Cost-Effective Dynamic Scaling for AI Workloads on AWS EKS

In this day and age, when AI governs everything, being agile with workload management has become crucial to guarantee peak performance and cost-effectiveness. The need for dynamic scaling solutions that optimize infrastructure utilization efficiently without driving up costs becomes increasingly prominent as demand continues to fluctuate. Kubernetes, indeed, acts as a powerful framework for managing containerized applications.

Moreover, by integrating Kubernetes with intelligent scaling software, such as Karpenter, it will be a game-changing combination. Karpenter autosizes the cluster automatically according to the needs of your AI workloads. This ensures that you’re using only the resources you actually need, avoiding unnecessary expenses while still maintaining high availability and performance.

Karpenter’s ability to react in real time to workload fluctuations enables your infrastructure to scale dynamically and more efficiently. Whether it’s scaling up during peak demand or scaling down when usage decreases, Karpenter helps optimize your cloud infrastructure, making it the perfect fit for AI-driven workloads where demand can shift unpredictably.

This blog takes you through the architecture and steps needed to achieve cost-effective dynamic scaling for AI workloads using Kubernetes, with Karpenter at the core. You’ll learn how to maximize resource efficiency, reduce cloud expenses, and maintain performance and availability—all essential aspects for running AI workloads at scale.
Here is a simple overview of how the Karpenter utility will be helpful to dynamically allocate a GPU.

Prerequisites:

Before starting with dynamic scaling, make sure the following components are set up:

- AWS CLI install and configure
- Existing EKS cluster
- Existing vpc, subnets and security groups
- Create an IAM OIDC provider for Kubernetes service accounts
Now, to setup automatic GPU node provisioning facility by having authorized access to AWS cloud services from Kubernetes workload, it is required to create IAM roles with the required IAM policies. Here, the IAM roles and IAM policies are created using a command line method for ease of use. However, the AWS console can be used for the same actions.
Prior to moving on, confirm that the following environment variables are set.

				
					KARPENTER_NAMESPACE=kube-system
CLUSTER_NAME=<cluster-name>
AWS_PARTITION="aws" # if you are not using standard partitions, you may need to configure to aws-cn / aws-us-gov
AWS_REGION="$(aws configure list | grep region | tr -s " " | cut -d" " -f3)"
OIDC_ENDPOINT="$(aws eks describe-cluster --name "${CLUSTER_NAME}" \
    --query "cluster.identity.oidc.issuer" --output text)"
AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query 'Account' \
    --output text)
K8S_VERSION=1.30
GPU_AMI_ID="$(aws ssm get-parameter --name /aws/service/eks/optimized-ami/${K8S_VERSION}/amazon-linux-2-gpu/recommended/image_id --query Parameter.Value --output text)"

Step 1: Creating an AWS IAM role

IAM role for GPU worker node:
Starting with creation of IAM role, one will be utilized by the Karpenter controller to manage the autoscaling instances, while the other will be linked to the GPU worker nodes.

				
					echo '{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "ec2.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}' > node-trust-policy.json

aws iam create-role --role-name "KarpenterNodeRole-${CLUSTER_NAME}" \
    --assume-role-policy-document file://node-trust-policy.json

Follow the commands below to attach the policy to the Karpenter node role.

				
					aws iam attach-role-policy --role-name "KarpenterNodeRole-${CLUSTER_NAME}" \
    --policy-arn "arn:${AWS_PARTITION}:iam::aws:policy/AmazonEKSWorkerNodePolicy"

aws iam attach-role-policy --role-name "KarpenterNodeRole-${CLUSTER_NAME}" \
    --policy-arn "arn:${AWS_PARTITION}:iam::aws:policy/AmazonEKS_CNI_Policy"

aws iam attach-role-policy --role-name "KarpenterNodeRole-${CLUSTER_NAME}" \
    --policy-arn "arn:${AWS_PARTITION}:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly"

aws iam attach-role-policy --role-name "KarpenterNodeRole-${CLUSTER_NAME}" \
    --policy-arn "arn:${AWS_PARTITION}:iam::aws:policy/AmazonSSMManagedInstanceCore"

