | 262K | RUNS WELL | A71 |
Q4_K_M·29.5 t/s tok/s·500K ctx·RUNS WELL | Q4_K_M | 18.7 GB | 29.5 t/s | 500K | RUNS WELL | A70 |
Q4_K_M·28.0 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 16.1 GB | 28.0 t/s | 262K | RUNS WELL | A69 |
Q4_K_M·23.5 t/s tok/s·262K ctx·DECENT | Q4_K_M | 21.9 GB | 23.5 t/s | 262K | DECENT | B64 |
Q4_K_M·22.6 t/s tok/s·203K ctx·DECENT | Q4_K_M | 19.8 GB | 22.6 t/s | 203K | DECENT | B63 |
Q4_K_M·76.7 t/s tok/s·128K ctx·RUNS GREAT | Q4_K_M | 5.5 GB | 76.7 t/s | 128K | RUNS GREAT | S87 |
Q4_K_M·27.5 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 21.4 GB | 27.5 t/s | 262K | RUNS WELL | A68 |
Q4_K_M·4.1 t/s tok/s·262K ctx·BARELY RUNS | Q4_K_M | 20.4 GB | 4.1 t/s | 262K | BARELY RUNS | C40 |
Q4_K_M·29.3 t/s tok/s·262K ctx·RUNS WELL | Q4_K_M | 18.7 GB | 29.3 t/s | 262K | RUNS WELL | A70 |
Q4_K_M·62.8 t/s tok/s·131K ctx·RUNS WELL | Q4_K_M | 5.3 GB | 62.8 t/s | 131K | RUNS WELL | A84 |
Q4_K_M·41.8 t/s tok/s·131K ctx·RUNS WELL | Q4_K_M | 7.7 GB | 41.8 t/s | 131K | RUNS WELL | A76 |
Mac Mini M4 (32 GB) Specifications
- Brand
- Apple
- Chip
- M4
- Type
- Mini PC
- Unified Memory
- 32 GB
- Memory Bandwidth
- 120.0 GB/s
- GPU Cores
- 10
- CPU Cores
- 10
- Neural Engine
- 38.0 TOPS
- Release Date
- 2024-11-08
Get Started
Prompt Processing
Estimated for Qwen3.6 35B A3b Crown Halo Mtp Dynamic, the compute-bound phase that reads your prompt before the first reply token appears.
Short chat
1.8 s
512 tok prompt
Long chat
14.3 s
4,096 tok prompt
Document / codebase
114 s
32,768 tok prompt
Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →
Performance figures are estimates calibrated as of 2026-09-21 — see calibration basis →
Devices to Consider
Similar devices and upgrades with more memory or higher bandwidth
Frequently Asked Questions
- Can Mac Mini M4 (32 GB) run Gemma 4 26B A4B IT?
Yes, the Mac Mini M4 (32 GB) with 32 GB unified memory can run Gemma 4 26B A4B IT, Qwen3.8 27B, Muse Glimmer 30B, and 2382 other models. 538 models achieve excellent performance, and 548 run at good quality. Apple Silicon's unified memory architecture lets the GPU access the full memory pool without copying data, making it efficient for AI workloads.
- How much memory is available for AI on Mac Mini M4 (32 GB)?
The Mac Mini M4 (32 GB) has 32 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 28.5 GB is available for AI models. Unlike discrete GPUs where VRAM is separate from system RAM, Apple Silicon shares one memory pool between the CPU and GPU — this means no data copying overhead, but you share memory with macOS and open apps.
- Is Mac Mini M4 (32 GB) good for AI?
With 32 GB unified memory and 120.0 GB/s bandwidth, the Mac Mini M4 (32 GB) is very good for running local AI models. It supports 1086 models at good quality or better. This is a strong configuration for AI — 7B models run at maximum quality, and you can comfortably handle 14B models like DeepSeek R1 Distill 14B and larger.
- What's the best model for Mac Mini M4 (32 GB)?
The top-rated models for the Mac Mini M4 (32 GB) are Gemma 4 26B A4B IT, Qwen3.8 27B, Muse Glimmer 30B. For general chat, instruction-tuned 7B models give the best speed-to-quality ratio. For coding or reasoning, a 14B model at Q4_K_M is a sweet spot.
- How fast is Mac Mini M4 (32 GB) for AI inference?
With 120.0 GB/s memory bandwidth, the Mac Mini M4 (32 GB) achieves approximately 19 tok/s on a 7B model at Q4_K_M — that's functional for interactive use. A 14B model runs at ~9 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.
tok/s = (120 GB/s ÷ model GB) × efficiency
Apple Silicon achieves ~70% bandwidth efficiency thanks to unified memory and Metal acceleration.
Estimated speed on Mac Mini M4 (32 GB)
~28 tok/s~5 tok/s~5 tok/s~28 tok/sReal-world results typically within ±20%.
- Can I run AI offline on Mac Mini M4 (32 GB)?
Yes — once you download a model, it runs entirely on the Mac Mini M4 (32 GB) without internet. Applications like Ollama and LM Studio make it straightforward to download, manage, and run models locally. All your conversations stay private on your device with zero data sent to external servers. This is one of the key advantages of local AI: complete privacy, no API costs, and no rate limits.