Razer Blade 16 - RZ09-0528
Geekbench AI Score
28099
Single Precision Score
45426
Half Precision Score
22262
Quantized Score
Geekbench AI 1.5.0 for Windows AVX2
Result Information
| User | springkim |
| Upload Date | October 23 2025 09:55 PM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | ONNX |
| Backend | DirectML |
| Device | NVIDIA GeForce RTX 5090 Laptop GPU |
| System Information | |
|---|---|
| Operating System | Microsoft Windows 11 Pro (64-bit) |
| Model | Razer Blade 16 - RZ09-0528 |
| Motherboard | Razer SO6110 |
| CPU Information | |
|---|---|
| Name | AMD Ryzen AI 9 HX 370 |
| Topology | 1 Processor, 12 Cores, 24 Threads |
| Identifier | AuthenticAMD Family 26 Model 36 Stepping 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 4 Cores |
| Cluster 2 | 8 Cores |
| Memory Information | |
|---|---|
| Size | 32.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
10445
1.94 KIPS |
|
|
Image Classification (HP)
|
100% |
14991
2.79 KIPS |
|
|
Image Classification (Q)
|
100% |
9556
1.78 KIPS |
|
|
Image Segmentation (SP)
|
100% |
20509
332.5 IPS |
|
|
Image Segmentation (HP)
|
100% |
28468
461.5 IPS |
|
|
Image Segmentation (Q)
|
98% |
18962
308.3 IPS |
|
|
Pose Estimation (SP)
|
100% |
192433
224.5 IPS |
|
|
Pose Estimation (HP)
|
100% |
548612
640.1 IPS |
|
|
Pose Estimation (Q)
|
96% |
163933
192.0 IPS |
|
|
Object Detection (SP)
|
100% |
14586
1.16 KIPS |
|
|
Object Detection (HP)
|
100% |
21379
1.70 KIPS |
|
|
Object Detection (Q)
|
86% |
12838
1.03 KIPS |
|
|
Face Detection (SP)
|
100% |
42751
508.0 IPS |
|
|
Face Detection (HP)
|
100% |
60023
713.2 IPS |
|
|
Face Detection (Q)
|
97% |
36415
434.2 IPS |
|
|
Depth Estimation (SP)
|
100% |
73022
562.6 IPS |
|
|
Depth Estimation (HP)
|
99% |
141227
1.09 KIPS |
|
|
Depth Estimation (Q)
|
78% |
57322
457.7 IPS |
|
|
Style Transfer (SP)
|
100% |
272791
350.7 IPS |
|
|
Style Transfer (HP)
|
100% |
817018
1.05 KIPS |
|
|
Style Transfer (Q)
|
98% |
238883
308.0 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
39405
1.46 KIPS |
|
|
Image Super-Resolution (HP)
|
100% |
58776
2.17 KIPS |
|
|
Image Super-Resolution (Q)
|
99% |
32207
1.19 KIPS |
|
|
Text Classification (SP)
|
100% |
2774
3.70 KIPS |
|
|
Text Classification (HP)
|
100% |
3348
4.47 KIPS |
|
|
Text Classification (Q)
|
97% |
1823
2.44 KIPS |
|
|
Machine Translation (SP)
|
100% |
5483
94.4 IPS |
|
|
Machine Translation (HP)
|
100% |
5485
94.5 IPS |
|
|
Machine Translation (Q)
|
70% |
2679
50.2 IPS |
