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GPU workloads on EKS just got way simpler with Qovery

Running GPU workloads on EKS has never been easy, until now. With Qovery’s latest update, you can enable GPU nodes, configure GPU access, and optimize costs automatically, all without writing a single line of YAML or touching Helm charts. Qovery now handles everything behind the scenes so you can focus entirely on your applications.
Alessandro Carrano
Lead Product Manager
Summary
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Run GPU-Powered applications in minutes

Whether you’re training models, running inference pipelines, or powering compute-heavy workloads, Qovery makes GPU provisioning on a EKS cluster simple as that:

  • Enable the GPUs on your cluster:
    • enable GPU node pools in your cluster settings
    • choose the instance types you need.
    • Mix On-Demand and Spot instances for the best balance of cost and performance.
    • Define limits on the number of GPUs that can be used overall by your cluster.
  • Define GPU needs per application: specify how many GPUs your app requires. Qovery handles provisioning, scheduling, and placement for you.

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Qovery's Provision (our Kubernetes deployment product) automatically takes care of:

  • NVIDIA plugin setup: Qovery installs and configures the NVIDIA Kubernetes Device Plugin automatically, so your applications can access GPUs right away.
  • Cost optimization: Qovery selects the most cost-effective instance type combination based on your workload’s GPU requirements, thanks to the Karpenter implementation.

What it used to look like (before Qovery)

Before this release, setting up GPU workloads on Kubernetes meant doing everything yourself:

  1. Manually define and deploy GPU node pools
    • Create YAML or CLI definitions for GPU-capable nodes, labels, taints, and autoscaling.
    • Configure Spot instance handling and scaling behavior.
  2. Install NVIDIA components
    • Add the NVIDIA Helm charts for the device plugin and drivers.
    • Manage chart values, version compatibility, and updates across clusters.
  3. Modify application manifests
    • Add resources.requests/limits for nvidia.com/gpu.
    • Set node selectors, tolerations, and affinities for GPU nodes.
    • Tune and redeploy Helm charts for GPU access.
  4. Maintain and optimize over time
    • Monitor GPU utilization and keep costs under control.
    • Update plugins and drivers as Kubernetes versions evolve.

Now, Qovery does all that for you.

Why this matters

GPU workloads are core to modern applications but Kubernetes wasn’t built to make GPU management simple.

Qovery bridges that gap by abstracting away the complexity. In just a few clicks, your applications can access powerful GPUs, without the DevOps overhead.

Get started

You can start using GPU node provisioning today.

Check out our documentation to learn how to enable GPU support for your clusters and applications.

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