test
NVIDIA Performance on MLPerf 0.6 AI Benchmarks
ResNet-50 v1.5 Time to Solution on V100
MXNet | Batch Size refer to CNN V100 Training table below | Precision: Mixed | Dataset: ImageNet2012 | Convergence criteria - refer to MLPerf requirements
Training Image Classification on CNNs
ResNet-50 V1.5 Throughput on V100
DGX-1: 8x Tesla V100-SXM2-32GB, E5-2698 v4 2.2 GHz | Batch Size = 256 | MXNet = 19.06-py3, Tensorflow and PyTorch = 19.07_py3 | Precision: Mixed | Dataset: ImageNet2012
ResNet-50 V1.5 Throughput on T4
Supermicro SYS-4029GP-TRT T4: 8x Tesla T4 16GB, Gold 6140 2.3 GHz | Batch Size = 208 for MXNet, PyTorch = 256, TensorFlow = 128 | MXNet and TensorFlow = 19.05-py3, PyTorch = 19.07_py3 | Precision: Mixed | Dataset: ImageNet2012
Training Performance
NVIDIA Performance on MLPerf 0.6 AI Benchmarks
Framework | Network | Network Type | Time to Solution | GPU | Server | MLPerf-ID | Precision | Dataset | GPU Version |
---|---|---|---|---|---|---|---|---|---|
MXNet | ResNet-50 v1.5 | CNN | 115.22 minutes | 8x V100 | DGX-1 | 0.6-8 | Mixed | ImageNet2012 | V100-SXM2-16GB |
CNN | 57.87 minutes | 16x V100 | DGX-2 | 0.6-17 | Mixed | ImageNet2012 | V100-SXM3-32GB | ||
CNN | 52.74 minutes | 16x V100 | DGX-2H | 0.6-19 | Mixed | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 2.59 minutes | 512x V100 | DGX-2H | 0.6-29 | Mixed | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 1.69 minutes | 1040x V100 | DGX-1 | 0.6-16 | Mixed | ImageNet2012 | V100-SXM2-16GB | ||
CNN | 1.33 minutes | 1536x V100 | DGX-2H | 0.6-30 | Mixed | ImageNet2012 | V100-SXM3-32GB-H | ||
PyTorch | SSD-ResNet-34 | CNN | 22.36 minutes | 8x V100 | DGX-1 | 0.6-9 | Mixed | COCO2017 | V100-SXM2-16GB |
CNN | 12.21 minutes | 16x V100 | DGX-2 | 0.6-18 | Mixed | COCO2017 | V100-SXM3-32GB | ||
CNN | 11.41 minutes | 16x V100 | DGX-2H | 0.6-20 | Mixed | COCO2017 | V100-SXM3-32GB-H | ||
CNN | 4.78 minutes | 64x V100 | DGX-2H | 0.6-21 | Mixed | COCO2017 | V100-SXM3-32GB-H | ||
CNN | 2.67 minutes | 240x V100 | DGX-1 | 0.6-13 | Mixed | COCO2017 | V100-SXM2-16GB | ||
CNN | 2.56 minutes | 240x V100 | DGX-2H | 0.6-24 | Mixed | COCO2017 | V100-SXM3-32GB-H | ||
CNN | 2.23 minutes | 240x V100 | DGX-2H | 0.6-27 | Mixed | COCO2017 | V100-SXM3-32GB-H | ||
Mask R-CNN | CNN | 207.48 minutes | 8x V100 | DGX-1 | 0.6-9 | Mixed | COCO2017 | V100-SXM2-16GB | |
CNN | 101 minutes | 16x V100 | DGX-2 | 0.6-18 | Mixed | COCO2017 | V100-SXM3-32GB | ||
CNN | 95.2 minutes | 16x V100 | DGX-2H | 0.6-20 | Mixed | COCO2017 | V100-SXM3-32GB-H | ||
CNN | 32.72 minutes | 64x V100 | DGX-2H | 0.6-21 | Mixed | COCO2017 | V100-SXM3-32GB-H | ||
CNN | 22.03 minutes | 192x V100 | DGX-1 | 0.6-12 | Mixed | COCO2017 | V100-SXM2-16GB | ||
CNN | 18.47 minutes | 192x V100 | DGX-2H | 0.6-23 | Mixed | COCO2017 | V100-SXM3-32GB-H | ||
PyTorch | GNMT | RNN | 20.55 minutes | 8x V100 | DGX-1 | 0.6-9 | Mixed | WMT16 English-German | V100-SXM2-16GB |
RNN | 10.94 minutes | 16x V100 | DGX-2 | 0.6-18 | Mixed | WMT16 English-German | V100-SXM3-32GB | ||
RNN | 9.87 minutes | 16x V100 | DGX-2H | 0.6-20 | Mixed | WMT16 English-German | V100-SXM3-32GB-H | ||
RNN | 2.12 minutes | 256x V100 | DGX-2H | 0.6-25 | Mixed | WMT16 English-German | V100-SXM3-32GB-H | ||
RNN | 1.99 minutes | 384x V100 | DGX-1 | 0.6-14 | Mixed | WMT16 English-German | V100-SXM2-16GB | ||
RNN | 1.8 minutes | 384x V100 | DGX-2H | 0.6-26 | Mixed | WMT16 English-German | V100-SXM3-32GB-H | ||
PyTorch | Transformer | Attention | 20.34 minutes | 8x V100 | DGX-1 | 0.6-9 | Mixed | WMT17 English-German | V100-SXM2-16GB |
Attention | 11.04 minutes | 16x V100 | DGX-2 | 0.6-18 | Mixed | WMT17 English-German | V100-SXM3-32GB | ||
Attention | 9.8 minutes | 16x V100 | DGX-2H | 0.6-20 | Mixed | WMT17 English-German | V100-SXM3-32GB-H | ||
Attention | 2.41 minutes | 160x V100 | DGX-2H | 0.6-22 | Mixed | WMT17 English-German | V100-SXM3-32GB-H | ||
