MacBook Pro M2 Pro 16GB
Apple · M2 Pro · 16GB Unified Memory · Can run 36 models
Buy Apple
| Manufacturer | Apple |
| Unified Mem | 16 GB |
| Chip | M2 Pro |
| CPU Cores | 12 |
| GPU Cores | 19 |
| Neural Engine | 16 |
| Bandwidth | 200 GB/s |
| MSRP | $1,999 |
| Released | Jan 17, 2023 |
AI Notes
The MacBook Pro M2 Pro 16GB delivers double the memory bandwidth of the base M2 at 200 GB/s, resulting in significantly faster inference. It runs 7B models at high speed and handles 13B models comfortably. The Pro chip's active cooling sustains performance during extended inference sessions.
Compatible Models
| Model | Parameters | Best Quant | VRAM Used | Fit | Est. Speed |
|---|---|---|---|---|---|
| Gemma 3 1B | 1B | Q8_0 | 2 GB | Runs | ~100 tok/s |
| Llama 3.2 1B | 1B | Q8_0 | 3 GB | Runs | ~67 tok/s |
| DeepSeek R1 1.5B | 1.5B | Q8_0 | 3 GB | Runs | ~67 tok/s |
| Gemma 2 2B | 2B | Q8_0 | 4 GB | Runs | ~50 tok/s |
| Llama 3.2 3B | 3B | Q8_0 | 5 GB | Runs | ~40 tok/s |
| Phi-3 Mini 3.8B | 3.8B | Q8_0 | 5.8 GB | Runs | ~34 tok/s |
| Phi-4 Mini 3.8B | 3.8B | Q4_K_M | 4.5 GB | Runs | ~44 tok/s |
| Gemma 3 4B | 4B | Q4_K_M | 5 GB | Runs | ~40 tok/s |
| Qwen 3 4B | 4B | Q4_K_M | 4.5 GB | Runs | ~44 tok/s |
| DeepSeek R1 7B | 7B | Q8_0 | 9 GB | Runs | ~22 tok/s |
| Mistral 7B | 7B | Q8_0 | 9 GB | Runs | ~22 tok/s |
| Qwen 2.5 7B | 7B | Q8_0 | 9 GB | Runs | ~22 tok/s |
| Qwen 2.5 Coder 7B | 7B | Q8_0 | 9 GB | Runs | ~22 tok/s |
| DeepSeek R1 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~27 tok/s |
| Llama 3.1 8B | 8B | Q8_0 | 10 GB | Runs | ~20 tok/s |
| Qwen 3 8B | 8B | Q4_K_M | 7.5 GB | Runs | ~27 tok/s |
| Gemma 2 9B | 9B | Q8_0 | 11 GB | Runs | ~18 tok/s |
| Gemma 3 12B | 12B | Q4_K_M | 10.5 GB | Runs | ~19 tok/s |
| Mistral Nemo 12B | 12B | Q4_K_M | 9.5 GB | Runs | ~21 tok/s |
| DeepSeek R1 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~20 tok/s |
| Phi-4 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~20 tok/s |
| Qwen 2.5 14B | 14B | Q4_K_M | 9.9 GB | Runs | ~20 tok/s |
| Qwen 2.5 Coder 14B | 14B | Q4_K_M | 12 GB | Runs | ~17 tok/s |
| Qwen 3 14B | 14B | Q4_K_M | 12 GB | Runs | ~17 tok/s |
| Codestral 22B | 22B | Q4_K_M | 14.7 GB | Runs (tight) | ~14 tok/s |
| StarCoder2 15B | 15B | Q8_0 | 17 GB | CPU Offload | ~12 tok/s |
| Devstral 24B | 24B | Q4_K_M | 17 GB | CPU Offload | ~12 tok/s |
| Mistral Small 3.1 24B | 24B | Q4_K_M | 18 GB | CPU Offload | ~11 tok/s |
| Gemma 2 27B | 27B | Q4_K_M | 17.7 GB | CPU Offload | ~11 tok/s |
| Gemma 3 27B | 27B | Q4_K_M | 20 GB | CPU Offload | ~10 tok/s |
| Qwen 3 30B-A3B (MoE) | 30B | Q4_K_M | 22 GB | CPU Offload | ~9 tok/s |
| DeepSeek R1 32B | 32B | Q4_K_M | 20.7 GB | CPU Offload | ~10 tok/s |
| Qwen 2.5 32B | 32B | Q4_K_M | 20.7 GB | CPU Offload | ~10 tok/s |
| Qwen 2.5 Coder 32B | 32B | Q4_K_M | 23 GB | CPU Offload | ~9 tok/s |
| Qwen 3 32B | 32B | Q4_K_M | 23 GB | CPU Offload | ~9 tok/s |
| Command R 35B | 35B | Q4_K_M | 22.5 GB | CPU Offload | ~9 tok/s |
7
model(s) are too large for this hardware.