KAI Scheduler | NVIDIA Cloud Functions

KAI Scheduler is an open source Kubernetes scheduler for AI workloads. NVCF integrates with KAI to bin-pack GPU workloads onto eligible nodes to help improve cluster utilization.

KAI coexists with the default Kubernetes scheduler. When the KAIScheduler feature gate is enabled, the NVIDIA Cluster Agent (NVCA) assigns NVCF workload Pods to KAI. Other Pods can continue using the default scheduler.

Operational responsibilities

The platform operator owns the KAI installation and lifecycle. NVCA manages its integration with KAI, not the KAI service itself.

Component	Responsibility
Platform operator	Install and configure KAI, enable it for NVCF, and manage monitoring, availability, and upgrades.
NVCA	Create NVCF workloads and assign their scheduler and queue.
KAI Scheduler	Select eligible nodes and schedule the workload Pods.

The compute plane stack can automate KAI installation and configuration. This does not transfer lifecycle ownership to NVCA.

Install KAI Scheduler

Use a tested KAI Scheduler release that is compatible with your NVCF compute plane stack.

Use the compute plane stack

Set the following in your nvcf-compute-plane Helmfile environment:

addons:
  kaiScheduler:
    enabled: true

The add-on installs KAI as release and namespace kai-scheduler, configures its default queues, and enables NVCA’s KAIScheduler feature gate unless explicitly disabled with -KAIScheduler. Apply the environment through your compute plane installation workflow. Skip the manual installation below.

Use an existing or separately managed installation

If KAI is managed outside the compute plane stack, leave the installation add-on disabled and configure KAI with the values below. Do not install a second KAI release.

NVCA expects a parent queue named default-parent-queue and a child queue named default-queue. Other queues may also exist.

Set unlimited (-1) quotas and limits on the queues used for NVCF workloads. NVCA relies on this configuration for cluster capacity and usage tracking.

If NVCF and non-NVCF workloads share a cluster with limited KAI queues, enable Shared Cluster mode so NVCA excludes non-NVCF nodes from capacity tracking and scheduling. Nodes running NVCF workloads must be labeled nvca.nvcf.nvidia.io/schedule=true.

Create values.yaml with the required scheduler and queue settings:

values.yaml

scheduler:
  placementStrategy: binpack
  plugins:
    nodeplacement:
      arguments:
        gpu: binpack
        cpu: spread
  actions:
    preempt:
      enabled: false
    consolidation:
      enabled: false
defaultQueue:
  createDefaultQueue: true
  parentName: default-parent-queue
  childName: default-queue
  parentResources:
    cpu:
      quota: -1
      limit: -1
      overQuotaWeight: 1
    gpu:
      quota: -1
      limit: -1
      overQuotaWeight: 1
    memory:
      quota: -1
      limit: -1
      overQuotaWeight: 1
  childResources:
    cpu:
      quota: -1
      limit: -1
      overQuotaWeight: 1
    gpu:
      quota: -1
      limit: -1
      overQuotaWeight: 1
    memory:
      quota: -1
      limit: -1
      overQuotaWeight: 1

For a new installation, replace <kai-version> with the version of the kai-scheduler release in deploy/stacks/nvcf-compute-plane/helmfile.d/01-dependencies.yaml.gotmpl. Read this file from your selected compute plane release tag, not from main.

helm install kai-scheduler \
  oci://ghcr.io/kai-scheduler/kai-scheduler/kai-scheduler \
  --namespace kai-scheduler --create-namespace \
  --version <kai-version> -f values.yaml

For an existing KAI release, apply the same values with helm upgrade:

helm upgrade kai-scheduler \
  oci://ghcr.io/kai-scheduler/kai-scheduler/kai-scheduler \
  --namespace kai-scheduler \
  --version <kai-version> -f values.yaml

After KAI is ready, add KAIScheduler to the existing NVCA feature-gate list. For standalone NVCA Helm values, use selfManaged.featureGateValues. For compute plane environment values, use global.nvcaOperator.selfManaged.featureGateValues. Preserve the other feature gates when updating the list. See Managing Feature Flags.

Installing KAI alone does not enable NVCF to use it. Both the KAI installation and the NVCA feature gate are required.

Verify the integration

Check the KAI components and default queues in the compute cluster:

kubectl get pods -n kai-scheduler
kubectl get queues.scheduling.run.ai default-parent-queue default-queue -o yaml

After deploying a function, inspect its workload Pods:

kubectl get pods -n <workload-namespace> \
  -o custom-columns='NAME:.metadata.name,SCHEDULER:.spec.schedulerName,NODE:.spec.nodeName' \
  -L kai.scheduler/queue

New NVCF workload Pods should use kai-scheduler and default-queue. If a Pod remains Pending, check its events, KAI component health, queue configuration, and available resources on eligible nodes.

Maintain KAI Scheduler

Monitor KAI component readiness and pending workload Pods.
Validate KAI upgrades with your NVCA and compute plane versions before applying them to production. Follow the selected KAI release’s upgrade guidance.
After an upgrade, verify the queues and deploy a test function to confirm scheduling, readiness, and invocation.

Pods assigned to KAI do not automatically fall back to the default scheduler. If KAI is unavailable, new or replacement workload Pods can remain pending. The platform operator is responsible for restoring KAI availability.

Schedule multi-Pod workloads

KAI can hold a multi-Pod workload until all required members fit. Grove and Dynamo build on this behavior for multi-role inference services. See Gang Scheduling for add-on configuration, workload examples, supported resource types, and troubleshooting.

On NVLink-optimized clusters, KAI can also place the complete gang in one GPU clique. See Topology-Aware Scheduling for GPU DRA prerequisites, topology configuration, Grove bindings, and function examples.