Attention | 2.05 minutes | 480x V100 | DGX-1 | 0.6-15 | Mixed | WMT17 English-German | V100-SXM2-16GB | ||
Attention | 1.59 minutes | 480x V100 | DGX-2H | 0.6-28 | Mixed | WMT17 English-German | V100-SXM3-32GB-H | ||
TensorFlow | MiniGo | Reinforcement Learning | 27.39 minutes | 8x V100 | DGX-1 | 0.6-10 | Mixed | N/A | V100-SXM2-16GB |
Reinforcement Learning | 13.57 minutes | 24x V100 | DGX-1 | 0.6-11 | Mixed | N/A | V100-SXM2-16GB |
V100 Training Performance
Framework | Network | Network Type | Throughput | GPU | Server | Container | Precision | Batch Size | Dataset | GPU Version |
---|---|---|---|---|---|---|---|---|---|---|
MXNet | Inception V3 | CNN | 527 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-32GB |
CNN | 606 images/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 4105 images/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-32GB | ||
CNN | 4648 images/sec | 8x V100 | DGX-2H | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
ResNet-50 | CNN | 1409 images/sec | 1x V100 | DGX-1 | 19.02-py3 | Mixed | 128 | ImageNet2012 | V100-SXM2-16GB | |
CNN | 1442 images/sec | 1x V100 | DGX-2 | 19.02-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB | ||
CNN | 10380 images/sec | 8x V100 | DGX-1 | 19.02-py3 | Mixed | 128 | ImageNet2012 | V100-SXM2-16GB | ||
CNN | 10530 images/sec | 8x V100 | DGX-2 | 19.02-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB | ||
ResNet-50 v1.5 | CNN | 1422 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 208 | ImageNet2012 | V100-SXM2-16GB | |
CNN | 1597 images/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 9566 images/sec | 8x V100 | DGX-1 | 19.06-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-32GB | ||
CNN | 11056 images/sec | 8x V100 | DGX-2 | 19.05-py3 | Mixed | 128 | ImageNet2012 | V100-SXM3-32GB | ||
CNN | 11507 images/sec | 8x V100 | DGX-2H | 19.05-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
PyTorch | Inception V3 | CNN | 543 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-32GB |
CNN | 629 images/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 4156 images/sec | 8x V100 | DGX-1 | 19.03-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-32GB | ||
Mask R-CNN | CNN | 14 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 4 | COCO2014 | V100-SXM2-32GB | |
CNN | 17 images/sec | 1x V100 | DGX-2H | 19.05-py3 | Mixed | 16 | COCO2014 | V100-SXM3-32GB-H | ||
CNN | 88 images/sec | 8x V100 | DGX-1 | 19.06-py3 | Mixed | 16 | COCO2014 | V100-SXM2-32GB | ||
ResNet-50 | CNN | 819 images/sec | 1x V100 | DGX-1 | 19.02_py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-16GB | |
CNN | 820 images/sec | 1x V100 | DGX-2 | 19.02-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB | ||
CNN | 6218 images/sec | 8x V100 | DGX-1 | 19.02-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-16GB | ||
ResNet-50 v1.5 | CNN | 928 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-16GB | |
CNN | 1036 images/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 7288 images/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-16GB | ||
SSD v1.1 | CNN | 225 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 64 | COCO 2017 | V100-SXM2-32GB | |
CNN | 299 images/sec | 1x V100 | DGX-2H | 19.05-py3 | Mixed | 64 | COCO 2017 | V100-SXM3-32GB-H | ||
CNN | 2018 images/sec | 8x V100 | DGX-1 | 19.06-py3 | Mixed | 64 | COCO 2017 | V100-SXM2-32GB | ||
Tacotron2 | CNN | 11743 total input tokens/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 80 | LJ Speech 1.1 | V100-SXM2-16GB | |
CNN | 16847 total input tokens/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 80 | LJ Speech 1.1 | V100-SXM3-32GB-H | ||
CNN | 81410 total input tokens/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 80 | LJ Speech 1.1 | V100-SXM2-32GB | ||
CNN | 104447 total input tokens/sec | 8x V100 | DGX-2H | 19.07-py3 | Mixed | 80 | LJ Speech 1.1 | V100-SXM3-32GB-H | ||
