Cloud GPU VM FAQ
5 min
general what are cloud gpu vms? cloud graphics processing units (gpu) cloud virtual machines (vms) provide access to powerful gpus through a virtualized cloud environment, enabling massive parallel processing this specialized hardware accelerates compute intensive workloads, including training complex ai models, machine learning inference, and high speed 3d rendering direct gpu access delivers the low latency that real time applications require, while predictable, straightforward pricing makes continuous operation affordable while based on shared resources, the cloud gpu vm can rival physical servers through a platform design that can redistribute available performance capacities among individual instances at the same time, reduced operational complexity and highly optimized resource usage translate into lower operating costs the cloud gpu connects directly to the vm via peripheral component interconnect express (pcie) passthrough for near native performance cloud gpu vms come complete with vcpus and ram you can choose a template https //docs ionos com/cloud/compute services/compute engine/cloud gpu vm/overview#template specifications that suits your needs for more information, see cloud gpu vm workflow https //docs ionos com/cloud/compute services/compute engine/cloud gpu vm/api how tos#cloud gpu vm workflow you can also create and manage your cloud gpu vm from the dcd https //docs ionos com/cloud/compute services/compute engine/cloud gpu vm/how tos how many cloud gpu vms can i deploy? 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 gpu architecture and performance what architecture does cloud gpu vm use for hardware access? cloud gpu vm uses a pcie passthrough architecture it provides direct hardware access to ensure optimal performance, simplify deployment, and accelerate your production readiness how are the eight gpus configured in cloud gpu vm? cloud gpu vm organizes the eight gpus into two clusters of four (2 x 4 gpus), connected through nvlink within each cluster how does the configuration affect my workloads? because the gpus are split into two groups, address each cluster separately rather than as a single 8 gpu pool to maximize performance, split your workloads into smaller chunks, process them across the two gpu groups, and merge the results afterward
