HONOR PTP-N49
Geekbench AI Score
688
Single Precision Score
690
Half Precision Score
1625
Quantized Score
Geekbench AI 1.5.0 for Android AArch64
Result Information
| User | slavatsarev |
| Upload Date | November 12 2025 03:35 PM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Qualcomm SM8750 |
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | HONOR PTP-N49 |
| Model ID | HONOR PTP-N49 |
| Motherboard | PTP |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | Qualcomm SM8750 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 81 architecture 8 variant 3 part 1 revision 4 |
| Base Frequency | 3.53 GHz |
| Cluster 1 | 6 Cores @ 3.53 GHz |
| Cluster 2 | 2 Cores @ 4.32 GHz |
| Memory Information | |
|---|---|
| Size | 10.94 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
410
76.3 IPS |
|
|
Image Classification (HP)
|
100% |
420
78.1 IPS |
|
|
Image Classification (Q)
|
99% |
1152
214.9 IPS |
|
|
Image Segmentation (SP)
|
100% |
533
8.63 IPS |
|
|
Image Segmentation (HP)
|
100% |
536
8.69 IPS |
|
|
Image Segmentation (Q)
|
98% |
1081
17.6 IPS |
|
|
Pose Estimation (SP)
|
100% |
706
0.82 IPS |
|
|
Pose Estimation (HP)
|
100% |
699
0.82 IPS |
|
|
Pose Estimation (Q)
|
98% |
2785
3.26 IPS |
|
|
Object Detection (SP)
|
100% |
394
31.2 IPS |
|
|
Object Detection (HP)
|
100% |
395
31.3 IPS |
|
|
Object Detection (Q)
|
87% |
1209
97.0 IPS |
|
|
Face Detection (SP)
|
100% |
998
11.9 IPS |
|
|
Face Detection (HP)
|
100% |
1017
12.1 IPS |
|
|
Face Detection (Q)
|
97% |
2412
28.8 IPS |
|
|
Depth Estimation (SP)
|
100% |
860
6.63 IPS |
|
|
Depth Estimation (HP)
|
99% |
858
6.61 IPS |
|
|
Depth Estimation (Q)
|
64% |
2131
19.3 IPS |
|
|
Style Transfer (SP)
|
100% |
1481
1.90 IPS |
|
|
Style Transfer (HP)
|
100% |
1482
1.90 IPS |
|
|
Style Transfer (Q)
|
98% |
4157
5.36 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
468
17.3 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
472
17.4 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
1742
64.5 IPS |
|
|
Text Classification (SP)
|
100% |
659
879.0 IPS |
|
|
Text Classification (HP)
|
100% |
648
864.9 IPS |
|
|
Text Classification (Q)
|
91% |
1072
1.44 KIPS |
|
|
Machine Translation (SP)
|
100% |
1007
17.3 IPS |
|
|
Machine Translation (HP)
|
100% |
999
17.2 IPS |
|
|
Machine Translation (Q)
|
57% |
768
18.8 IPS |
