MECHREVO YAOSHI Series
Geekbench AI Score
27830
Single Precision Score
47479
Half Precision Score
21612
Quantized Score
Geekbench AI 1.6.0 for Windows AVX2
Result Information
| User | tobbyng |
| Upload Date | January 31 2026 07:12 AM |
| Views | 6 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | DirectML |
| Device | NVIDIA GeForce RTX 5080 Laptop GPU |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 11 (64-bit) |
| Model | MECHREVO YAOSHI Series |
| Motherboard | MECHREVO YAOSHI Series-X6AR55xY |
| CPU Information | |
|---|---|
| Name | Intel Core Ultra 9 275HX |
| Topology | 1 Processor, 24 Cores |
| Identifier | GenuineIntel Family 6 Model 198 Stepping 2 |
| Base Frequency | 2.70 GHz |
| Cluster 1 | 8 Cores |
| Cluster 2 | 16 Cores |
| Memory Information | |
|---|---|
| Size | 32.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
11322
2.11 KIPS |
|
|
Image Classification (HP)
|
100% |
18166
3.38 KIPS |
|
|
Image Classification (Q)
|
100% |
10234
1.90 KIPS |
|
|
Image Segmentation (SP)
|
100% |
20241
328.1 IPS |
|
|
Image Segmentation (HP)
|
100% |
28427
460.8 IPS |
|
|
Image Segmentation (Q)
|
98% |
18117
294.6 IPS |
|
|
Pose Estimation (SP)
|
100% |
160071
186.8 IPS |
|
|
Pose Estimation (HP)
|
100% |
450577
525.7 IPS |
|
|
Pose Estimation (Q)
|
96% |
142560
167.0 IPS |
|
|
Object Detection (SP)
|
100% |
13944
1.11 KIPS |
|
|
Object Detection (HP)
|
100% |
22388
1.78 KIPS |
|
|
Object Detection (Q)
|
86% |
11695
939.6 IPS |
|
|
Face Detection (SP)
|
100% |
39699
471.7 IPS |
|
|
Face Detection (HP)
|
100% |
55809
663.1 IPS |
|
|
Face Detection (Q)
|
97% |
33971
405.0 IPS |
|
|
Depth Estimation (SP)
|
100% |
68754
529.7 IPS |
|
|
Depth Estimation (HP)
|
99% |
130475
1.01 KIPS |
|
|
Depth Estimation (Q)
|
78% |
53648
428.6 IPS |
|
|
Style Transfer (SP)
|
100% |
250539
322.1 IPS |
|
|
Style Transfer (HP)
|
100% |
828680
1.07 KIPS |
|
|
Style Transfer (Q)
|
98% |
215295
277.6 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
39558
1.46 KIPS |
|
|
Image Super-Resolution (HP)
|
100% |
69538
2.57 KIPS |
|
|
Image Super-Resolution (Q)
|
99% |
31615
1.17 KIPS |
|
|
Text Classification (SP)
|
100% |
3609
4.82 KIPS |
|
|
Text Classification (HP)
|
100% |
4628
6.18 KIPS |
|
|
Text Classification (Q)
|
97% |
2153
2.88 KIPS |
|
|
Machine Translation (SP)
|
100% |
5581
96.1 IPS |
|
|
Machine Translation (HP)
|
100% |
5755
99.1 IPS |
|
|
Machine Translation (Q)
|
70% |
2693
50.5 IPS |
