OnePlus CPH2709
Geekbench AI Score
275
Single Precision Score
247
Half Precision Score
563
Quantized Score
Geekbench AI 1.4.0 for Android AArch64
Result Information
| Upload Date | August 28 2025 08:18 AM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | ARM ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | OnePlus CPH2709 |
| Model ID | OnePlus CPH2709 |
| Motherboard | pineapple |
| Governor | uag |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3458 revision 1 |
| Base Frequency | 2.02 GHz |
| Cluster 1 | 3 Cores @ 2.02 GHz |
| Cluster 2 | 4 Cores @ 2.80 GHz |
| Cluster 3 | 1 Core @ 3.01 GHz |
| Memory Information | |
|---|---|
| Size | 7.14 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
284
52.8 IPS |
|
|
Image Classification (HP)
|
100% |
286
53.2 IPS |
|
|
Image Classification (Q)
|
99% |
733
136.8 IPS |
|
|
Image Segmentation (SP)
|
100% |
369
5.99 IPS |
|
|
Image Segmentation (HP)
|
100% |
375
6.07 IPS |
|
|
Image Segmentation (Q)
|
98% |
769
12.5 IPS |
|
|
Pose Estimation (SP)
|
100% |
506
0.59 IPS |
|
|
Pose Estimation (HP)
|
100% |
210
0.25 IPS |
|
|
Pose Estimation (Q)
|
98% |
796
0.93 IPS |
|
|
Object Detection (SP)
|
100% |
116
9.20 IPS |
|
|
Object Detection (HP)
|
100% |
116
9.22 IPS |
|
|
Object Detection (Q)
|
87% |
326
26.2 IPS |
|
|
Face Detection (SP)
|
100% |
336
3.99 IPS |
|
|
Face Detection (HP)
|
100% |
336
3.99 IPS |
|
|
Face Detection (Q)
|
97% |
686
8.18 IPS |
|
|
Depth Estimation (SP)
|
100% |
282
2.17 IPS |
|
|
Depth Estimation (HP)
|
99% |
285
2.19 IPS |
|
|
Depth Estimation (Q)
|
64% |
643
5.84 IPS |
|
|
Style Transfer (SP)
|
100% |
649
0.83 IPS |
|
|
Style Transfer (HP)
|
100% |
516
0.66 IPS |
|
|
Style Transfer (Q)
|
98% |
1617
2.08 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
134
4.93 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
134
4.95 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
455
16.9 IPS |
|
|
Text Classification (SP)
|
100% |
165
220.6 IPS |
|
|
Text Classification (HP)
|
100% |
165
220.3 IPS |
|
|
Text Classification (Q)
|
91% |
277
372.7 IPS |
|
|
Machine Translation (SP)
|
100% |
303
5.22 IPS |
|
|
Machine Translation (HP)
|
100% |
302
5.20 IPS |
|
|
Machine Translation (Q)
|
57% |
244
5.97 IPS |
