GPU optimized virtual machine sizes
GPU optimized VM sizes are specialized virtual machines available with single, multiple, or fractional GPUs. These sizes are designed for compute-intensive, graphics-intensive, and visualization workloads. This article provides information about the number and type of GPUs, vCPUs, data disks, and NICs. Storage throughput and network bandwidth are also included for each size in this grouping.
The NCv3-series and NC T4_v3-series sizes are optimized for compute-intensive GPU-accelerated applications. Some examples are CUDA and OpenCL-based applications and simulations, AI, and Deep Learning. The NC T4 v3-series is focused on inference workloads featuring NVIDIA's Tesla T4 GPU and AMD EPYC2 Rome processor. The NCv3-series is focused on high-performance computing and AI workloads featuring NVIDIA’s Tesla V100 GPU.
The ND A100 v4-series size is focused on scale-up and scale-out deep learning training and accelerated HPC applications. The ND A100 v4-series uses 8 NVIDIA A100 TensorCore GPUs, each available with a 200 Gigabit Mellanox InfiniBand HDR connection and 40 GB of GPU memory.
NV-series and NVv3-series sizes are optimized and designed for remote visualization, streaming, gaming, encoding, and VDI scenarios using frameworks such as OpenGL and DirectX. These VMs are backed by the NVIDIA Tesla M60 GPU.
NVv4-series VM sizes optimized and designed for VDI and remote visualization. With partitioned GPUs, NVv4 offers the right size for workloads requiring smaller GPU resources. These VMs are backed by the AMD Radeon Instinct MI25 GPU. NVv4 VMs currently support only Windows guest operating system.
Supported operating systems and drivers
To take advantage of the GPU capabilities of Azure N-series VMs, NVIDIA or AMD GPU drivers must be installed.
For VMs backed by NVIDIA GPUs, the NVIDIA GPU Driver Extension installs appropriate NVIDIA CUDA or GRID drivers. Install or manage the extension using the Azure portal or tools such as Azure PowerShell or Azure Resource Manager templates. See the NVIDIA GPU Driver Extension documentation for supported operating systems and deployment steps. For general information about VM extensions, see Azure virtual machine extensions and features.
Alternatively, you may install NVIDIA GPU drivers manually. See Install NVIDIA GPU drivers on N-series VMs running Windows or Install NVIDIA GPU drivers on N-series VMs running Linux for supported operating systems, drivers, installation, and verification steps.
For VMs backed by AMD GPUs, the AMD GPU driver extension installs appropriate AMD drivers. Install or manage the extension using the Azure portal or tools such as Azure PowerShell or Azure Resource Manager templates. For general information about VM extensions, see Azure virtual machine extensions and features.
Alternatively, you may install AMD GPU drivers manually. See Install AMD GPU drivers on N-series VMs running Windows for supported operating systems, drivers, installation, and verification steps.
For availability of N-series VMs, see Products available by region.
N-series VMs can only be deployed in the Resource Manager deployment model.
N-series VMs differ in the type of Azure Storage they support for their disks. NC and NV VMs only support VM disks that are backed by Standard Disk Storage (HDD). All other GPU VMs support VM disks that are backed by Standard Disk Storage and Premium Disk Storage (SSD).
If you want to deploy more than a few N-series VMs, consider a pay-as-you-go subscription or other purchase options. If you're using an Azure free account, you can use only a limited number of Azure compute cores.
You might need to increase the cores quota (per region) in your Azure subscription, and increase the separate quota for NC, NCv2, NCv3, ND, NDv2, NV, or NVv2 cores. To request a quota increase, open an online customer support request at no charge. Default limits may vary depending on your subscription category.
- General purpose
- Compute optimized
- High performance compute
- Memory optimized
- Storage optimized
- Previous generations
Learn more about how Azure compute units (ACU) can help you compare compute performance across Azure SKUs.