Google nirva
Geekbench AI Score
128
Single Precision Score
190
Half Precision Score
148
Quantized Score
Geekbench AI 1.5.0 for Android AArch64
Result Information
| Upload Date | November 04 2025 07:08 AM |
| Views | 3 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | placeholder |
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Google nirva |
| Model ID | Google nirva |
| Motherboard | telithn50 |
| CPU Information | |
|---|---|
| Name | placeholder |
| Topology | 1 Processor, 2 Cores |
| Identifier | ARM implementer 78 architecture 8 variant 2 part 0 revision 1 |
| Base Frequency | 0 MHz |
| Cluster 1 | 2 Cores |
| Memory Information | |
|---|---|
| Size | 6.83 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
83
15.5 IPS |
|
|
Image Classification (HP)
|
100% |
132
24.5 IPS |
|
|
Image Classification (Q)
|
96% |
129
24.1 IPS |
|
|
Image Segmentation (SP)
|
100% |
105
1.70 IPS |
|
|
Image Segmentation (HP)
|
100% |
184
2.99 IPS |
|
|
Image Segmentation (Q)
|
98% |
167
2.71 IPS |
|
|
Pose Estimation (SP)
|
100% |
105
0.12 IPS |
|
|
Pose Estimation (HP)
|
100% |
197
0.23 IPS |
|
|
Pose Estimation (Q)
|
94% |
197
0.23 IPS |
|
|
Object Detection (SP)
|
100% |
67
5.31 IPS |
|
|
Object Detection (HP)
|
100% |
103
8.19 IPS |
|
|
Object Detection (Q)
|
86% |
102
8.21 IPS |
|
|
Face Detection (SP)
|
100% |
230
2.74 IPS |
|
|
Face Detection (HP)
|
100% |
361
4.29 IPS |
|
|
Face Detection (Q)
|
96% |
341
4.07 IPS |
|
|
Depth Estimation (SP)
|
100% |
163
1.25 IPS |
|
|
Depth Estimation (HP)
|
99% |
267
2.05 IPS |
|
|
Depth Estimation (Q)
|
63% |
224
2.09 IPS |
|
|
Style Transfer (SP)
|
100% |
354
0.46 IPS |
|
|
Style Transfer (HP)
|
100% |
595
0.76 IPS |
|
|
Style Transfer (Q)
|
98% |
597
0.77 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
78
2.88 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
124
4.58 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
127
4.69 IPS |
|
|
Text Classification (SP)
|
100% |
101
134.7 IPS |
|
|
Text Classification (HP)
|
100% |
101
135.3 IPS |
|
|
Text Classification (Q)
|
91% |
42
56.4 IPS |
|
|
Machine Translation (SP)
|
100% |
195
3.36 IPS |
|
|
Machine Translation (HP)
|
100% |
175
3.01 IPS |
|
|
Machine Translation (Q)
|
57% |
48
1.18 IPS |