IAM role for Karpenter controller:
To create an IAM Roles for Service Accounts(IRSA) for the Karpenter controller with an OIDC endpoint, use the following policy and command. Here, the trust policy will make sure that the access is provided to a certain identity and that no other principle can take on this IAM role.

				
					cat << EOF > controller-trust-policy.json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Federated": "arn:${AWS_PARTITION}:iam::${AWS_ACCOUNT_ID}:oidc-provider/${OIDC_ENDPOINT#*//}"
            },
            "Action": "sts:AssumeRoleWithWebIdentity",
            "Condition": {
                "StringEquals": {
                    "${OIDC_ENDPOINT#*//}:aud": "sts.amazonaws.com",
                    "${OIDC_ENDPOINT#*//}:sub": "system:serviceaccount:${KARPENTER_NAMESPACE}:karpenter"
                }
            }
        }
    ]
}
EOF

aws iam create-role --role-name "KarpenterControllerRole-${CLUSTER_NAME}" \
    --assume-role-policy-document file://controller-trust-policy.json

cat << EOF > controller-policy.json
{
    "Statement": [
        {
            "Action": [
                "ssm:GetParameter",
                "ec2:DescribeImages",
                "ec2:RunInstances",
                "ec2:DescribeSubnets",
                "ec2:DescribeSecurityGroups",
                "ec2:DescribeLaunchTemplates",
                "ec2:DescribeInstances",
                "ec2:DescribeInstanceTypes",
                "ec2:DescribeInstanceTypeOfferings",
                "ec2:DeleteLaunchTemplate",
                "ec2:CreateTags",
                "ec2:CreateLaunchTemplate",
                "ec2:CreateFleet",
                "ec2:DescribeSpotPriceHistory",
                "pricing:GetProducts"
            ],
            "Effect": "Allow",
            "Resource": "*",
            "Sid": "Karpenter"
        },
        {
            "Action": "ec2:TerminateInstances",
            "Condition": {
                "StringLike": {
                    "ec2:ResourceTag/karpenter.sh/nodepool": "*"
                }
            },
            "Effect": "Allow",
            "Resource": "*",
            "Sid": "ConditionalEC2Termination"
        },
        {
            "Effect": "Allow",
            "Action": "iam:PassRole",
            "Resource": "arn:${AWS_PARTITION}:iam::${AWS_ACCOUNT_ID}:role/KarpenterNodeRole-${CLUSTER_NAME}",
            "Sid": "PassNodeIAMRole"
        },
        {
            "Effect": "Allow",
            "Action": "eks:DescribeCluster",
            "Resource": "arn:${AWS_PARTITION}:eks:${AWS_REGION}:${AWS_ACCOUNT_ID}:cluster/${CLUSTER_NAME}",
            "Sid": "EKSClusterEndpointLookup"
        },
        {
            "Sid": "AllowScopedInstanceProfileCreationActions",
            "Effect": "Allow",
            "Resource": "*",
            "Action": [
            "iam:CreateInstanceProfile"
            ],
            "Condition": {
            "StringEquals": {
                "aws:RequestTag/kubernetes.io/cluster/${CLUSTER_NAME}": "owned",
                "aws:RequestTag/topology.kubernetes.io/region": "${AWS_REGION}"
            },
            "StringLike": {
                "aws:RequestTag/karpenter.k8s.aws/ec2nodeclass": "*"
            }
            }
        },
        {
            "Sid": "AllowScopedInstanceProfileTagActions",
            "Effect": "Allow",
            "Resource": "*",
            "Action": [
            "iam:TagInstanceProfile"
            ],
            "Condition": {
            "StringEquals": {
                "aws:ResourceTag/kubernetes.io/cluster/${CLUSTER_NAME}": "owned",
                "aws:ResourceTag/topology.kubernetes.io/region": "${AWS_REGION}",
                "aws:RequestTag/kubernetes.io/cluster/${CLUSTER_NAME}": "owned",
                "aws:RequestTag/topology.kubernetes.io/region": "${AWS_REGION}"
            },
            "StringLike": {
                "aws:ResourceTag/karpenter.k8s.aws/ec2nodeclass": "*",
                "aws:RequestTag/karpenter.k8s.aws/ec2nodeclass": "*"
            }
            }
        },
        {
            "Sid": "AllowScopedInstanceProfileActions",
            "Effect": "Allow",
            "Resource": "*",
            "Action": [
            "iam:AddRoleToInstanceProfile",
            "iam:RemoveRoleFromInstanceProfile",
            "iam:DeleteInstanceProfile"
            ],
            "Condition": {
            "StringEquals": {
                "aws:ResourceTag/kubernetes.io/cluster/${CLUSTER_NAME}": "owned",
                "aws:ResourceTag/topology.kubernetes.io/region": "${AWS_REGION}"
            },
            "StringLike": {
                "aws:ResourceTag/karpenter.k8s.aws/ec2nodeclass": "*"
            }
            }
        },
        {
            "Sid": "AllowInstanceProfileReadActions",
            "Effect": "Allow",
            "Resource": "*",
            "Action": "iam:GetInstanceProfile"
        }
    ],
    "Version": "2012-10-17"
}
EOF

