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Methodology We are using the standard “tf_cnn_benchmarks.py” benchmark script from official TensorFlow GitHub ( more details ).

Based on 1,196 user benchmarks for the Nvidia Quadro RTX 8000 and the Titan RTX, we rank them both on effective speed and value for money against the best 639 GPUs. Based on 177,068 user benchmarks for the Nvidia Quadro RTX 6000 and the RTX 2080-Ti, we rank them both on effective speed and value for money against the best 639 GPUs. Furthermore, the raw image throughput performance (img/sec) is on par with the mighty RTX 8000. We could likely use larger batch sizes, however, we are starting to see diminishing returns even at 128.I disagree with the closing statement, the real competition to this card when you don’t need the memory capacity but do need additional performance is a dual RTX5000 setup and not a single RTX6000.

@Nejc The scene probably needs to address some out-of-core memory on 11GB VRAM cards which is not needed on RTX Titan & RTX 6000/8000We also edit lines 44 and below as shown to enable FP16 precision:In our benchmarks for Inferencing, a ResNet50 Model trained in Caffe will be run using the command line as follows.We then open the en_de_gnmt-like-4GPUs.py and edit our variables.Using precision of INT8 is by far the fastest inferencing method if at all possible converting code to INT8 will yield faster runs. Objects per second trained which we plot.While Resnet-50 is a Convolutional Neural Network (CNN) that is typically used for image classification, Recurrent Neural Networks (RNN) such as Google Neural Machine Translation (GNMT) are used for applications such as real-time language translations.We will run batch sizes of 16, 32, 64, 128 and change from FP16 to FP32.

To enable this benchmark to finish on these GPU’s one might need to lower the batch size to smaller values like 32, 16, 8. In this post, we are comparing the most popular graphics cards for deep learning in 2020: NVIDIA RTX 2080 Ti, Titan RTX, Quadro RTX 6000, and Quadro RTX 8000.

NVIDIA Quadro RTX 8000 Deep Learning Benchmarks. As we continue to innovate on our review format, we are now adding deep learning benchmarks. During training the neural network is learning features of images, (e.g. For this section, we compare training the official Transformer model (BASE and BIG) from the official Tensorflow Github.Looking at the TITAN RTX and the 2080 Ti, this card you gives you the best of both worlds. If you’ve done any significant amount deep learning on GPUs, you’ll be familiar with the dreaded ‘RuntimeError: CUDA error: out of memory’.

... 4000 5000 6000 8000.

In future reviews, we will add more results to this data set. The large memory capacity, plus the blower design allows for densely populated system configurations with ample memory capacity to train large models.