Overview

The NVIDIA Cluster Agent (NVCA) connects GPU clusters to the NVCF control plane, enabling them to act as deployment targets for Cloud Functions. NVCA is a function deployment orchestrator that registers a cluster’s GPU resources, communicates with the control plane, and manages the lifecycle of function deployments on GPU nodes.

After installing NVCA on a cluster:

• The registered cluster will show as a deployment option in the GET /v2/nvcf/clusterGroups API response.

• Any functions under the cluster’s authorized NCA IDs can now deploy on the cluster.

Authentication & Keys

Key Type	Description
NVCF API Key (NAK)	Also called Service Account Key (SAK). Used by NVCA to authenticate with the control plane. See API for details on API key generation.

Prerequisites

• Access to a Kubernetes cluster including GPU-enabled nodes (“GPU cluster”)

  • The cluster must have a compatible version of Kubernetes.

  • The cluster must have the NVIDIA GPU Operator installed.

    • If your cloud provider does not support the NVIDIA GPU Operator, Manual Instance Configuration is possible, but not recommended due to lack of maintainability.

    • To get the most out of clusters with multi-node NVLink (MNNVL) GPUs like GB200, the NVIDIA GPU DRA driver must be installed. See the NVLink-optimized Clusters for details.

    • For development or testing environments without physical GPUs, install the Fake GPU Operator (Development / Testing) instead.

• Registering the cluster requires kubectl and helm installed.

• The user registering the cluster must have the cluster-admin role privileges to install the NVIDIA Cluster Agent Operator (nvca-operator).

Supported Kubernetes Versions

• Minimum Kubernetes Supported Version: v1.25.0

• Maximum Kubernetes Supported Version v1.32.x

Considerations

• The NVIDIA Cluster Agent currently only supports caching if the cluster is enabled with StorageClass configurations. If the “Caching Support” capability is enabled, the agent will make the best effort by attempting to detect storage during deployments and fall back on non-cached workflows.

• Each function and task requires several infrastructure containers be deployed alongside workload containers. These infrastructure containers collectively need 6 CPU cores and 8 Gi of system memory to execute. Each GPU node must have at least this many resources, ideally significantly more for workload resource usage.