GPU optimized virtual machine sizes

Applies to: ✔️ Linux VMs ✔️ Windows VMs ✔️ Flexible scale sets ✔️ Uniform scale sets


Try the Virtual machines selector tool to find other sizes that best fit your workload.

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 as well as storage throughput and network bandwidth for sizes in this series.

  • 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 NC 100 v4-series sizes are focused on midrange AI training and batch inference workload. The NC A100 v4-series offers flexibility to select one, two, or four NVIDIA A100 80GB PCIe Tensor Core GPUs per VM to use the right-size GPU acceleration for your workload.

  • The ND A100 v4-series sizes are 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.

  • NGads V620-series VM sizes are optimized for high performance, interactive gaming experiences hosted in Azure. They're powered by AMD Radeon PRO V620 GPUs and AMD EPYC 7763 (Milan) CPUs.

  • NVv3-series and NVads A10 v5-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 NVIDIA Tesla M60 GPU (NVv3) and A10 GPUs(NVads A10 v5).

  • 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 AMD Radeon Instinct MI25 GPU. NVv4 VMs currently support only Windows guest operating system.

  • NDm A100 v4-series virtual machines are designed for high-end Deep Learning training and tightly coupled scale-up and scale-out HPC workloads. The NDm A100 v4 series starts with a single virtual machine (VM) and eight NVIDIA Ampere A100 80GB Tensor Core GPUs.

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.

Deployment considerations

  • 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 quota separately for each GPU VM family. To request a quota increase, open an online customer support request at no charge. Default limits may vary depending on your subscription category.

Other sizes

Next steps

Learn more about how Azure compute units (ACU) can help you compare compute performance across Azure SKUs.