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GPU as a Service

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.

What Is GPU as a Service?

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. 

How Does GPU as a Service Work?

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. 

Why vMind GPU as a Service: Advantages

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. 

End-to-End Setup End-to-End Setup
Up-to-Date Software Layer Up-to-Date Software Layer
24/7 Proactive Monitoring 24/7 Proactive Monitoring
Capacity and Cost Management Capacity and Cost Management
Service Level Commitment Service Level Commitment

What Do We Do Under GPU as a Service?

  • 1- Infrastructure setup...
    Designing and bringing online the GPU selection, cluster topology and network architecture suited to your workload.
  • 2- Software layer management...
    Installing and keeping up to date the GPU drivers, CUDA, container runtime and orchestration layer.
  • 3- Continuous monitoring...
    24/7 monitoring of GPU temperature, memory usage, utilization rate and workload health.
  • 4- Proactive intervention...
    When an anomaly is detected, responding within 15 minutes in critical incidents.
  • 5- Capacity management...
    Scale-up and scale-down recommendations based on usage trends, and reporting of idle resources.
  • 6- Cost optimization...
    Sharing the monthly usage report and savings recommendations.

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.

With vMind, regardless of your preference - cloud, hybrid, or on-premises solutions - the service you receive will always be top-notch.

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