Razer Blade 16 - RZ09-0528
Geekbench AI Score
24692
Single Precision Score
40514
Half Precision Score
19491
Quantized Score
Geekbench AI 1.5.0 for Windows AVX2
Result Information
| User | springkim |
| Upload Date | October 28 2025 02:15 PM |
| Views | 7 |
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% |
10752
2.00 KIPS |
|
|
Image Classification (HP)
|
100% |
13128
2.44 KIPS |
|
|
Image Classification (Q)
|
100% |
8467
1.57 KIPS |
|
|
Image Segmentation (SP)
|
100% |
19153
310.5 IPS |
|
|
Image Segmentation (HP)
|
100% |
26072
422.6 IPS |
|
|
Image Segmentation (Q)
|
98% |
15972
259.7 IPS |
|
|
Pose Estimation (SP)
|
100% |
155477
181.4 IPS |
|
|
Pose Estimation (HP)
|
100% |
543705
634.4 IPS |
|
|
Pose Estimation (Q)
|
96% |
137601
161.2 IPS |
|
|
Object Detection (SP)
|
100% |
13227
1.05 KIPS |
|
|
Object Detection (HP)
|
100% |
19350
1.53 KIPS |
|
|
Object Detection (Q)
|
86% |
11486
922.8 IPS |
|
|
Face Detection (SP)
|
100% |
34241
406.9 IPS |
|
|
Face Detection (HP)
|
100% |
47955
569.8 IPS |
|
|
Face Detection (Q)
|
97% |
29343
349.9 IPS |
|
|
Depth Estimation (SP)
|
100% |
63342
488.0 IPS |
|
|
Depth Estimation (HP)
|
99% |
121473
935.9 IPS |
|
|
Depth Estimation (Q)
|
78% |
51063
407.8 IPS |
|
|
Style Transfer (SP)
|
100% |
234052
300.9 IPS |
|
|
Style Transfer (HP)
|
100% |
747139
960.5 IPS |
|
|
Style Transfer (Q)
|
98% |
219775
283.4 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
35321
1.30 KIPS |
|
|
Image Super-Resolution (HP)
|
100% |
57795
2.13 KIPS |
|
|
Image Super-Resolution (Q)
|
99% |
28646
1.06 KIPS |
|
|
Text Classification (SP)
|
100% |
2330
3.11 KIPS |
|
|
Text Classification (HP)
|
100% |
2736
3.65 KIPS |
|
|
Text Classification (Q)
|
97% |
1648
2.21 KIPS |
|
|
Machine Translation (SP)
|
100% |
4763
82.0 IPS |
|
|
Machine Translation (HP)
|
100% |
4808
82.8 IPS |
|
|
Machine Translation (Q)
|
70% |
2382
44.7 IPS |
