vMind GPU as a Service means getting both the hardware and the operation of GPU infrastructure as a service. GPU selection, cluster setup, driver and CUDA management, 24/7 monitoring and capacity planning rest with the vMind team. The service level commitment is 99.99% availability and a 15-minute response time for critical incidents.
GPU as a Service, or GPUaaS for short, means getting the operation of GPU infrastructure as a service along with its hardware. With classic GPU rental, you are given a resource, and driver installation, cluster configuration, monitoring, fault response and capacity planning are the organization's responsibility. Under vMind GPU as a Service, all of these are included in the service.
The difference is clearest in a crisis. When training crashes at midnight, when a driver update conflicts with a library, or when the cluster runs below expected performance, the vMind team responds.
Your workload is assessed, and the suitable GPU model, cluster topology and network architecture are determined. The infrastructure is built and brought online by the vMind team. Your team is given console access with defined permissions; even though management is with vMind, the infrastructure is not a black box for you.
After go-live, GPU temperature, memory usage, utilization rate and workload health are continuously monitored.
When an anomaly is detected, proactive intervention is carried out.
A monthly usage report and capacity recommendations are shared regularly.
Leave the Infrastructure to Us, You Build Your Model
vMind GPU as a Service keeps your AI team from losing time on infrastructure management. All operations, from setup to continuous operation, are with the vMind team.
You can leave the infrastructure to the vMind team. Share your workload, and let us determine the suitable GPU, cluster configuration and service level together.
We offer customized solutions to our customers by bringing together the necessary technology and services.
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