FUJITSU ESPRIMO P2760
Geekbench AI Score
208
Single Precision Score
162
Half Precision Score
266
Quantized Score
Geekbench AI 1.3.0 for Windows x86 (64-bit)
Result Information
| Upload Date | April 09 2025 01:29 PM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | CPU |
| Device | Intel Core i5-661 |
| System Information | |
|---|---|
| Operating System | Windows 10 Pro (64-bit) |
| Model | FUJITSU ESPRIMO P2760 |
| Motherboard | MICRO-STAR INTERNATIONAL CO.,LTD D2415 |
| CPU Information | |
|---|---|
| Name | Intel Core i5-661 |
| Topology | 1 Processor, 2 Cores, 4 Threads |
| Identifier | GenuineIntel Family 6 Model 37 Stepping 5 |
| Base Frequency | 3.32 GHz |
| Cluster 1 | 2 Cores |
| Memory Information | |
|---|---|
| Size | 4.00 GB |
| Type | SDRAM |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
159
29.6 IPS |
|
|
Image Classification (HP)
|
100% |
94
17.5 IPS |
|
|
Image Classification (Q)
|
96% |
256
47.7 IPS |
|
|
Image Segmentation (SP)
|
100% |
137
2.22 IPS |
|
|
Image Segmentation (HP)
|
100% |
123
2.00 IPS |
|
|
Image Segmentation (Q)
|
97% |
224
3.65 IPS |
|
|
Pose Estimation (SP)
|
100% |
281
0.33 IPS |
|
|
Pose Estimation (HP)
|
100% |
267
0.31 IPS |
|
|
Pose Estimation (Q)
|
86% |
460
0.54 IPS |
|
|
Object Detection (SP)
|
100% |
138
11.0 IPS |
|
|
Object Detection (HP)
|
100% |
100
7.96 IPS |
|
|
Object Detection (Q)
|
58% |
181
19.7 IPS |
|
|
Face Detection (SP)
|
100% |
410
4.87 IPS |
|
|
Face Detection (HP)
|
100% |
242
2.88 IPS |
|
|
Face Detection (Q)
|
96% |
538
6.42 IPS |
|
|
Depth Estimation (SP)
|
100% |
386
2.97 IPS |
|
|
Depth Estimation (HP)
|
99% |
252
1.94 IPS |
|
|
Depth Estimation (Q)
|
78% |
339
2.70 IPS |
|
|
Style Transfer (SP)
|
100% |
717
0.92 IPS |
|
|
Style Transfer (HP)
|
100% |
719
0.92 IPS |
|
|
Style Transfer (Q)
|
87% |
912
1.19 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
152
5.63 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
147
5.44 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
143
5.29 IPS |
|
|
Text Classification (SP)
|
100% |
60
79.6 IPS |
|
|
Text Classification (HP)
|
100% |
67
89.0 IPS |
|
|
Text Classification (Q)
|
97% |
85
114.4 IPS |
|
|
Machine Translation (SP)
|
100% |
178
3.07 IPS |
|
|
Machine Translation (HP)
|
100% |
97
1.68 IPS |
|
|
Machine Translation (Q)
|
68% |
188
3.63 IPS |
