ND40rs_v2 by Microsoft Azure
(All-cores)
(Single-core)
Specifications
Server Metadata
Vendor ID | azure |
Server ID | Standard_ND40rs_v2 |
Name | ND40rs_v2 |
API Reference | Standard_ND40rs_v2 |
Description | NDSv2 family [RDMA capable] [Premium Storage capable] [Intel processor] 40 vCPUs |
Family | NDSv2 |
Hw Virt | - |
Status | active |
Observed At | 2026-09-16T17:34:05.502071 |
Availability
| REGION / ID | SPOT | ONDEMAND |
|---|---|---|
East US (US) / eastus | 4.0715 USD/h | 22.032 USD/h |
West US 2 (US) / westus2 | 4.0715 USD/h | 22.032 USD/h |
South Central US (US) / southcentralus | 4.8857 USD/h | 26.438 USD/h |
West Europe (NL) / westeurope | 5.0868 USD/h | 27.526 USD/h |
Southeast Asia (SG) / southeastasia | 5.6349 USD/h | 30.492 USD/h |
Processor
vCPUs | 40 |
Hypervisor | Microsoft Hyper-V |
CPU Allocation | Dedicated |
CPU Architecture | x86_64 |
System Resources and Accelerators
| MEMORY | |
|---|---|
Memory Amount | 672 GiB |
| GPU | |
|---|---|
GPU Count | 8 |
GPU Memory Min | 30 GiB |
GPU Memory Total | 238 GiB |
GPU Manufacturer | NVIDIA |
GPU Model | V100 |
GPUs |
| STORAGE | |
|---|---|
Storage Size | 2970 GB |
Storages |
|
| NETWORK | |
|---|---|
Inbound Traffic | 0 GB/month |
Outbound Traffic | 0 GB/month |
IPv4 | 0 |
Server Description
A high-performance GPU instance featuring eight NVIDIA V100 accelerators, massive memory, and RDMA networking for clustered parallel workloads.
Microsoft Azure ND40rs_v2 is a high-end, GPU-accelerated server from the NDSv2 family, running on the Microsoft Hyper-V hypervisor. Built on the x86_64 architecture, it features 40 dedicated Intel vCPUs and 672.0 GB of system memory, providing 16.8 GB of RAM per core. The hardware configuration is heavily optimized for parallel computing, featuring eight NVIDIA V100 GPUs with a total of 238 GB of VRAM. Additionally, the server includes 2970 GB of local storage, supports Premium Storage, and is RDMA-capable for high-throughput, low-latency networking. This resource-dense profile makes the ND40rs_v2 suitable for demanding workloads such as deep learning, high-performance computing clusters, and large-scale scientific simulations.
Economics
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