aws iam put-role-policy --role-name "KarpenterControllerRole-${CLUSTER_NAME}" \
    --policy-name "KarpenterControllerPolicy-${CLUSTER_NAME}" \
    --policy-document file://controller-policy.json

Step 2: Adding tags to subnets and security groups

To ensure that Karpenter can identify which subnet to consider when provisioning GPU worker nodes for AI workloads, it is mandatory to add the appropriate tags to the EKS node group subnet.

				
					for NODEGROUP in $(aws eks list-nodegroups --cluster-name "${CLUSTER_NAME}" --query 'nodegroups' --output text); do
    aws ec2 create-tags \
        --tags "Key=karpenter.sh/discovery,Value=${CLUSTER_NAME}" \
        --resources $(aws eks describe-nodegroup --cluster-name "${CLUSTER_NAME}" \
        --nodegroup-name "${NODEGROUP}" --query 'nodegroup.subnets' --output text )
done

Moving forward to tag security groups, the command below add tags only to the security groups associated with the first node group in the EKS cluster. However, the AWS console can be used if requires to includes multiple security groups or node groups with different security groups.

				
					NODEGROUP=$(aws eks list-nodegroups --cluster-name "${CLUSTER_NAME}" \
    --query 'nodegroups[0]' --output text)

LAUNCH_TEMPLATE=$(aws eks describe-nodegroup --cluster-name "${CLUSTER_NAME}" \
    --nodegroup-name "${NODEGROUP}" --query 'nodegroup.launchTemplate.{id:id,version:version}' \
    --output text | tr -s "\t" ",")

# If your EKS setup is configured to use only Cluster security group, then please execute -

SECURITY_GROUPS=$(aws eks describe-cluster \
    --name "${CLUSTER_NAME}" --query "cluster.resourcesVpcConfig.clusterSecurityGroupId" --output text)

# If your setup uses the security groups in the Launch template of a managed node group, then :

SECURITY_GROUPS="$(aws ec2 describe-launch-template-versions \
    --launch-template-id "${LAUNCH_TEMPLATE%,*}" --versions "${LAUNCH_TEMPLATE#*,}" \
    --query 'LaunchTemplateVersions[0].LaunchTemplateData.[NetworkInterfaces[0].Groups||SecurityGroupIds]' \
    --output text)"

aws ec2 create-tags \
    --tags "Key=karpenter.sh/discovery,Value=${CLUSTER_NAME}" \
    --resources "${SECURITY_GROUPS}"

The tags that must be present in subnets and security groups are as follows:

				
					Key=karpenter.sh/discovery | Value=${CLUSTER_NAME}

Step 3: Setup EKS Cluster access for GPU instance:

To allow GPU nodes to join the EKS cluster, the IAM role (created above for GPU) must be whitelisted in the security configMap set by EKS service. Follow the steps to modify this ConfigMap and allow the access for GPU instances:

				
					kubectl edit configmap aws-auth -n kube-system

				
					- groups:
  - system:bootstrappers
  - system:nodes
  rolearn: arn:${AWS_PARTITION}:iam::${AWS_ACCOUNT_ID}:role/KarpenterNodeRole-${CLUSTER_NAME}
  username: system:node:{{EC2PrivateDNSName}}

