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 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 support https //docs ionos com/cloud/support/general information/contact information 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 https //docs ionos com/cloud/compute services/compute engine/cloud gpu vm/overview/limitations storage selection note the platform supports only 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 https //docs ionos com/cloud/backup and storage/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 https //docs ionos com/cloud/compute services/compute engine/cloud gpu vm/cloud gpu vm faq#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 provides ssd premium as the default attached volume for cloud gpu vms