NVIDIA GeForce RTX 2080 Super Review


NVIDIA GeForce RTX 2080 Super Deep Learning Benchmarks

As we continue to innovate on our review format, we are now adding deep learning benchmarks. In future reviews, we will add more results to this data set.

ResNet-50 Inferencing Using Tensor Cores

ImageNet is an image classification database launched in 2007 designed for use in visual object recognition research. Organized by the WordNet hierarchy, hundreds of image examples represent each node (or category of specific nouns).

In our benchmarks for Inferencing, a ResNet50 Model trained in Caffe will be run using the command line as follows.

nvidia-docker run --shm-size=1g --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 --rm -v ~/Downloads/models/:/models -w /opt/tensorrt/bin nvcr.io/nvidia/tensorrt:18.11-py3 giexec --deploy=/models/ResNet-50-deploy.prototxt --model=/models/ResNet-50-model.caffemodel --output=prob --batch=16 --iterations=500 --fp16

Options are:
–deploy: Path to the Caffe deploy (.prototxt) file used for training the model
–model: Path to the model (.caffemodel)
–output: Output blob name
–batch: Batch size to use for inferencing
–iterations: The number of iterations to run
–int8: Use INT8 precision
–fp16: Use FP16 precision (for Volta or Turing GPUs), no specification will equal FP32

We can change the batch size to 16, 32, 64, 128 and precision to INT8, FP16, and FP32.

The results are in inference latency (in seconds.) If we take the batch size / Latency, that will equal the Throughput (images/sec) which we plot on our charts.

We also found that this benchmark does not use two GPU’s; it only runs on a single GPU. You can, however, run different instances on each GPU using commands like.
```NV_GPUS=0 nvidia-docker run ... &
NV_GPUS=1 nvidia-docker run ... &```

With these commands, a user can scale workloads across many GPU’s. Our graphs show combined totals.

We start with Turing’s new INT8 mode which is one of the benefits of using the NVIDIA RTX cards.

NVIDIA RTX 2080 Super ResNet 50 Inferencing INT8
NVIDIA RTX 2080 Super ResNet 50 Inferencing INT8

Using precision of INT8 is by far the fastest inferencing method if at all possible converting code to INT8 will yield faster runs. Installed memory has one of the largest impacts on these benchmarks which we can see by the increase in results in the RTX 2080 Ti and Titan RTX results. The NVIDIA GeForce RTX 2080 Super performs better than the RTX 2080, but the RTX 2080 Ti is still around 20% faster.

Let us look at FP16 and FP32 results.

NVIDIA RTX 2080 Super ResNet 50 Inferencing FP16
NVIDIA RTX 2080 Super ResNet 50 Inferencing FP16
NVIDIA RTX 2080 Super ResNet 50 Inferencing FP32
NVIDIA RTX 2080 Super ResNet 50 Inferencing FP32

Again, the NVIDIA GeForce RTX 2080 Super is no match for the RTX 2080 Ti, but it beats the RTX 2080 here.

ResNet-50 Training using Tensor Cores and TensorFlow

We also wanted to train the venerable ResNet-50 using Tensorflow. During training the neural network is learning features of images, (e.g. objects, animals, etc.) and determining what features are important. Periodically (every 1000 iterations), the neural network will test itself against the test set to determine training loss, which affects the accuracy of training the network. Accuracy can be increased through repetition (or running a higher number of epochs.)

The command line we will use is:

nvidia-docker run --shm-size=1g --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -v ~/Downloads/imagenet12tf:/imagenet --rm -w /workspace/nvidia-examples/cnn/ nvcr.io/nvidia/tensorflow:18.11-py3 python resnet.py --data_dir=/imagenet --layers=50 --batch_size=128 --iter_unit=batch --num_iter=500 --display_every=20 --precision=fp16

Parameters for resnet.py:
–layers: The number of neural network layers to use, i.e. 50.
–batch_size or -b: The number of ImageNet sample images to use for training the network per iteration. Increasing the batch size will typically increase training performance.
–iter_unit or -u: Specify whether to run batches or epochs.
–num_iter or -i: The number of batches or iterations to run, i.e. 500.
–display_every: How frequently training performance will be displayed, i.e. every 20 batches.
–precision: Specify FP32 or FP16 precision, which also enables TensorCore math for Volta and Turing GPUs.

While this script TensorFlow cannot specify individual GPUs to use, they can be specified by
setting export CUDA_VISIBLE_DEVICES= separated by commas (i.e. 0,1,2,3) within the Docker container workspace.

We will run batch sizes of 16, 32, 64, 128 and change from FP16 to FP32. Our graphs show combined totals.

Some GPUs like the new Super cards as well as the GeForce RTX 2060, RTX 2070, RTX 2080 and RTX 2080 Ti will not show higher batch size runs because of limited memory.

NVIDIA RTX 2080 Super ResNet 50 Training FP16
NVIDIA RTX 2080 Super ResNet 50 Training FP16
NVIDIA RTX 2080 Super ResNet 50 Training FP32
NVIDIA RTX 2080 Super ResNet 50 Training FP32

Again we are seeing some solid performance gains. These are probably not the results that have you sell your GeForce RTX 2080 and buy the Super version. Still buying today Super is the way to go.

Next, we are going to look at the NVIDIA GeForce RTX 2080 Super power and temperature tests and then give our final words.


  1. Since the Quadro P6000 was placed on some of the performance graphs, it would have been nice to see how the RTX 2080 Super compares against the Quadro RTX5000.

  2. GeForce RTX 2080 Super is a fucking gpu for deep learning! I really don’t recommend it!

  3. The GeForce RTX 2080 SUPER is a pretty good gaming GPU. However, due to the supply and demand on this card, the prices are sky rocketing. You could probably find a 3070 for the same price, if not less. If you can manage to find a good deal on one, and not on any 30 series product, pull the trigger on it. In my opinion, this is a great card, but you could find better.


Please enter your comment!
Please enter your name here

This site uses Akismet to reduce spam. Learn how your comment data is processed.