Best AI Models for MacBook Pro 16" 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 MacBook Pro 16" M4 Pro (24 GB) Run?
105 models · 7 excellent · 15 good
Showing compatibility for MacBook Pro 16" M4 Pro (24 GB)
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·65.9 t/s tok/s·262K ctx·EASY RUN | Q4_K_M | 2.9 GB | 65.9 t/s | 262K | EASY RUN | C31 |
Q4_K_M·34.6 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 5.5 GB | 34.6 t/s | 41K | EASY RUN | C38 |
Q4_K_M·36.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 36.1 t/s | 131K | EASY RUN | C37 |
Q4_K_M·22.2 t/s tok/s·FAIR FIT | Q4_K_M | 8.6 GB | 22.2 t/s | — | FAIR FIT | B51 |
Q4_K_M·35.9 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.3 GB | 35.9 t/s | 131K | EASY RUN | C37 |
Q4_K_M·48.3 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.0 GB | 48.3 t/s | 33K | EASY RUN | C34 |
Q4_K_M·67.3 t/s tok/s·EASY RUN | Q4_K_M | 2.8 GB | 67.3 t/s | — | EASY RUN | C31 |
Q4_K_M·20.1 t/s tok/s·33K ctx·FAIR FIT | Q4_K_M | 9.5 GB | 20.1 t/s | 33K | FAIR FIT | B55 |
Q4_K_M·38.8 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 4.9 GB | 38.8 t/s | 33K | EASY RUN | C36 |
Q4_K_M·53.2 t/s tok/s·EASY RUN | Q4_K_M | 3.6 GB | 53.2 t/s | — | EASY RUN | C33 |
Q4_K_M·56.2 t/s tok/s·4K ctx·EASY RUN | Q4_K_M | 3.4 GB | 56.2 t/s | 4K | EASY RUN | C32 |
Q4_K_M·66.6 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 66.6 t/s | 131K | EASY RUN | C31 |
Q4_K_M·90.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.1 GB | 90.1 t/s | 131K | EASY RUN | C30 |
Q4_K_M·22.2 t/s tok/s·FAIR FIT | Q4_K_M | 8.6 GB | 22.2 t/s | — | FAIR FIT | B51 |
Q4_K_M·233.0 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.8 GB | 233.0 t/s | 131K | EASY RUN | D27 |
Q4_K_M·72.4 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 2.6 GB | 72.4 t/s | 2K | EASY RUN | C31 |
MacBook Pro 16" M4 Pro (24 GB) Specifications
- Brand
- Apple
- Chip
- M4 Pro
- Type
- Laptop
- 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 MacBook Pro 16" M4 Pro (24 GB) run Gemma 3 27B IT?
Yes, the MacBook Pro 16" M4 Pro (24 GB) with 24 GB unified memory can run Gemma 3 27B IT, Qwen3.6 27B, Gemma 4 26B A4B IT, and 1264 other models. 69 models achieve excellent performance, and 153 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 MacBook Pro 16" M4 Pro (24 GB)?
The MacBook Pro 16" 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 MacBook Pro 16" M4 Pro (24 GB) good for AI?
With 24 GB unified memory and 273.0 GB/s bandwidth, the MacBook Pro 16" M4 Pro (24 GB) is solid for running local AI models. It supports 222 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 MacBook Pro 16" M4 Pro (24 GB)?
The top-rated models for the MacBook Pro 16" M4 Pro (24 GB) are Gemma 3 27B IT, Qwen3.6 27B, Gemma 4 26B A4B IT. 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 MacBook Pro 16" M4 Pro (24 GB) for AI inference?
With 273.0 GB/s memory bandwidth, the MacBook Pro 16" 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 MacBook Pro 16" M4 Pro (24 GB)
~11 tok/s~11 tok/s~12 tok/s~11 tok/sReal-world results typically within ±20%.
- Can I run AI offline on MacBook Pro 16" M4 Pro (24 GB)?
Yes — once you download a model, it runs entirely on the MacBook Pro 16" 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.
- Will MacBook Pro 16" M4 Pro (24 GB) throttle or drain the battery running LLMs?
Yes, sustained LLM inference is one of the most thermally demanding workloads a laptop can face — more continuous than most gaming sessions. Plugged in, the MacBook Pro 16" M4 Pro (24 GB) will run at full performance but may still throttle if the chassis thermal limit is hit under prolonged load; expect the fans to spin up noticeably. On battery, macOS and the firmware typically cap power draw to protect the cells, which can reduce inference speed by 20–40%. For long sessions (generating documents, batch processing), keep the laptop plugged in, on a hard flat surface with the vents unobstructed, and consider a 7B model at Q4_K_M rather than a larger model — smaller models generate less heat while still giving useful results. Real-world speed on battery is typically within ±20% of the on-charger figure for short bursts, but diverges more over 10+ minutes of continuous generation.