CoreWeave Kubernetes Guide (2026): GPU on K8s

CoreWeave offers a robust Kubernetes (K8s) platform tailored for GPU workloads, making it a preferred choice for AI engineers and data scientists. This guide will walk you through the essential steps to deploy and manage GPU resources effectively on CoreWeave Kubernetes, ensuring you maximize performance while minimizing costs.

Understanding CoreWeave Kubernetes

CoreWeave Kubernetes is built on the premise of scalability and flexibility, allowing users to orchestrate GPU-based workloads seamlessly. With GPU support integrated into Kubernetes, users can leverage advanced scheduling and resource management capabilities.

Key Features of CoreWeave Kubernetes

• Scalable Architecture: Easily scale your GPU resources based on workload demands.
• Cost Efficiency: Competitive pricing starting from $1.25 per hour.
• Advanced GPU Scheduling: Optimize GPU utilization through Kubernetes’ scheduling features.
• Multi-Cloud Strategy: Integrate with other cloud services or deploy in a hybrid environment.

Setting Up Your CoreWeave Kubernetes Cluster

To get started with CoreWeave Kubernetes, follow these steps:

Step 1: Create Your CoreWeave Account

1. Visit CoreWeave and sign up for an account.
2. Choose your pricing plan based on your expected usage. CoreWeave starts at $1.25/h, providing access to various GPU types.

Step 2: Deploy Your Kubernetes Cluster

1. Navigate to the CoreWeave dashboard and select the option to create a new Kubernetes cluster.
2. Configure your cluster parameters, including node types and number of nodes, ensuring you select GPU-enabled nodes.
3. Launch the cluster and wait for it to initialize.

Step 3: Install Kubernetes CLI

To interact with your Kubernetes cluster, install the Kubernetes command-line interface (CLI):

curl -LO "https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl"
chmod +x ./kubectl
sudo mv ./kubectl /usr/local/bin/kubectl

Step 4: Configure Access

Set up access to your cluster using the Kubeconfig file provided by CoreWeave:

export KUBECONFIG=path/to/kubeconfig

This file contains the necessary credentials to authenticate and manage your Kubernetes resources.

Deploying Your First GPU Workload

Now that your Kubernetes cluster is set up, you can deploy a GPU workload. Here’s a simple example of deploying a TensorFlow application that utilizes GPU resources.

Step 1: Create a Deployment YAML File

Create a file called tensorflow-gpu-deployment.yaml:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: tensorflow-gpu
spec:
  replicas: 1
  selector:
    matchLabels:
      app: tensorflow-gpu
  template:
    metadata:
      labels:
        app: tensorflow-gpu
    spec:
      containers:
      - name: tensorflow
        image: tensorflow/tensorflow:latest-gpu
        resources:
          limits:
            nvidia.com/gpu: 1

Step 2: Apply the Deployment

Run the following command to deploy your application:

kubectl apply -f tensorflow-gpu-deployment.yaml

Step 3: Monitor Your Deployment

You can monitor the status of your deployment using:

kubectl get pods

This command will show the running pods and their statuses.

Managing GPU Resources

Managing GPU resources effectively is crucial for optimizing performance and cost. Here are some best practices:

Resource Requests and Limits

Define resource requests and limits in your deployment configurations. This ensures Kubernetes efficiently schedules your pods based on available GPU resources.

Autoscaling

Utilize the Cluster Autoscaler to automatically adjust the number of nodes based on resource utilization. This helps in managing costs during low-demand periods.

Monitoring Tools

Integrate monitoring tools like Prometheus and Grafana to track GPU utilization and application performance. This can help identify bottlenecks and optimize your workloads further.

Comparison of GPU Cloud Providers

When considering GPU cloud providers, it’s essential to evaluate their offerings. Below is a comparison table highlighting various providers, including CoreWeave:

For a more detailed comparison, check out our full GPU cloud comparison.

FAQ

How do I choose the right GPU type for my workload on CoreWeave?

Choosing the right GPU type depends on your specific workload requirements. If you’re running deep learning models or complex simulations, select more powerful GPUs that can handle intensive computations. CoreWeave offers various options, ensuring you can find a suitable fit. Analyze your performance needs, consider the pricing, and use CoreWeave’s resources to identify the best GPU for your project.

Can I use CoreWeave Kubernetes for production workloads?

Yes, CoreWeave Kubernetes is designed to support production workloads. Its scalable architecture, combined with advanced scheduling and resource management, ensures that your applications can run efficiently. Make sure to implement best practices for resource allocation and monitoring to maintain optimal performance in a production environment.

What kind of support does CoreWeave offer for Kubernetes users?

CoreWeave provides comprehensive support for Kubernetes users, including detailed documentation, tutorials, and community forums. If you encounter specific issues, their support team can assist you with technical challenges, ensuring your GPU workloads run smoothly. Utilizing these resources can significantly enhance your experience and productivity when deploying on their platform.