GMKtec EVO-X2 (Ryzen AI Max+ 395) vs Mac mini M4 Pro for Local AI: Local AI Performance
Side-by-side specification comparison with AI model-fit analysis. Data sourced from manufacturer specs, verified 2026-07-21.
|
GMKtec EVO-X2 (Ryzen AI Max+ 395) GMKtec |
Mac mini M4 Pro Apple |
|
|---|---|---|
| Price | $2,199MSRP | $1,599MSRP |
| AI Framework | ROCm, Vulkan, DirectML | MLX, CoreML, llama.cpp |
| CPU Cores | 16 | 14 |
| Ethernet | 2.5GbE | 10GbE (optional) |
| GPU Compute Units | 40 | 20 |
| GPU | Radeon 8060S | Apple M4 Pro GPU |
| Memory Bandwidth (GB/s) | 256 | 273 |
| Memory Bus Width | 256-bit | 192-bit |
| Memory (GB) | 128 | 48 |
| Memory Type | LPDDR5X 8000 | Unified LPDDR5X |
| Memory Upgradeable | No (soldered LPDDR5X) | No (unified memory) |
| NPU TOPS | 50 | 38 |
| OCuLink / PCIe | Yes (OCuLink) | No |
| OS Support | Windows 11, Linux | macOS |
| Platform | AMD Strix Halo | Apple M4 Pro |
| Storage Type | NVMe Gen4 SSD | NVMe SSD |
| TDP (W) | 120 | 70 |
| Buy | Check Price → | Check Price → |
| Details | View → | View → |
Model Fit Analysis
GMKtec EVO-X2 (Ryzen AI Max+ 395)
Max Q4: 70B class models (169.4B params)
Usable AI memory: 88.2GB · Method: unified_memory_desktop_workload Conservative
Llama 3.1 8B
✓
Llama 3.1 70B
✓
Llama 3.2 1B
✓
Llama 3.2 3B
✓
Qwen 2.5 7B
✓
Qwen 2.5 14B
✓
Qwen 2.5 32B
✓
Qwen 2.5 72B
✓
Mistral 7B
✓
Mixtral 8x7B
✓
DeepSeek R1 7B
✓
DeepSeek R1 32B
✓
DeepSeek R1 70B
✓
Phi-3 Medium 14B
✓
Gemma 2 9B
✓
Gemma 2 27B
✓
Stable Diffusion XL
✓
Flux.1 Dev
✓
Flux.1 Schnell
✓
Mac mini M4 Pro
Max Q4: 34B class models (56B params)
Usable AI memory: 31.5GB · Method: unified_memory_desktop_workload Conservative
Llama 3.1 8B
✓
Llama 3.1 70B
✗
Llama 3.2 1B
✓
Llama 3.2 3B
✓
Qwen 2.5 7B
✓
Qwen 2.5 14B
✓
Qwen 2.5 32B
✓
Qwen 2.5 72B
✗
Mistral 7B
✓
Mixtral 8x7B
✓
DeepSeek R1 7B
✓
DeepSeek R1 32B
✓
DeepSeek R1 70B
✗
Phi-3 Medium 14B
✓
Gemma 2 9B
✓
Gemma 2 27B
✓
Stable Diffusion XL
✓
Flux.1 Dev
✓
Flux.1 Schnell
✓
Specifications sourced from manufacturer datasheets. Prices are MSRP, verified 2026-07-21. Model-fit uses conservative estimates — actual performance varies. Methodology