Mac mini M4 32GB vs AMD Radeon AI PRO R9700
For running models locally, priced in India. At 8k context the first runs 26 models fully in memory, the second 29.
In short
Both have 32 GB of memory for models; the Mac mini M4 32GB has 120 GB/s of bandwidth and the AMD Radeon AI PRO R9700 640 GB/s. At 8k context the AMD Radeon AI PRO R9700 runs 29 of the models we track fully in memory and the Mac mini M4 32GB 26; only the AMD Radeon AI PRO R9700 holds Qwen 3.6 35B-A3B. On the models both run fully in memory, the AMD Radeon AI PRO R9700 is about 4.5× as fast as the Mac mini M4 32GB (estimated, median across models).
Updated . Speeds are estimates; how they are worked out.
Specifications
| Spec | Mac mini M4 32GB | AMD Radeon AI PRO R9700 |
|---|---|---|
| Chip | Apple M4 (10-core GPU) | – |
| Memory bus | 128-bit | 256-bit |
| Memory bandwidth | 120 GB/s | 640 GB/s |
| FP16 compute | 3.76 TFLOPS | 191 TFLOPS |
| Software | Metal | ROCm |
| Unified memory | 32 GB | – |
| GPU can use by default | 21.3 GB | – |
| GPU can use, tuned | 24 GB | – |
| Operating system | macos | – |
| Processor | M4 10-core | – |
| Storage | 512 GB | – |
| GPU | – | Radeon AI PRO R9700 |
| Memory | – | 32 GB GDDR6 |
| Board power | – | 300 W |
| PCIe generation | – | Gen 5 |
| PCIe lanes | – | x16 |
What each runs
Estimated decode speed with the most precise quant that fits each one est.
| Model | Mac mini M4 32GB | AMD Radeon AI PRO R9700 |
|---|---|---|
| Qwen 3.8 27B | 4tok/sUD-Q5_K_M | 12tok/sQ8_0 |
| Qwen 3.6 27B | 4tok/sQ5_K_M | 12tok/sQ8_0 |
| Gemma 4 31B | 4tok/sQ4_K_M | 13tok/sQ6_K |
| Qwen 3.5 27B | 4tok/sQ5_K_M | 12tok/sQ8_0 |
| Qwen 3.6 35B-A3B | won't run | 79tok/sUD-Q6_K |
| Gemma 4 26B-A4B | 13tok/sUD-Q5_K_M | 47tok/sQ8_0 |
| GLM 4.7 Flash | 12tok/sQ5_K_M | 37tok/sQ8_0 |
| Devstral Small 2 24B | 4tok/sQ6_K | 13tok/sQ8_0 |
| Qwen 3.5 35B-A3B | won't run | 80tok/sQ6_K |
| Qwen3 Coder 30B-A3B | 18tok/sQ4_K_M | 65tok/sQ6_K |
| gpt-oss 20B | 18tok/sMXFP4 | 80tok/sMXFP4 |
| Kimi Linear 48B-A3B | won't run | 109tok/sQ4_K_M |
| Qwen3 32B | 4tok/sQ4_K_M | 12tok/sQ6_K |
| Gemma 4 12B | 6tok/sQ8_0 | 27tok/sQ8_0 |
| Qwen 3.5 9B | 8tok/sQ8_0 | 36tok/sQ8_0 |
| Gemma 3 27B | 4tok/sQ5_K_M | 12tok/sQ8_0 |
| Mistral Small 3.2 24B | 4tok/sQ6_K | 13tok/sQ8_0 |
| DeepSeek R1 Distill Qwen 32B | 4tok/sQ4_K_M | 12tok/sQ6_K |
| Ministral 3 14B | 5tok/sQ8_0 | 22tok/sQ8_0 |
| Qwen3 14B | 5tok/sQ8_0 | 21tok/sQ8_0 |
| DeepSeek R1 0528 Qwen3 8B | 8tok/sQ8_0 | 36tok/sQ8_0 |
| Qwen3 8B | 8tok/sQ8_0 | 36tok/sQ8_0 |
| Phi-4 14B | 5tok/sQ8_0 | 20tok/sQ8_0 |
| Qwen 3.5 4B | 16tok/sQ8_0 | 73tok/sQ8_0 |
| Ministral 3 8B | 8tok/sQ8_0 | 35tok/sQ8_0 |
| Granite 4.2 8B | 7tok/sQ8_0 | 33tok/sQ8_0 |
| Gemma 4 E4B | 9tok/sQ8_0 | 42tok/sQ8_0 |
| LFM2.5 8B-A1B | 32tok/sQ8_0 | 144tok/sQ8_0 |
| Llama 3.1 8B | 10tok/sQ6_K | 46tok/sQ6_K |
| Qwen 3.5 122B-A10B | won't run | offload |
| Nemotron 3 Super 120B-A12B | won't run | offload |
| Qwen3 Coder Next | won't run | offload |
| Mistral Small 4 119B | won't run | offload |
| GLM 4.5 Air | won't run | offload |
| Qwen3 Next 80B-A3B | won't run | offload |
| Nemotron 3.5 Lightning 30B-A3B | won't run | offload |
| Llama 3.3 70B | won't run | offload |
| DeepSeek V4 Flash | won't run | won't run |
| GLM 5.3 Flash | won't run | won't run |
| Qwen 3.8 Flash Next | won't run | won't run |
| MiniMax M2.7 | won't run | won't run |
| gpt-oss 120B | won't run | won't run |
Questions
- Which is better for local AI, the Mac mini M4 32GB or the AMD Radeon AI PRO R9700?
- Choose the AMD Radeon AI PRO R9700 to run bigger models. Choose the AMD Radeon AI PRO R9700 for speed.
- Which can run bigger models, the Mac mini M4 32GB or the AMD Radeon AI PRO R9700?
- At 8k context the AMD Radeon AI PRO R9700 runs 29 of the models we track fully in memory and the Mac mini M4 32GB 26; only the AMD Radeon AI PRO R9700 holds Qwen 3.6 35B-A3B.
- Which is faster for local AI, the Mac mini M4 32GB or the AMD Radeon AI PRO R9700?
- On the models both run fully in memory, the AMD Radeon AI PRO R9700 is about 4.5× as fast as the Mac mini M4 32GB (estimated, median across models).