ASRock NUC BOX-255H vs Lenovo ThinkCentre Neo 50q
Side-by-side specification comparison with AI model-fit analysis. Data sourced from manufacturer specs, verified 2026-07-21.
|
ASRock NUC BOX-255H (Core Ultra 7 255H) ASRock |
Lenovo ThinkCentre Neo 50q (Core Ultra 7 258V) Lenovo |
|
|---|---|---|
| Price | $699MSRP | $949MSRP |
| AI Framework | OpenVINO, DirectML | OpenVINO, DirectML, IPEX |
| CPU Cores | 16 | 8 |
| Ethernet | 2.5GbE | 2.5GbE |
| GPU Compute Units | 8 | 8 |
| GPU | Intel Arc | Intel Arc |
| Memory Bandwidth (GB/s) | 102.4 | 136.5 |
| Memory Bus Width | 128-bit | 128-bit |
| Memory (GB) | 96 | 32 |
| Memory Type | DDR5 6400 SODIMM | LPDDR5X 8533 |
| Memory Upgradeable | Yes (DDR5 SODIMM) | No (soldered LPDDR5X) |
| NPU TOPS | 13 | 47 |
| OCuLink / PCIe | No | No |
| OS Support | Windows 11, Linux | Windows 11, Linux |
| Platform | Intel Arrow Lake-H | Intel Lunar Lake |
| Storage Type | NVMe Gen4 SSD | NVMe Gen4 SSD |
| TDP (W) | 65 | 30 |
| Buy | Check Price → | Check Price → |
| Details | View → | View → |
Model Fit Analysis
ASRock NUC BOX-255H (Core Ultra 7 255H)
Max Q4: 70B class models (85B params)
Usable AI memory: 46GB · Method: intel_igpu_bios_limited 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
✓
Lenovo ThinkCentre Neo 50q (Core Ultra 7 258V)
Max Q4: 14B class models (21B params)
Usable AI memory: 14GB · Method: intel_igpu_bios_limited 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