WaveGlow | CNN | 77780 output samples/sec | 1x V100 | DGX-1 | 19.05-py3 | Mixed | 8 | LJ Speech 1.1 | V100-SXM2-16GB | |
CNN | 91052 output samples/sec | 1x V100 | DGX-2H | 19.05-py3 | Mixed | 8 | LJ Speech 1.1 | V100-SXM3-32GB-H | ||
CNN | 533364 output samples/sec | 8x V100 | DGX-1 | 19.05-py3 | Mixed | 8 | LJ Speech 1.1 | V100-SXM2-32GB | ||
TensorFlow | Inception V3 | CNN | 542 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-32GB |
CNN | 626 images/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 4097 images/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-32GB | ||
ResNet-50 v1.5 | CNN | 840 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-16GB | |
CNN | 965 images/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM3-32GB-H | ||
CNN | 6474 images/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 256 | ImageNet2012 | V100-SXM2-16GB | ||
SSD v1.1 | CNN | 114 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 32 | COCO 2017 | V100-SXM2-32GB | |
CNN | 125 images/sec | 1x V100 | DGX-2 | 19.07-py3 | Mixed | 32 | COCO 2017 | V100-SXM3-32GB | ||
CNN | 665 images/sec | 8x V100 | DGX-1 | 19.05-py3 | Mixed | 32 | COCO 2017 | V100-SXM2-32GB | ||
CNN | 770 images/sec | 8x V100 | DGX-2 | 19.05-py3 | Mixed | 32 | COCO 2017 | V100-SXM3-32GB | ||
U-Net Industrial | CNN | 95 images/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 16 | DAGM2007 | V100-SXM2-32GB | |
CNN | 110 images/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 16 | DAGM2007 | V100-SXM3-32GB-H | ||
CNN | 492 images/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 2 | DAGM2007 | V100-SXM2-32GB | ||
CNN | 515 images/sec | 8x V100 | DGX-2 | 19.07-py3 | Mixed | 2 | DAGM2007 | V100-SXM3-32GB | ||
PyTorch | GNMT V2 | RNN | 75975 total tokens/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 128 | WMT16 English-German | V100-SXM2-32GB |
RNN | 82766 total tokens/sec | 1x V100 | DGX-2 | 19.07-py3 | Mixed | 128 | WMT16 English-German | V100-SXM3-32GB | ||
RNN | 582605 total tokens/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 128 | WMT16 English-German | V100-SXM2-32GB | ||
TensorFlow | GNMT V2 | RNN | 22471 total tokens/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 192 | WMT16 English-German | V100-SXM2-16GB |
RNN | 26039 total tokens/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 192 | WMT16 English-German | V100-SXM3-32GB-H | ||
RNN | 149008 total tokens/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 192 | WMT16 English-German | V100-SXM2-16GB | ||
PyTorch | NCF | Recommender | 22093850 samples/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | V100-SXM2-16GB |
Recommender | 24473776 samples/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | V100-SXM3-32GB-H | ||
Recommender | 104122673 samples/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | V100-SXM2-16GB | ||
Recommender | 109969915 samples/sec | 8x V100 | DGX-2H | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | V100-SXM3-32GB-H | ||
TensorFlow | NCF | Recommender | 26415693 samples/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | V100-SXM2-16GB |
Recommender | 56183685 samples/sec | 8x V100 | DGX-1 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | V100-SXM2-32GB | ||
TensorFlow | BERT-LARGE | Attention | 32 sentences/sec | 1x V100 | DGX-1 | 19.07-py3 | Mixed | 10 | SQuaD v1.1 | V100-SXM2-32GB |
Attention | 37 sentences/sec | 1x V100 | DGX-2H | 19.07-py3 | Mixed | 10 | SQuaD v1.1 | V100-SXM3-32GB-H | ||
Attention | 147 sentences/sec | 8xV100 | DGX-1 | 19.06-py3 | Mixed | 10 | SQuaD v1.1 | V100-SXM2-32GB | ||
Attention | 189 sentences/sec | 8x V100 | DGX-2H | 19.07-py3 | Mixed | 10 | SQuaD v1.1 | V100-SXM3-32GB-H | ||
T4 Training Performance
Framework | Network | Network Type | Throughput | GPU | Server | Container | Precision | Batch Size | Dataset | GPU Version |
---|---|---|---|---|---|---|---|---|---|---|