Step 4: Deploying Karpenter controller in EKS with helm chart

To set the Karpenter release and generate the deployment YAML from the Helm chart, follow these commands:

				
					export KARPENTER_VERSION="1.0.1"

				
					helm template karpenter oci://public.ecr.aws/karpenter/karpenter --version "${KARPENTER_VERSION}" --namespace "${KARPENTER_NAMESPACE}" \
    --set "settings.clusterName=${CLUSTER_NAME}" \
    --set "serviceAccount.annotations.eks\.amazonaws\.com/role-arn=arn:${AWS_PARTITION}:iam::${AWS_ACCOUNT_ID}:role/KarpenterControllerRole-${CLUSTER_NAME}" \
    --set controller.resources.requests.cpu=1 \
    --set controller.resources.requests.memory=1Gi \
    --set controller.resources.limits.cpu=1 \
    --set controller.resources.limits.memory=1Gi > karpenter.yaml

Set node affinity:

To ensure Karpenter runs on one of the existing node group nodes, modify the value to match your $NODEGROUP name.

				
					affinity:
  nodeAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
      nodeSelectorTerms:
      - matchExpressions:
        - key: karpenter.sh/nodepool
          operator: DoesNotExist
        - key: eks.amazonaws.com/nodegroup
          operator: In
          values:
          - ${NODEGROUP}
  podAntiAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
      - topologyKey: "kubernetes.io/hostname"

Now, let’s create the Karpenter namespace, the NodePool CRD, and then deploy the rest of the Karpenter resources.

				
					kubectl create namespace "${KARPENTER_NAMESPACE}" || true
kubectl create -f \
    "https://raw.githubusercontent.com/aws/karpenter-provider-aws/v${KARPENTER_VERSION}/pkg/apis/crds/karpenter.sh_nodepools.yaml"
kubectl create -f \
    "https://raw.githubusercontent.com/aws/karpenter-provider-aws/v${KARPENTER_VERSION}/pkg/apis/crds/karpenter.k8s.aws_ec2nodeclasses.yaml"
kubectl create -f \
    "https://raw.githubusercontent.com/aws/karpenter-provider-aws/v${KARPENTER_VERSION}/pkg/apis/crds/karpenter.sh_nodeclaims.yaml"
kubectl apply -f karpenter.yaml

Step 5: Create NodePool and EC2NodeClass for Karpenter controller

Now, let’s create the NodePool and EC2NodeClass. Based on this YAML configuration, a new node will be created. Set the disruption policy as per your requirements to define the different ways Karpenter disrupts nodes.

				
					cat <<EOF | envsubst | kubectl apply -f -
apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: default
spec:
  template:
    spec:
      requirements:
        - key: node.kubernetes.io/instance-type
          operator: In
          values: ["g4dn.xlarge"]
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot"]
  disruption:
    consolidateAfter: 1m
    consolidationPolicy: WhenEmpty
---
apiVersion: karpenter.k8s.aws/v1
kind: EC2NodeClass
metadata:
  name: default
spec:
  amiFamily: AL2 # Amazon Linux 2
  role: "KarpenterNodeRole-${CLUSTER_NAME}" # replace with your cluster name
  subnetSelectorTerms:
    - tags:
        karpenter.sh/discovery: "${CLUSTER_NAME}" # replace with your cluster name
  securityGroupSelectorTerms:
    - tags:
        karpenter.sh/discovery: "${CLUSTER_NAME}" # replace with your cluster name
  amiSelectorTerms:
    - id: "${GPU_AMI_ID}" # <- GPU Optimized AMD AMI 
    - name: "amazon-eks-node-${K8S_VERSION}-*" # <- automatically upgrade when a new AL2 EKS Optimized AMI is released. This is unsafe for production workloads. Validate AMIs in lower environments before deploying them to production.
EOF

Step 6: Enabling support for GPU resource type

The NVIDIA device plugin for Kubernetes help automatically exposes GPUs on nodes, monitors their health, and enables GPU-accelerated containers in Kubernetes cluster. The DaemonSet will be deployed by using the command below. Afterward, NVIDIA GPUs can be identified by Kubernetes environment and can be requested by a pod using the limit resource type.

				
					kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.16.1/deployments/static/nvidia-device-plugin.yml

Before deploying the AI workload ensure the below annotation is set under resource limit in Kubernetes AI manifest.

