NVIDIA Network Operator on Kubernetes: Unlocking AI/ML Performance at Scale

AI doesn’t just need powerful GPUs — it needs them to talk fast.

While the race to train and deploy cutting-edge AI/ML models continues, an often-overlooked bottleneck quietly slows things down: network throughput. Especially when models get distributed across GPUs or nodes, and the data they crunch grows large and complex, interconnect latency and bandwidth can be the difference between waiting hours or seconds.

Enter the NVIDIA Network Operator — a silent enabler in high-performance AI infrastructure.

What is the NVIDIA Network Operator?

In simple terms, the NVIDIA Network Operator is a Kubernetes-native way to automate and manage networking components for GPU workloads that need high-speed, low-latency networking – particularly RDMA (Remote Direct Memory Access) via NVIDIA’s ConnectX SmartNICs.

It builds on top of Kubernetes Operators to manage:

Mellanox OFED drivers
RDMA Shared Device Plugin
Multus CNI and IPoIB
Secondary network interfaces for AI/ML workloads

Its purpose? Offload and optimize network traffic so that your AI models spend less time waiting for data and more time doing the math.

Why Does It Matter for AI/ML on Kubernetes?

Let’s break it down:

Distributed Training: Models like GPT, BERT, or large-scale vision models are often trained across multiple GPUs or even nodes. Efficient inter-GPU communication over high-speed networks (e.g., using NCCL over RDMA) is crucial.
Inference at Edge or Scale: Inference jobs may require multiple pods accessing large shared models. Faster data access = lower latency inference.
Less Overhead, More Output: By offloading networking to SmartNICs and reducing CPU load, the GPU can stay fully utilized — increasing cost-efficiency.
Kubernetes-Native: It integrates seamlessly into existing K8s environments, ensuring less operational friction and better lifecycle management.

Deploying NVIDIA Network Operator with Helm

Prerequisites

Kubernetes cluster (v1.30+ recommended)
Helm 3.x
NVIDIA GPUs with compatible ConnectX SmartNICs
NVIDIA GPU Operator already deployed (for full stack optimization)
Multus CNI support enabled

Step 1: Add NVIDIA Helm repository

				
					$ helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
$ helm repo update

Step 2: Install the NVIDIA Network Operator Helm chart

Before proceeding with the Helm chart installation, it’s recommend reviewing the official documentation on customizing Helm values. This ensures everything is configured as per your specific requirements.

				
					# Deploy without customization
helm install --wait --generate-name \
     -n nvidia-network-operator --create-namespace \
     nvidia/network-operator

# Deploy with customized helm values
$ helm install -f ./values.yaml -n nvidia-network-operator --create-namespace \
    --wait nvidia/network-operator network-operator

By default, this deploys the MOFED drivers, the Network Operator CRDs and sets up RDMA plugin and Multus support.

Step 3: Verify the Network Operator installation

				
					kubectl get pods -n nvidia-network-operator
kubectl get networkoperator -A

You should see pods like:

network-operator-<controller-revision-hash>
network-operator-<controller-revision-hash>-node-feature-discovery-gc
network-operator-<controller-revision-hash>-node-feature-discovery-master
network-operator-<controller-revision-hash>-node-feature-discovery-worker

Best Practices for AI Workloads

To maximize the GPU resource efficiency for AI/ML workloads,

Use MPI or NCCL with RDMA support for distributed training.
Leverage CUDA-aware libraries that work with RDMA-enabled networks.
Enable secondary interfaces using Multus in your pod specs to isolate data and control planes.
Monitor GPU and network metrics with DCGM Exporter and Prometheus/Grafana.

Final Thoughts

While flashy AI models often steal the spotlight, infrastructure plumbing matters just as much. Tools like the NVIDIA Network Operator empower your Kubernetes clusters to truly operate like AI-first infrastructure — where speed, scale, and cost-efficiency are not trade-offs, but defaults.

So the next time your training jobs take too long, or your inference pipeline lags — maybe it’s not the model’s fault. Maybe your GPUs are just waiting for data. Let them run free.

Curious about integrating the NVIDIA Network Operator into your AI/ML stack or looking to fine-tune your GPU infrastructure for maximum performance and scale? Let’s connect — I’d be glad to explore solutions tailored to your environment.

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