MXNet | Inception V3 | CNN | 174 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | ImageNet2012 | Tesla T4 |
CNN | 1381 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | ImageNet2012 | Tesla T4 | ||
ResNet-50 v1.5 | CNN | 446 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 208 | ImageNet2012 | Tesla T4 | |
CNN | 4116 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.05-py3 | Mixed | 208 | ImageNet2012 | Tesla T4 | ||
PyTorch | Inception V3 | CNN | 176 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | ImageNet2012 | Tesla T4 |
CNN | 1337 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | ImageNet2012 | Tesla T4 | ||
Mask R-CNN | CNN | 6 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.05-py3 | Mixed | 4 | COCO2014 | Tesla T4 | |
CNN | 38 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 4 | COCO2014 | Tesla T4 | ||
ResNet-50 v1.5 | CNN | 286 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 256 | ImageNet2012 | Tesla T4 | |
CNN | 2295 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 256 | ImageNet2012 | Tesla T4 | ||
SSD v1.1 | CNN | 76 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 64 | COCO 2017 | Tesla T4 | |
CNN | 622 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 64 | COCO 2017 | Tesla T4 | ||
Tacotron2 | CNN | 11236 total input tokens/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 80 | LJ Speech 1.1 | Tesla T4 | |
CNN | 78803 total input tokens/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 80 | LJ Speech 1.1 | Tesla T4 | ||
WaveGlow | CNN | 32167 output samples/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 8 | LJ Speech 1.1 | Tesla T4 | |
CNN | 250173 output samples/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.05-py3 | Mixed | 8 | LJ Speech 1.1 | Tesla T4 | ||
TensorFlow | Inception V3 | CNN | 177 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | ImageNet2012 | Tesla T4 |
CNN | 1345 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | ImageNet2012 | Tesla T4 | ||
ResNet-50 v1.5 | CNN | 263 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.05-py3 | Mixed | 256 | ImageNet2012 | Tesla T4 | |
CNN | 2057 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 256 | ImageNet2012 | Tesla T4 | ||
SSD v1.1 | CNN | 51 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 32 | COCO 2017 | Tesla T4 | |
CNN | 281 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.06-py3 | Mixed | 32 | COCO 2017 | Tesla T4 | ||
U-Net Industrial | CNN | 28 images/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 16 | DAGM2007 | Tesla T4 | |
CNN | 190 images/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 2 | DAGM2007 | Tesla T4 | ||
PyTorch | GNMT V2 | RNN | 25288 total tokens/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | WMT16 English-German | Tesla T4 |
RNN | 182072 total tokens/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 128 | WMT16 English-German | Tesla T4 | ||
TensorFlow | GNMT V2 | RNN | 9679 total tokens/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 192 | WMT16 English-German | Tesla T4 |
RNN | 57464 total tokens/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 192 | WMT16 English-German | Tesla T4 | ||
PyTorch | NCF | Recommender | 7584587 samples/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | Tesla T4 |
Recommender | 27011297 samples/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | Tesla T4 | ||
TensorFlow | NCF | Recommender | 10297010 samples/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | Tesla T4 |
Recommender | 16872638 samples/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 1048576 | MovieLens 20 Million | Tesla T4 | ||
TensorFlow | BERT | Attention | 9 sentences/sec | 1x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 3 | SQuaD v1.1 | Tesla T4 |
Attention | 30 sentences/sec | 8x T4 | Supermicro SYS-4029GP-TRT T4 | 19.07-py3 | Mixed | 3 | SQuaD v1.1 | Tesla T4 | ||