				
					
resources:
  limits:
    nvidia.com/gpu: "1"

Step 7: Deploying the AI workload in EKS Cluster

Once everything is in place, deploy the AI workload. The AI pod will remain in a pending state until the GPU instance arrives. And while describing the pod, the update can be seen as follows.

				
					Events:
  Type     Reason            Age                  From               Message
  ----     ------            ----                 ----               -------
  Warning  FailedScheduling  1m10s                default-scheduler  0/1 nodes are available: 1 Insufficient nvidia.com/gpu. preemption: 0/1 nodes are available: 1 No preemption victims found for incoming pod..
  Normal   Nominated         50s                  karpenter          Pod should schedule on: nodeclaim/gpu-node-47x98

To check what is happening behind the scenes when the AI pod is in the pending state, it can be determined by looking at the Karpenter Controller pod’s logs.

				
					kubectl logs -f -l app.kubernetes.io/instance=karpenter -n kube-system

As soon as the GPU node joins the cluster, the following logs can be seen in the Karpenter pod.

				
					{"level":"INFO","time":"2024-09-17T12:18:45.080Z","logger":"controller","message":"found provisionable pod(s)","commit":"490ef94","controller":"provisioner","Pods":"default/ai-workload","duration":"36.342563ms"}
{"level":"INFO","time":"2024-09-17T12:18:45.080Z","logger":"controller","message":"computed new nodeclaim(s) to fit pod(s)","commit":"490ef94","controller":"provisioner","nodeclaims":1,"pods":1}
{"level":"INFO","time":"2024-09-17T12:18:45.091Z","logger":"controller","message":"created nodeclaim","commit":"490ef94","controller":"provisioner","NodePool":{"name":"gpu-node"},"NodeClaim":{"name":"gpu-node-dtd6f"},"requests":{"cpu":"3100m","memory":"12338Mi","nvidia.com/gpu":"1","pods":"6"},"instance-types":"g4dn.xlarge"}
{"level":"DEBUG","time":"2024-09-17T12:18:45.251Z","logger":"controller","message":"created launch template","commit":"490ef94","controller":"nodeclaim.lifecycle","controllerGroup":"karpenter.sh","controllerKind":"NodeClaim","NodeClaim":{"name":"gpu-node-dtd6f"},"namespace":"","name":"gpu-node-dtd6f","reconcileID":"26f84d62-0f25-4aca-ab62-bd7113626aaa","launch-template-name":"karpenter.k8s.aws/10360022445544704430","id":"lt-07c834bed674458e6"}
{"level":"INFO","time":"2024-09-17T12:18:46.653Z","logger":"controller","message":"launched nodeclaim","commit":"490ef94","controller":"nodeclaim.lifecycle","controllerGroup":"karpenter.sh","controllerKind":"NodeClaim","NodeClaim":{"name":"gpu-node-dtd6f"},"namespace":"","name":"gpu-node-dtd6f","reconcileID":"26f84d62-0f25-4aca-ab62-bd7113626aaa","provider-id":"aws:///aws-region/instance-id","instance-type":"g4dn.xlarge","zone":"aws-az","capacity-type":"on-demand","allocatable":{"cpu":"3920m","ephemeral-storage":"17Gi","memory":"14481Mi","nvidia.com/gpu":"1","pods":"29","vpc.amazonaws.com/pod-eni":"39"}}
{"level":"DEBUG","time":"2024-09-17T12:20:40.897Z","logger":"controller","message":"deleted launch template","commit":"490ef94","id":"lt-07c834bed674458e6","name":"karpenter.k8s.aws/10360022445544704430"}
{"level":"INFO","time":"2024-09-17T12:22:07.305Z","logger":"controller","message":"registered nodeclaim","commit":"490ef94","controller":"nodeclaim.lifecycle","controllerGroup":"karpenter.sh","controllerKind":"NodeClaim","NodeClaim":{"name":"gpu-node-dtd6f"},"namespace":"","name":"gpu-node-dtd6f","reconcileID":"eebb8f81-9086-4aae-b026-ccda37ab3841","provider-id":"aws:///aws-region/instance-id","Node":{"name":"ip-address"}}
{"level":"INFO","time":"2024-09-17T12:22:07.347Z","logger":"controller","message":"initialized nodeclaim","commit":"490ef94","controller":"nodeclaim.lifecycle","controllerGroup":"karpenter.sh","controllerKind":"NodeClaim","NodeClaim":{"name":"gpu-node-dtd6f"},"namespace":"","name":"gpu-node-dtd6f","reconcileID":"eebb8f81-9086-4aae-b026-ccda37ab3841","provider-id":"aws:///aws-region/instance-id","Node":{"name":"ip-address"},"allocatable":{"cpu":"4","ephemeral-storage":"262232369538","hugepages-1Gi":"0","hugepages-2Mi":"0","memory":"15988508Ki","nvidia.com/gpu":"1","pods":"110"}}

