HP HP ProDesk 600 G5 MT
Geekbench ML Score
2059
ONNX CPU Inference Score
Geekbench ML 0.6.0 Pro for Windows AVX2
Result Information
| Upload Date | August 20 2024 03:22 PM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Microsoft Windows 11 Pro (64-bit) |
| Model | HP HP ProDesk 600 G5 MT |
| Motherboard | HP 8596 |
| Power Plan | Prestazioni elevate |
| CPU Information | |
|---|---|
| Name | Intel Core i7-9700 |
| Topology | 1 Processor, 8 Cores |
| Identifier | GenuineIntel Family 6 Model 158 Stepping 13 |
| Base Frequency | 3.00 GHz |
| Cluster 1 | 8 Cores |
| L1 Instruction Cache | 32.0 KB x 4 |
| L1 Data Cache | 32.0 KB x 4 |
| L2 Cache | 256 KB x 4 |
| L3 Cache | 12.0 MB x 1 |
| Memory Information | |
|---|---|
| Size | 16.00 GB |
| Inference Information | |
|---|---|
| Framework | ONNX |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
1222
228.8 IPS |
|
|
Image Classification (F16)
|
100% |
1275
238.5 IPS |
|
|
Image Classification (I8)
|
97% |
1968
368.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
883
14.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
888
14.8 IPS |
|
|
Image Segmentation (I8)
|
97% |
1604
26.8 IPS |
|
|
Pose Estimation (F32)
|
100% |
4064
4.92 IPS |
|
|
Pose Estimation (F16)
|
100% |
4157
5.03 IPS |
|
|
Pose Estimation (I8)
|
100% |
9034
10.9 IPS |
|
|
Object Detection (F32)
|
100% |
1060
79.2 IPS |
|
|
Object Detection (F16)
|
100% |
1065
79.5 IPS |
|
|
Object Detection (I8)
|
60% |
1122
83.7 IPS |
|
|
Face Detection (F32)
|
100% |
1115
13.3 IPS |
|
|
Face Detection (F16)
|
100% |
1128
13.4 IPS |
|
|
Face Detection (I8)
|
87% |
2224
26.4 IPS |
|
|
Depth Estimation (F32)
|
100% |
4147
32.2 IPS |
|
|
Depth Estimation (F16)
|
100% |
4333
33.6 IPS |
|
|
Depth Estimation (I8)
|
74% |
7575
58.7 IPS |
|
|
Style Transfer (F32)
|
100% |
10895
14.3 IPS |
|
|
Style Transfer (F16)
|
100% |
10757
14.1 IPS |
|
|
Style Transfer (I8)
|
85% |
8754
11.5 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
2044
73.0 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1987
71.0 IPS |
|
|
Image Super-Resolution (I8)
|
97% |
2471
88.2 IPS |
|
|
Text Classification (F32)
|
100% |
1276
1.83 KIPS |
|
|
Text Classification (F16)
|
100% |
1286
1.85 KIPS |
|
|
Text Classification (I8)
|
94% |
321
460.8 IPS |
|
|
Machine Translation (F32)
|
100% |
1128
20.8 IPS |
|
|
Machine Translation (F16)
|
100% |
1109
20.4 IPS |
|
|
Machine Translation (I8)
|
67% |
1343
24.7 IPS |
