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# Training performance report on NVIDIA RTX3090
[GEFORCE RTX 3090](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090/)
The GeForce RTX™ 3090 is a big ferocious GPU (BFGPU) with TITAN class performance.
Besides, we can also use GeForce RTX™ 3090 to train deep learning models by its FP16 and TF32 supports.
## Test Server Spec
| Key | Value |
|--------------|---------------------------------------------------|
| CPU | 2 x Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz |
| Memory | 384GB |
| GPU | 8 x GeForce RTX™ 3090 |
| OS | Ubuntu 18.04.4 LTS |
| Installation | CUDA 11.1, |
| Installation | Python 3.7.3 |
| Installation | PyTorch 1.9.0 (pip) |
## Experiments on arcface_torch
We report training speed in following table, please also note that:
1. The training dataset is SyntheticDataset.
2. Embedding-size are all set to 512.
### 1. 2 Million Identities
We use a large dataset which contains about 2 millions identities to simulate real cases.
| Dataset | Classes | Backbone | Batch-size | FP16 | TF32 | Partial FC | Samples/sec |
|------------|------------|------------|------------|------|------|------------|-------------|
| WebFace40M | 2 Millions | IResNet-50 | 512 | × | × | × | ~1750 |
| WebFace40M | 2 Millions | IResNet-50 | 512 | × | √ | × | ~1810 |
| WebFace40M | 2 Millions | IResNet-50 | 1024 | √ | √ | × | ~2810 |
| WebFace40M | 2 Millions | IResNet-50 | 1024 | √ | √ | √ | ~4220 |
| WebFace40M | 2 Millions | IResNet-50 | 2048 | √ | √ | √ | ~5330 |
### 2. 600K Identities
We use a large dataset which contains about 600k identities to simulate real cases.
| Dataset | Classes | Backbone | Batch-size | FP16 | Samples/sec |
|-------------|---------|------------|------------|------|-------------|
| WebFace600K | 618K | IResNet-50 | 512 | × | ~2220 |
| WebFace600K | 618K | IResNet-50 | 512 | √ | ~2610 |
| WebFace600K | 618K | IResNet-50 | 1024 | × | ~2940 |
| WebFace600K | 618K | IResNet-50 | 1024 | √ | ~3790 |
| WebFace600K | 618K | IResNet-50 | 2048 | √ | ~4680 |