MacBook Pro (16-inch, 2023)
Geekbench ML Score
9409
Core ML Neural Engine Inference Score
Geekbench ML 0.6.0 for macOS AArch64
Result Information
| Upload Date | May 08 2024 07:41 AM |
| Views | 4 |
System Information
| System Information | |
|---|---|
| Operating System | macOS 14.5 (Build 23F5074a) |
| Model | MacBook Pro (16-inch, 2023) |
| Model ID | Mac14,6 |
| Motherboard | Mac14,6 |
| CPU Information | |
|---|---|
| Name | Apple M2 Max |
| Topology | 1 Processor, 12 Cores |
| Identifier | Apple M2 Max |
| Base Frequency | 3.67 GHz |
| Cluster 1 | 8 Cores |
| Cluster 2 | 4 Cores |
| L1 Instruction Cache | 128 KB x 1 |
| L1 Data Cache | 64.0 KB x 1 |
| L2 Cache | 4.00 MB x 1 |
| Memory Information | |
|---|---|
| Size | 32.00 GB |
| Inference Information | |
|---|---|
| Framework | Core ML |
| Backend | Neural Engine |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
5967
1.12 KIPS |
|
|
Image Classification (F16)
|
100% |
7128
1.33 KIPS |
|
|
Image Classification (I8)
|
99% |
7841
1.47 KIPS |
|
|
Image Segmentation (F32)
|
100% |
2286
38.2 IPS |
|
|
Image Segmentation (F16)
|
100% |
1917
32.0 IPS |
|
|
Image Segmentation (I8)
|
100% |
1929
32.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
73962
89.6 IPS |
|
|
Pose Estimation (F16)
|
100% |
78203
94.7 IPS |
|
|
Pose Estimation (I8)
|
100% |
78753
95.4 IPS |
|
|
Object Detection (F32)
|
100% |
3671
274.1 IPS |
|
|
Object Detection (F16)
|
100% |
4265
318.4 IPS |
|
|
Object Detection (I8)
|
97% |
3654
272.8 IPS |
|
|
Face Detection (F32)
|
100% |
20669
245.8 IPS |
|
|
Face Detection (F16)
|
97% |
11317
134.6 IPS |
|
|
Face Detection (I8)
|
96% |
11285
134.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
31157
241.6 IPS |
|
|
Depth Estimation (F16)
|
100% |
37512
290.9 IPS |
|
|
Depth Estimation (I8)
|
37% |
36563
283.5 IPS |
|
|
Style Transfer (F32)
|
100% |
98553
129.6 IPS |
|
|
Style Transfer (F16)
|
100% |
128007
168.4 IPS |
|
|
Style Transfer (I8)
|
100% |
133254
175.3 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
12442
444.3 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
17232
615.4 IPS |
|
|
Image Super-Resolution (I8)
|
100% |
16425
586.6 IPS |
|
|
Text Classification (F32)
|
100% |
450
647.3 IPS |
|
|
Text Classification (F16)
|
100% |
2495
3.59 KIPS |
|
|
Text Classification (I8)
|
96% |
2375
3.41 KIPS |
|
|
Machine Translation (F32)
|
100% |
950
17.5 IPS |
|
|
Machine Translation (F16)
|
100% |
1488
27.4 IPS |
|
|
Machine Translation (I8)
|
99% |
872
16.1 IPS |
