NVIDIA Tesla K80 (or V100 GPU for NCv2 series) - per Vanja's comment

README.md

@@ -35,7 +35,7 @@ Follow the instruction bellow to read about compute requirements and install req
 
 We recommend using a virtual machine to run the example notebooks and scripts. Specifically, you will need a GPU powered Linux machine, as this repository is developed and tested on __Linux only__. The easiest way to get started is to use the [Azure Data Science Virtual Machine (DSVM) for Linux (Ubuntu)](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro). This VM will come installed with all the system requirements that are needed to create the conda environment described below and then run the notebooks in this repository. 
 
-For this repo, we recommend selecting a multi-GPU Ubuntu VM of type [Standard_NC12](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series). The machine is powered by NVIDIA Tesla V100 GPU which can be found in most Azure regions.
+For this repo, we recommend selecting a multi-GPU Ubuntu VM of type [Standard_NC12](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series). The machine is powered by NVIDIA Tesla K80 (or V100 GPU for NCv2 series) which can be found in most Azure regions.
 
 > NOTE: For users new to Azure, your subscription may not come with a quota for GPUs. You may need to go into the Azure portal to increase your quota for GPU VMs. Learn more about how to do this here: https://docs.microsoft.com/en-us/azure/azure-subscription-service-limits.