FUJITSU FUTRO S940
Geekbench AI Score
196
Single Precision Score
196
Half Precision Score
653
Quantized Score
Geekbench AI 1.6.0 for Windows x86 (64-bit)
Result Information
| User | Bambooz |
| Upload Date | February 08 2026 01:45 PM |
| Views | 12 |
System Information
| AI Information | |
|---|---|
| Framework | OpenVINO |
| Backend | CPU |
| Device | Intel(R) Pentium(R) Silver J5005 CPU @ 1.50GHz |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 10 Enterprise LTSC (64-bit) |
| Model | FUJITSU FUTRO S940 |
| Motherboard | FUJITSU D3543-A1 |
| Power Plan | Ausbalanciert |
| CPU Information | |
|---|---|
| Name | Intel Pentium Silver J5005 |
| Topology | 1 Processor, 4 Cores |
| Identifier | GenuineIntel Family 6 Model 122 Stepping 1 |
| Base Frequency | 1.50 GHz |
| Cluster 1 | 4 Cores |
| Memory Information | |
|---|---|
| Size | 8.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
203
37.8 IPS |
|
|
Image Classification (HP)
|
100% |
200
37.2 IPS |
|
|
Image Classification (Q)
|
100% |
428
79.6 IPS |
|
|
Image Segmentation (SP)
|
100% |
248
4.02 IPS |
|
|
Image Segmentation (HP)
|
100% |
247
4.00 IPS |
|
|
Image Segmentation (Q)
|
99% |
516
8.40 IPS |
|
|
Pose Estimation (SP)
|
100% |
350
0.41 IPS |
|
|
Pose Estimation (HP)
|
100% |
357
0.42 IPS |
|
|
Pose Estimation (Q)
|
94% |
875
1.03 IPS |
|
|
Object Detection (SP)
|
100% |
189
15.0 IPS |
|
|
Object Detection (HP)
|
100% |
191
15.2 IPS |
|
|
Object Detection (Q)
|
87% |
433
34.7 IPS |
|
|
Face Detection (SP)
|
100% |
621
7.38 IPS |
|
|
Face Detection (HP)
|
100% |
626
7.44 IPS |
|
|
Face Detection (Q)
|
97% |
1233
14.7 IPS |
|
|
Depth Estimation (SP)
|
100% |
506
3.90 IPS |
|
|
Depth Estimation (HP)
|
99% |
505
3.89 IPS |
|
|
Depth Estimation (Q)
|
81% |
1093
8.64 IPS |
|
|
Style Transfer (SP)
|
100% |
6
0.01 IPS |
|
|
Style Transfer (HP)
|
100% |
6
0.01 IPS |
|
|
Style Transfer (Q)
|
98% |
2780
3.59 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
233
8.60 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
233
8.61 IPS |
|
|
Image Super-Resolution (Q)
|
98% |
571
21.1 IPS |
|
|
Text Classification (SP)
|
100% |
207
276.4 IPS |
|
|
Text Classification (HP)
|
100% |
206
274.3 IPS |
|
|
Text Classification (Q)
|
91% |
286
384.2 IPS |
|
|
Machine Translation (SP)
|
100% |
279
4.81 IPS |
|
|
Machine Translation (HP)
|
100% |
279
4.81 IPS |
|
|
Machine Translation (Q)
|
100% |
278
4.79 IPS |
