HP HP ProDesk 600 G5 MT
Geekbench ML Score
814
ONNX DirectML Inference Score
Geekbench ML 0.6.0 Pro for Windows AVX2
Result Information
| Upload Date | August 20 2024 01:46 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 | DirectML |
| Device | Intel(R) UHD Graphics 630 |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
359
67.2 IPS |
|
|
Image Classification (F16)
|
100% |
408
76.4 IPS |
|
|
Image Classification (I8)
|
100% |
358
67.0 IPS |
|
|
Image Segmentation (F32)
|
100% |
578
9.65 IPS |
|
|
Image Segmentation (F16)
|
100% |
522
8.72 IPS |
|
|
Image Segmentation (I8)
|
98% |
483
8.06 IPS |
|
|
Pose Estimation (F32)
|
100% |
2312
2.80 IPS |
|
|
Pose Estimation (F16)
|
100% |
2305
2.79 IPS |
|
|
Pose Estimation (I8)
|
100% |
2077
2.51 IPS |
|
|
Object Detection (F32)
|
100% |
460
34.3 IPS |
|
|
Object Detection (F16)
|
100% |
461
34.4 IPS |
|
|
Object Detection (I8)
|
54% |
425
31.7 IPS |
|
|
Face Detection (F32)
|
100% |
771
9.17 IPS |
|
|
Face Detection (F16)
|
100% |
782
9.30 IPS |
|
|
Face Detection (I8)
|
89% |
637
7.57 IPS |
|
|
Depth Estimation (F32)
|
100% |
1718
13.3 IPS |
|
|
Depth Estimation (F16)
|
100% |
1763
13.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
1543
12.0 IPS |
|
|
Style Transfer (F32)
|
100% |
6112
8.04 IPS |
|
|
Style Transfer (F16)
|
100% |
6122
8.05 IPS |
|
|
Style Transfer (I8)
|
98% |
5146
6.77 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1242
44.4 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
148
5.29 IPS |
|
|
Image Super-Resolution (I8)
|
99% |
969
34.6 IPS |
|
|
Text Classification (F32)
|
100% |
482
692.8 IPS |
|
|
Text Classification (F16)
|
100% |
500
717.9 IPS |
|
|
Text Classification (I8)
|
98% |
301
432.9 IPS |
|
|
Machine Translation (F32)
|
100% |
457
8.41 IPS |
|
|
Machine Translation (F16)
|
100% |
457
8.40 IPS |
|
|
Machine Translation (I8)
|
70% |
304
5.59 IPS |
