HONOR FMB-P
Geekbench AI Score
4902
Single Precision Score
1914
Half Precision Score
7510
Quantized Score
Geekbench AI 1.3.0 for Windows AVX2
Result Information
| Upload Date | June 17 2025 06:21 AM |
| Views | 4 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | CPU |
| Device | Intel(R) Core(TM) Ultra 9 285H |
| System Information | |
|---|---|
| Operating System | Windows 11 (64-bit) |
| Model | HONOR FMB-P |
| Motherboard | HONOR FMB-P-PCB |
| CPU Information | |
|---|---|
| Name | Intel(R) Core(TM) Ultra 9 285H |
| Topology | 1 Processor, 16 Cores |
| Identifier | GenuineIntel Family 6 Model 197 Stepping 2 |
| Base Frequency | 2.90 GHz |
| Cluster 1 | 6 Cores |
| Cluster 2 | 10 Cores |
| Memory Information | |
|---|---|
| Size | 32.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
3739
695.4 IPS |
|
|
Image Classification (HP)
|
100% |
663
123.3 IPS |
|
|
Image Classification (Q)
|
99% |
5155
961.5 IPS |
|
|
Image Segmentation (SP)
|
100% |
2377
38.5 IPS |
|
|
Image Segmentation (HP)
|
100% |
1320
21.4 IPS |
|
|
Image Segmentation (Q)
|
98% |
3597
58.5 IPS |
|
|
Pose Estimation (SP)
|
100% |
8419
9.82 IPS |
|
|
Pose Estimation (HP)
|
100% |
5959
6.95 IPS |
|
|
Pose Estimation (Q)
|
94% |
28185
33.0 IPS |
|
|
Object Detection (SP)
|
100% |
3580
284.0 IPS |
|
|
Object Detection (HP)
|
100% |
852
67.6 IPS |
|
|
Object Detection (Q)
|
86% |
8224
660.9 IPS |
|
|
Face Detection (SP)
|
100% |
9507
113.0 IPS |
|
|
Face Detection (HP)
|
100% |
2118
25.2 IPS |
|
|
Face Detection (Q)
|
97% |
10817
129.0 IPS |
|
|
Depth Estimation (SP)
|
100% |
10127
78.0 IPS |
|
|
Depth Estimation (HP)
|
99% |
2813
21.7 IPS |
|
|
Depth Estimation (Q)
|
78% |
17581
140.3 IPS |
|
|
Style Transfer (SP)
|
100% |
19838
25.5 IPS |
|
|
Style Transfer (HP)
|
100% |
13883
17.8 IPS |
|
|
Style Transfer (Q)
|
98% |
25548
32.9 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
3571
131.9 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
2539
93.7 IPS |
|
|
Image Super-Resolution (Q)
|
99% |
3656
135.4 IPS |
|
|
Text Classification (SP)
|
100% |
1658
2.21 KIPS |
|
|
Text Classification (HP)
|
100% |
719
960.2 IPS |
|
|
Text Classification (Q)
|
97% |
1930
2.59 KIPS |
|
|
Machine Translation (SP)
|
100% |
2645
45.6 IPS |
|
|
Machine Translation (HP)
|
100% |
985
17.0 IPS |
|
|
Machine Translation (Q)
|
65% |
3875
77.9 IPS |
