# Training performance report on NVIDIA A30 [NVIDIA A30 Tensor Core GPU](https://www.nvidia.com/en-us/data-center/products/a30-gpu/) is the most versatile mainstream compute GPU for AI inference and mainstream enterprise workloads. Besides, we can also use A30 to train deep learning models by its FP16 and TF32 supports. ## Test Server Spec | Key | Value | | ------------ | ------------------------------------------------ | | System | ServMax G408-X2 Rackmountable Server | | CPU | 2 x Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz | | Memory | 384GB, 12 x Samsung 32GB DDR4-2933 | | GPU | 8 x NVIDIA A30 24GB | | Cooling | 2x Customized GPU Kit for GPU support FAN-1909L2 | | Hard Drive | Intel SSD S4500 1.9TB/SATA/TLC/2.5" | | OS | Ubuntu 16.04.7 LTS | | Installation | CUDA 11.1, cuDNN 8.0.5 | | Installation | Python 3.7.10 | | Installation | PyTorch 1.9 (conda) | This server is donated by [AMAX](https://www.amaxchina.com/), many thanks! ## Experiments on arcface_torch We report training speed in following table, please also note that: 1. The training dataset is in mxnet record format and located on SSD hard drive. 2. Embedding-size are all set to 512. 3. We use a large dataset which contains about 618K identities to simulate real cases. | Dataset | Classes | Backbone | Batch-size | FP16 | TF32 | Samples/sec | | ----------- | ------- | ----------- | ---------- | ---- | ---- | ----------- | | WebFace600K | 618K | IResNet-50 | 1024 | × | × | ~2230 | | WebFace600K | 618K | IResNet-50 | 1024 | × | √ | ~3200 | | WebFace600K | 618K | IResNet-50 | 1024 | √ | × | ~3940 | | WebFace600K | 618K | IResNet-50 | 1024 | √ | √ | ~4350 | | WebFace600K | 618K | IResNet-50 | 2048 | √ | √ | ~5100 | | WebFace600K | 618K | IResNet-100 | 1024 | √ | √ | ~2810 | | WebFace600K | 618K | IResNet-180 | 1024 | √ | √ | ~1800 |