NVCF Self-Hosted 0.6.0 Release Notes

NVCF Self-Hosted 0.6.0 is a stable release. It adds asynchronous task workloads, multi-cluster control-plane management, OpenAI-compatible LLM routing, single-mode Multi-Instance GPU (MIG) support, and control-plane observability. It also adds Early Access support for optional NVIDIA Dynamo and Grove operator integration.

Highlights

NVIDIA Cloud Tasks (NVCT)

NVIDIA Cloud Tasks (NVCT) adds an asynchronous workload type for run-to-completion GPU jobs. Tasks are the batch counterpart to Cloud Functions: functions stay online to serve invocation requests, while tasks run to completion and release GPU capacity when they finish.

Use NVCT for batch inference, fine-tuning, evaluation, solvers, data preparation, and other GPU workloads that do not require an always-on endpoint.

Multi-Cluster GPU Management

A single NVCF control plane can now register and manage multiple GPU clusters. Each cluster keeps its own identity and configuration, so operators can expand across regions, cloud providers, or on-premises environments without running a separate NVCF control plane for each cluster.

After registration, workloads can deploy to the available clusters through the same control-plane API.

Dynamo Operator Integration

NVCF can integrate with optional NVIDIA Dynamo and Grove operators installed on each GPU cluster. KAI Scheduler is a separate prerequisite for using these components.

This integration is Early Access and optional.

LLM API Gateway

The LLM API Gateway provides an OpenAI-compatible entry point for LLM workloads on NVCF. It reads the target function and model from the request’s model field, routes chat completions, Responses, and embeddings to the matching model backend, and applies per-model token rate limits.

When cache-aware routing is enabled, the gateway can keep multi-turn requests on the same backend for better cache locality.

Single-Mode MIG

NVCF can detect GPUs configured with the single MIG strategy and expose each MIG profile as an NVCF instance type. Workloads can target an isolated GPU slice instead of a full GPU, improving capacity use on supported nodes.

Control-Plane Observability

Control-plane observability is configured through one Helmfile configuration block. NVCF can export Prometheus-compatible metrics, control-plane logs, and optional OpenTelemetry Protocol (OTLP) traces to an existing observability backend, including in air-gapped environments.

Upgrade Notes

For an existing 0.5.x deployment, follow the 0.5.0 to 0.6.0 Upgrade procedure. It covers the required migration stops, full stack sync, and GPU cluster re-registration steps.

Customers with GPU clusters running NVIDIA Cluster Agent (NVCA) versions older than 2.51.0 must upgrade those clusters to NVCA 2.51.0 before moving to NVCA 3.x. NVCA 2.51.0 includes fixes for workload lifecycle management and GPU capacity accounting. Skipping this version can cause incorrect GPU resource accounting, untracked workload pods, or capacity mismatches between the GPU cluster and the control plane.

Check the current NVCA version:

$
kubectl get nvcfbackend -n nvca-operator

The VERSION column shows the deployed NVCA version.

Upgrade the NVCA operator to 2.51.0 before continuing to 3.x:

$
helm upgrade nvca-operator -n nvca-operator \
>
  --reuse-values \
>
  --wait \
>
  "${CHART_URL}" \
>
  --username='$oauthtoken' \
>
  --password="$(helm get values -n nvca-operator nvca-operator -o json | jq -r '.ngcConfig.serviceKey')" \
>
  --set helmManaged.nvcaVersion="2.51.0"

After the upgrade, verify that the cluster is healthy before proceeding:

$
kubectl get nvcfbackend -n nvca-operator
$
# Verify VERSION shows 2.51.0 and HEALTH shows healthy.