Azure N-Series Virtual Machines, the fastest GPUs in the public cloud, are now available in preview
N-Series instances are enabled with NVIDIA’s cutting edge GPUs to allow you to run GPU-accelerated workloads and visualize them. These powerful sizes come with the agility you have come to expect from Azure, paying per-minute of usage.
Our N-Series VMs are split into two categories.
The NC-Series (compute-focused GPUs)
With the NC Series you will be able to run compute intensive HPC workloads using CUDA or OpenCL.
The Tesla K80 GPUs and offers the fastest computational GPU available in the public cloud. Furthermore, unlike other providers,
These are perfect for CUDA across many scenarios including energy exploration applications, crash simulations, ray traced rendering, deep learning and more.
The Tesla K80 delivers 4992 CUDA cores with a dual-GPU design, up to 2.91 Teraflops of double-precision and up to 8.93 Teraflops of single-precision performance. Following are the Tesla K80 GPU sizes available:
NC6 |
NC12 |
NC24 |
|
Cores |
6 (E5-2690v3) |
12 (E5-2690v3) |
24 (E5-2690v3) |
GPU |
1 x K80 GPU (1/2 Physical Card) |
2 x K80 GPU (1 Physical Card) |
4 x K80 GPU (2 Physical Cards) |
Memory |
56 GB |
112 GB |
224 GB |
Disk |
380 GB SSD |
680 GB SSD |
1.44 TB SSD |
The NV-Series is focused more on visualization.
The NV Series is based around Data movement has traditionally been a challenge with HPC scenarios using large datasets produced in the cloud.
Tesla M60 GPUs and NVIDIA GRID in Azure for desktop accelerated applications and virtual desktops. With these powerful visualization GPUs in Azure, you will be able to visualize graphic-intensive workflows to get superior graphics capability and run single precision workloads such as encoding and rendering.
The Tesla M60 delivers 4096 CUDA cores in a dual-GPU design with up to 36 streams of 1080p H.264. Following are the Tesla M60 GPU Sizes available:
NV6 |
NV12 |
NV24 |
|
Cores |
6 (E5-2690v3) |
12 (E5-2690v3) |
24 (E5-2690v3) |
GPU |
1 x M60 GPU (1/2 Physical Card) |
2 x M60 GPU (1 Physical Card) |
4 x M60 GPU (2 Physical Cards) |
Memory |
56 GB |
112 GB |
224 GB |
Disk |
380 GB SSD |
680 GB SSD |
1.44 TB SSD |
The preview will start in South Central region initially and will expand to additional regions in the next couple of months as we approach General Availability before the end of the year.
We encourage you to register your interest in the preview. To learn more about the technology and use cases for N-Series, check out our recent Channel 9 video. Pricing information can be found on the Virtual Machines pricing page.
I am so excited by being able to offer UK Academics these virtual machines and cannot wait to see what new use cases and scenarios they’re able to solve with GPUs in Azure.
Comments
- Anonymous
November 16, 2016
Azure N-series virtual machines. Coming to all Azure Users on December 1st 2016, the Azure N-series VMs are designed for the most intensive compute workloads, including deep learning, simulations, rendering and the training of neural networks. https://azure.microsoft.com/en-us/blog/azure-n-series-general-availability-on-december-1/ - Anonymous
November 26, 2016
This blog is a great introduction to using Cloud GPU on a NC24 VM running on Ubuntu 16.04. N-Series VM sizes are at http://gpu.azure.com. In this post, we show you how to operationalize a Azure GPU-trained DCNN model using Azure ML and Azure ML Studio.https://blogs.technet.microsoft.com/machinelearning/2016/10/12/deep-neural-network-in-azure/ - Anonymous
November 26, 2016
Training Deep Neural Networks on ImageNet Using Microsoft R Server and Azure GPU VMs - https://blogs.technet.microsoft.com/machinelearning/2016/11/15/imagenet-deep-neural-network-training-using-microsoft-r-server-and-azure-gpu-vms/ - Anonymous
November 26, 2016
Building Deep Neural Networks in the Cloud with Azure GPU VMs, MXNet and Microsoft R Server https://blogs.technet.microsoft.com/machinelearning/2016/09/15/building-deep-neural-networks-in-the-cloud-with-azure-gpu-vms-mxnet-and-microsoft-r-server/