Cloud GPU VM Overview

The powerful Graphics Processing Unit (GPU) VMs excel at massive parallel processing. This specialized hardware best accelerates compute-intensive workloads, such as training complex AI models, machine learning inference, and high-speed 3D rendering.

Cloud GPU VMs use predefined templates that cannot be modified. Use the DCD or IONOS CLOUD API with template read access and volume management to deploy Cloud GPU VMs. Plan storage requirements carefully to select the appropriate template, as insufficient internal storage will require the use of additional volumes.

Cloud GPU VM configuration

Dedicated instance specifications and configuration templates

The server sizing model allocates Cloud GPU VMs with corresponding dedicated CPU cores and RAM based on available host capacity. The architecture uses PCIe passthrough for direct hardware access and optimal performance.

Template specifications

You may choose between the following four template sizes. The templates can only be used with the Cloud GPU VMs. CPU and RAM allocate proportionally to the number of GPUs. Resources use dedicated cores with limited flexibility.

Warning: Configuration templates are created during provisioning and cannot be changed later.

The breakdown of resources is as follows:

Template	GPU Model	GPU Type	Number of GPUs	Dedicated CPUs	RAM (GiB)	Storage (GB)
S	NVIDIA H200	H200 PCIe	1	15	267	1024
M	NVIDIA H200	H200 PCIe	2	30	534	1536
L	NVIDIA H200	H200 PCIe	4	60	1068	2048
XL	NVIDIA H200	H200 PCIe	8	127	2136	4096

Resource limits: By default, you can deploy only 1 Cloud GPU VM using the H200–S template. To deploy templates sized M, L, or XL, or to run multiple S instances, you must first request a resource limit increase through the IONOS Cloud Support.

Dedicated resource model

• CPU Cores: Dedicated AMD EPYC Turin (non-shared) allocation, with a fixed ratio proportional to the number of GPUs.

• Memory: Fixed ratio based on host specifications.

• Flexibility: Static resource allocation without dynamic scaling. For more information, see Known Constraints.

Storage selection

Note: The platform supports only IONOS CLOUD Linux images at start.

The first connected volume serves as the storage volume, containing the operating system and required system files. Provision storage volumes with adequate capacity at the initial Cloud GPU VM provisioning, because they use fixed sizing and cannot be detached or upscaled after deployment. You can also add additional storage for datasets requiring expansion. For more information, see Block Storage.

GPU specifications

The following table provides the specifications:

Specification	Details
Hardware architecture	Uses "PCIe passthrough" architecture to provide direct hardware access for optimal performance, simplified deployment, and accelerated production readiness. For more information, see GPU architecture and performance.
GPU model	Primary Offering: High-End NVIDIA H200 GPUs
Maximum GPUs per template	8x GPU units per server
Deployment density	Optimized for high-performance inference workloads

Data security

IONOS CLOUD provides SSD premium as the default attached volume for Cloud GPU VMs.