The user will need to play with the configuration to fine-tune the AI workflow, to ensure that the GPU instances terminate as soon as the they become idle.

				
					
    disruption:
      consolidateAfter: 1m
      consolidationPolicy: WhenEmpty

Once the AI workload is completed, the GPU node will be terminated according to the disruption policy set in the node pool, and all events can be monitored from the Karpenter pod logs.

				
					{"level":"DEBUG","time":"2024-09-17T12:26:07.758Z","logger":"controller","message":"marking empty","commit":"490ef94","controller":"nodeclaim.disruption","controllerGroup":"karpenter.sh","controllerKind":"NodeClaim","NodeClaim":{"name":"gpu-node-dtd6f"},"namespace":"","name":"gpu-node-dtd6f","reconcileID":"43b6cdb3-2266-44e1-bcba-61d4908f97f2"} 
{"level":"DEBUG","time":"2024-09-17T12:27:45.001Z","logger":"controller","message":"marking expired","commit":"490ef94","controller":"nodeclaim.disruption","controllerGroup":"karpenter.sh","controllerKind":"NodeClaim","NodeClaim":{"name":"gpu-node-dtd6f"},"namespace":"","name":"gpu-node-dtd6f","reconcileID":"c1269dbf-0ffb-44d4-802a-80078a267b67"}
{"level":"INFO","time":"2024-09-17T12:27:52.766Z","logger":"controller","message":"disrupting via expiration delete, terminating 1 nodes (0 pods) ip-address/g4dn.xlarge/on-demand","commit":"490ef94","controller":"disruption","command-id":"6fda1b9b-3dba-48c6-a8d6-0c0f4bf65ff3"}
{"level":"INFO","time":"2024-09-17T12:27:53.284Z","logger":"controller","message":"command succeeded","commit":"490ef94","controller":"disruption.queue","command-id":"6fda1b9b-3dba-48c6-a8d6-0c0f4bf65ff3"}
{"level":"INFO","time":"2024-09-17T12:27:53.314Z","logger":"controller","message":"tainted node","commit":"490ef94","controller":"node.termination","controllerGroup":"","controllerKind":"Node","Node":{"name":"ip-address"},"namespace":"","name":"ip-address","reconcileID":"81db31c9-79b2-4807-9434-5218b01d78b3"}
{"level":"INFO","time":"2024-09-17T12:27:56.684Z","logger":"controller","message":"deleted node","commit":"490ef94","controller":"node.termination","controllerGroup":"","controllerKind":"Node","Node":{"name":"ip-address"},"namespace":"","name":"ip-address","reconcileID":"85463ab4-28f0-478b-b505-179bbbd41267"}
{"level":"INFO","time":"2024-09-17T12:27:57.145Z","logger":"controller","message":"deleted nodeclaim","commit":"490ef94","controller":"nodeclaim.termination","controllerGroup":"karpenter.sh","controllerKind":"NodeClaim","NodeClaim":{"name":"gpu-node-dtd6f"},"namespace":"","name":"gpu-node-dtd6f","reconcileID":"29e81c31-9739-4c19-952e-dc7df5f43086","Node":{"name":"ip-address"},"provider-id":"aws:///aws-region/instance-id"}

Conclusion

Successfully managing an AI project in Kubernetes requires a dynamic and automated approach to resource scaling. By using Karpenter, you can ensure that your infrastructure adapts to the requirements of AI models in real time, while improving efficiency and reducing costs. This seamless integration empowers you to focus on innovation, knowing that your AI work is always supported by the right resources at the right time.