M1 Pro
68 AI models fit in 16 GB VRAM at Q4 native. 15 more run with CPU offloading. Real benchmarks below.
llama.cpp 0.2.x · CUDA 12 · Ubuntu 22.04 · Prices verified on Amazon · methodology →
Execution Context
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Full Specifications
Apple · 2021-10
| VRAM | 16 GB Unified Memory |
| Bandwidth | 200 GB/s |
| FP16 TFLOPS | 5.2 |
| AI Score | 25 / 140 |
| Tensor Cores | 16 |
| TDP | 30 W |
| Price Band | Integrada |
| Released | 2021-10 |
AI Benchmarks
Real inference measurements — llama.cpp Q4_K_M
| Task | Result |
|---|---|
| Llama 1B Q4 | 105 tok/s |
| Llama 3B Q4 | 42 tok/s |
| Llama 7B Q4 | 22 tok/s |
| Llama 13B Q4 | 9 tok/s |
| Llama 30B Q4 | VRAM N/A |
| Llama 70B Q4 | Offload or multi-GPU |
| Stable Diffusion 512px | 15s / img |
| Whisper Large RTF | 1.5x |
RTF < 1.0 = faster than real time. For Stable Diffusion and Whisper lower is better; for tokens/s higher is better.
Compare M1 Pro with another GPU
Is an upgrade worth it? Compare specs and real benchmarks side by side.
Open comparator →Compatible AI Models — M1 Pro
68 models run fully in VRAM · 15 with CPU offloading
Flux.1 Dev
Whisper Large V3
Stable Diffusion 3.5 Large
Stable Diffusion 3.5 Medium
Gemma 4 27B
Gemma 2 27B
Mistral Small 3
Phi-4
Show all 68 compatible models →
Also runs with CPU offloading (15)
- FLUX.2 Dev 8.8 GB Q2
- Qwen2.5-Coder 32B 9.6 GB Q2 How to install →
- DeepSeek R1 Distill 32B 9.6 GB Q2 How to install →
- Qwen2.5 32B 9.6 GB Q2 How to install →
- Qwen3.5 35B-A3B 9.6 GB Q2 How to install →
- Gemma 3 27B 8.1 GB Q2 How to install →
- Gemma 4 31B 8.5 GB Q2 How to install →
- Mixtral 8x7B 14 GB Q2 How to install →
- Qwen3 32B 8.8 GB Q2 How to install →
- Qwen3-Coder 30B-A3B 8.3 GB Q2 How to install →
- Qwen3 30B-A3B 8.3 GB Q2 How to install →
- CodeLlama 34B 10 GB Q2 How to install →
- Yi 1.5 34B 10 GB Q2 How to install →
- Nous Hermes 2 Mixtral 8x7B 13 GB Q2 How to install →
- Phi-3.5 MoE 11 GB Q2 How to install →
M1 Pro · Amazon
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M1 Pro for Local AI
La M1 Pro con 16GB de Unified Memory es una opción equilibrada para IA local. Puede correr modelos 7B y 13B en Q4 sin problemas, y modelos 7B en FP16. Es ideal para usuarios que quieren una experiencia fluida con los modelos más populares como Llama 3.1 8B, Mistral 7B, Phi-4 o Qwen2.5 14B.
Los benchmarks reales muestran 22 tokens/segundo en Llama 7B Q4. Para generación de imágenes, Stable Diffusion XL y SD 3.5 Medium caben perfectamente. Whisper Large V3 para transcripción de audio también es compatible.
Con 16GB de VRAM tienes acceso a 68 modelos completos y 15 adicionales con offloading parcial. Usa la calculadora de VRAM para ver las opciones de cuantización disponibles para cada modelo.
Plan your full AI build
M1 Pro · 16 GB VRAM — configure PSU, RAM, storage and check compatible models.
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Not sure which model to run on your M1 Pro?
The VRAM calculator tells you exactly which quantization you need.
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