GETAC S510
Geekbench AI Score
1890
Single Precision Score
500
Half Precision Score
2793
Quantized Score
Geekbench AI 1.0.0 for Windows AVX2
Result Information
| User | jefflin66 |
| Upload Date | August 27 2024 12:41 AM |
| Views | 5 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | CPU |
| Device | Intel(R) Core(TM) Ultra 7 165U |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 11 Pro (64-bit) |
| Model | GETAC S510 |
| Motherboard | GETAC S510 |
| Power Plan | Extreme |
| CPU Information | |
|---|---|
| Name | Intel(R) Core(TM) Ultra 7 165U |
| Topology | 1 Processor, 12 Cores, 14 Threads |
| Identifier | GenuineIntel Family 6 Model 170 Stepping 4 |
| Base Frequency | 2.10 GHz |
| Cluster 1 | 2 Cores |
| Cluster 2 | 10 Cores |
| Memory Information | |
|---|---|
| Size | 64.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
1171
217.8 IPS |
|
|
Image Classification (HP)
|
100% |
124
23.0 IPS |
|
|
Image Classification (Q)
|
99% |
2379
443.6 IPS |
|
|
Image Segmentation (SP)
|
100% |
944
15.3 IPS |
|
|
Image Segmentation (HP)
|
100% |
180
2.92 IPS |
|
|
Image Segmentation (Q)
|
98% |
1783
29.0 IPS |
|
|
Pose Estimation (SP)
|
100% |
3083
3.60 IPS |
|
|
Pose Estimation (HP)
|
100% |
2010
2.35 IPS |
|
|
Pose Estimation (Q)
|
95% |
11812
13.8 IPS |
|
|
Object Detection (SP)
|
100% |
1277
101.3 IPS |
|
|
Object Detection (HP)
|
100% |
165
13.1 IPS |
|
|
Object Detection (Q)
|
88% |
1239
99.4 IPS |
|
|
Face Detection (SP)
|
100% |
2467
29.3 IPS |
|
|
Face Detection (HP)
|
100% |
240
2.86 IPS |
|
|
Face Detection (Q)
|
97% |
2980
35.5 IPS |
|
|
Depth Estimation (SP)
|
100% |
3778
29.1 IPS |
|
|
Depth Estimation (HP)
|
99% |
674
5.19 IPS |
|
|
Depth Estimation (Q)
|
76% |
5909
47.6 IPS |
|
|
Style Transfer (SP)
|
100% |
7840
10.1 IPS |
|
|
Style Transfer (HP)
|
100% |
5091
6.54 IPS |
|
|
Style Transfer (Q)
|
98% |
14182
18.3 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
1468
54.2 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
983
36.3 IPS |
|
|
Image Super-Resolution (Q)
|
99% |
2856
105.8 IPS |
|
|
Text Classification (SP)
|
100% |
982
1.31 KIPS |
|
|
Text Classification (HP)
|
100% |
346
461.7 IPS |
|
|
Text Classification (Q)
|
97% |
435
582.7 IPS |
|
|
Machine Translation (SP)
|
100% |
1274
21.9 IPS |
|
|
Machine Translation (HP)
|
100% |
480
8.27 IPS |
|
|
Machine Translation (Q)
|
60% |
1501
33.5 IPS |
