GIGABYTE P27GV2
Geekbench AI Score
2077
Single Precision Score
2036
Half Precision Score
1559
Quantized Score
Geekbench AI 1.2.0 for Windows AVX2
Result Information
| Upload Date | December 25 2024 06:35 AM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | DirectML |
| Device | NVIDIA GeForce GTX 860M |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 11 (64-bit) |
| Model | GIGABYTE P27GV2 |
| Motherboard | GIGABYTE P27GV2 |
| CPU Information | |
|---|---|
| Name | Intel Core i7-4710MQ |
| Topology | 1 Processor, 4 Cores, 8 Threads |
| Identifier | GenuineIntel Family 6 Model 60 Stepping 3 |
| Base Frequency | 2.50 GHz |
| Cluster 1 | 4 Cores |
| Memory Information | |
|---|---|
| Size | 24.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
831
154.6 IPS |
|
|
Image Classification (HP)
|
100% |
817
151.9 IPS |
|
|
Image Classification (Q)
|
99% |
704
131.3 IPS |
|
|
Image Segmentation (SP)
|
100% |
1088
17.6 IPS |
|
|
Image Segmentation (HP)
|
100% |
1093
17.7 IPS |
|
|
Image Segmentation (Q)
|
98% |
897
14.6 IPS |
|
|
Pose Estimation (SP)
|
100% |
7148
8.34 IPS |
|
|
Pose Estimation (HP)
|
100% |
6672
7.79 IPS |
|
|
Pose Estimation (Q)
|
96% |
6272
7.35 IPS |
|
|
Object Detection (SP)
|
100% |
1025
81.3 IPS |
|
|
Object Detection (HP)
|
100% |
1014
80.5 IPS |
|
|
Object Detection (Q)
|
83% |
853
68.9 IPS |
|
|
Face Detection (SP)
|
100% |
1768
21.0 IPS |
|
|
Face Detection (HP)
|
100% |
1792
21.3 IPS |
|
|
Face Detection (Q)
|
97% |
1450
17.3 IPS |
|
|
Depth Estimation (SP)
|
100% |
3815
29.4 IPS |
|
|
Depth Estimation (HP)
|
99% |
3719
28.7 IPS |
|
|
Depth Estimation (Q)
|
77% |
3052
24.4 IPS |
|
|
Style Transfer (SP)
|
100% |
11521
14.8 IPS |
|
|
Style Transfer (HP)
|
100% |
11292
14.5 IPS |
|
|
Style Transfer (Q)
|
98% |
9868
12.7 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
3121
115.3 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
3014
111.3 IPS |
|
|
Image Super-Resolution (Q)
|
99% |
2228
82.5 IPS |
|
|
Text Classification (SP)
|
100% |
760
1.01 KIPS |
|
|
Text Classification (HP)
|
100% |
766
1.02 KIPS |
|
|
Text Classification (Q)
|
97% |
452
606.1 IPS |
|
|
Machine Translation (SP)
|
100% |
1227
21.1 IPS |
|
|
Machine Translation (HP)
|
96% |
1168
20.2 IPS |
|
|
Machine Translation (Q)
|
70% |
574
10.8 IPS |
