Best AI Models for Mac Mini M4 Pro (24 GB)
24 GB unified − 3.5 GB OS overhead = 20.5 GB available for AI models
24 GB is the enthusiast tier for running AI models locally. It comfortably handles 7B–13B models at high quality and opens the door to larger 30B models at moderate quantization.
This is one of the most popular memory tiers for local AI, found in GPUs like the RTX 4090 and RTX 3090. You can run Llama 3 8B, Mistral 7B, and Qwen 2.5 7B at Q5_K_M or Q6_K quality with fast token generation and generous context windows. Larger 14B models like DeepSeek R1 Distill fit comfortably at Q4_K_M. For even bigger models, 30B class runs at Q2–Q3, but 70B models are generally too heavy for single-GPU inference at this tier.
Runs Well
- 7B models (Llama 3 8B, Mistral 7B) at Q5–Q8 quality
- 13B–14B models at Q4–Q5 quality
- Small models (3B–4B) at FP16 precision
- Multimodal models like LLaVA 7B
Challenging
- 30B models only at Q2–Q3 quantization
- 70B models do not fit in VRAM
- Large context windows with 14B+ models
What LLMs Can Mac Mini M4 Pro (24 GB) Run?
28 models · 2 excellent · 6 good
Showing compatibility for Mac Mini M4 Pro (24 GB)
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·36.1 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.9 GB | 36.1 t/s | 33K | EASY RUN | C36 |
Q4_K_M·62.3 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 62.3 t/s | 131K | EASY RUN | C31 |
Q4_K_M·268.9 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 268.9 t/s | 131K | EASY RUN | D27 |
Q4_K_M·268.9 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 0.7 GB | 268.9 t/s | 33K | EASY RUN | D27 |
Q4_K_M·175.7 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 175.7 t/s | 2K | EASY RUN | D27 |
Q4_K_M·33.0 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.4 GB | 33.0 t/s | 131K | EASY RUN | C37 |
Q4_K_M·35.6 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.0 GB | 35.6 t/s | 131K | EASY RUN | C36 |
Q8_0·36.1 t/s tok/s·4K ctx·EASY RUN | Q8_0 | 4.9 GB | 36.1 t/s | 4K | EASY RUN | C35 |
Q4_K_M·134.4 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.3 GB | 134.4 t/s | 8K | EASY RUN | D28 |
Q4_K_M·8.3 t/s tok/s·4K ctx·FAIR FIT | Q4_K_M | 21.4 GB | 8.3 t/s | 4K | FAIR FIT | B56 |
Q4_K_M·32.9 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.4 GB | 32.9 t/s | 131K | EASY RUN | C37 |
Q4_K_M·29.1 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 6.1 GB | 29.1 t/s | 8K | EASY RUN | C40 |
Mac Mini M4 Pro (24 GB) Specifications
- Brand
- Apple
- Chip
- M4 Pro
- Type
- Mini PC
- Unified Memory
- 24 GB
- Memory Bandwidth
- 273.0 GB/s
- GPU Cores
- 20
- CPU Cores
- 14
- Neural Engine
- 38.0 TOPS
- Release Date
- 2024-11-08
Get Started
Devices to Consider
Similar devices and upgrades with more memory or higher bandwidth
Frequently Asked Questions
- Can Mac Mini M4 Pro (24 GB) run Gemma 3 27B IT?
Yes, the Mac Mini M4 Pro (24 GB) with 24 GB unified memory can run Gemma 3 27B IT, Gemma 2 27B IT, Qwen3 32B, and 1130 other models. 125 models achieve excellent performance, and 196 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 Pro (24 GB)?
The Mac Mini M4 Pro (24 GB) has 24 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 20.5 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 Pro (24 GB) good for AI?
With 24 GB unified memory and 273.0 GB/s bandwidth, the Mac Mini M4 Pro (24 GB) is solid for running local AI models. It supports 321 models at good quality or better. You can run most popular 7B–14B models at good quality. Apple Silicon's Metal acceleration provides smooth token generation for interactive chat.
- What's the best model for Mac Mini M4 Pro (24 GB)?
The top-rated models for the Mac Mini M4 Pro (24 GB) are Gemma 3 27B IT, Gemma 2 27B IT, Qwen3 32B. 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 Pro (24 GB) for AI inference?
With 273.0 GB/s memory bandwidth, the Mac Mini M4 Pro (24 GB) achieves approximately 43 tok/s on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~21 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.
tok/s = (273 GB/s ÷ model GB) × efficiency
Apple Silicon achieves ~70% bandwidth efficiency thanks to unified memory and Metal acceleration.
Estimated speed on Mac Mini M4 Pro (24 GB)
~10 tok/s~10 tok/s~9 tok/s~12 tok/sReal-world results typically within ±20%.
- Can I run AI offline on Mac Mini M4 Pro (24 GB)?
Yes — once you download a model, it runs entirely on the Mac Mini M4 Pro (24 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.