Acer Swift SFG16-72
Geekbench AI Score
2742
Single Precision Score
7917
Half Precision Score
11847
Quantized Score
Geekbench AI 1.2.0 for Windows AVX2
Result Information
| Upload Date | December 28 2024 01:55 PM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | OpenVINO |
| Backend | NPU |
| Device | Intel(R) AI Boost |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 11 (64-bit) |
| Model | Acer Swift SFG16-72 |
| Motherboard | MTL Pipefish_MTH |
| Power Plan | Acer |
| CPU Information | |
|---|---|
| Name | Intel Core Ultra 7 155H |
| Topology | 1 Processor, 16 Cores, 22 Threads |
| Identifier | GenuineIntel Family 6 Model 170 Stepping 4 |
| Base Frequency | 1.40 GHz |
| Cluster 1 | 6 Cores |
| Cluster 2 | 10 Cores |
| Memory Information | |
|---|---|
| Size | 16.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
1515
281.7 IPS |
|
|
Image Classification (HP)
|
100% |
5362
997.2 IPS |
|
|
Image Classification (Q)
|
100% |
7355
1.37 KIPS |
|
|
Image Segmentation (SP)
|
100% |
2392
38.8 IPS |
|
|
Image Segmentation (HP)
|
100% |
2352
38.1 IPS |
|
|
Image Segmentation (Q)
|
99% |
3107
50.4 IPS |
|
|
Pose Estimation (SP)
|
100% |
3723
4.34 IPS |
|
|
Pose Estimation (HP)
|
100% |
29318
34.2 IPS |
|
|
Pose Estimation (Q)
|
96% |
62511
73.2 IPS |
|
|
Object Detection (SP)
|
100% |
1488
118.0 IPS |
|
|
Object Detection (HP)
|
100% |
4859
385.4 IPS |
|
|
Object Detection (Q)
|
87% |
7454
598.2 IPS |
|
|
Face Detection (SP)
|
100% |
4643
55.2 IPS |
|
|
Face Detection (HP)
|
100% |
12692
150.8 IPS |
|
|
Face Detection (Q)
|
100% |
20713
246.1 IPS |
|
|
Depth Estimation (SP)
|
100% |
3903
30.1 IPS |
|
|
Depth Estimation (HP)
|
99% |
15908
122.6 IPS |
|
|
Depth Estimation (Q)
|
88% |
29623
230.5 IPS |
|
|
Style Transfer (SP)
|
100% |
9944
12.8 IPS |
|
|
Style Transfer (HP)
|
100% |
45007
57.9 IPS |
|
|
Style Transfer (Q)
|
98% |
80083
103.3 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
2038
75.3 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
11810
436.1 IPS |
|
|
Image Super-Resolution (Q)
|
99% |
20952
775.9 IPS |
|
|
Text Classification (SP)
|
100% |
1674
2.23 KIPS |
|
|
Text Classification (HP)
|
100% |
1908
2.55 KIPS |
|
|
Text Classification (Q)
|
92% |
1873
2.52 KIPS |
|
|
Machine Translation (SP)
|
100% |
1947
33.5 IPS |
|
|
Machine Translation (HP)
|
100% |
2630
45.3 IPS |
|
|
Machine Translation (Q)
|
100% |
2654
45.7 IPS |
