> ## Content Index > Fetch the complete content index at: https://devopscube.com/llms.txt > Use this file to discover other available public pages before exploring further. # Node Feature Discovery (NFD) on Kubernetes - URL: https://devopscube.com/node-feature-discovery/ - Published: 2025-10-31T12:21:10.000Z - Updated: 2025-10-31T12:21:10.000Z - Author: Bibin Wilson - Tags: Kubernetes, #blog, #syntax-highlight, HOW TO GUIDES In this blog, we will look at a Kubernetes feature called Node Feature Discovery. At the end of this blog, you will learn the following: - What is NFD in Kubernetes - NFD Architecture - Hands on NFD - Label Filtering to avoid label explosion. - Key Use Cases ## What is Node Feature Discovery? By default, every Kubernetes node comes with a few **basic built-in labels** that are automatically added by the **kubelet**. For example, 1. kubernetes.io/hostname= 2. kubernetes.io/os=linux 3. kubernetes.io/arch=amd64 If your cluster runs on a **cloud provider like AWS EKS**, you will also see extra labels that describe the cloud environment, such as: 1. topology.kubernetes.io/region=ap-south-1 2. topology.kubernetes.io/zone=ap-south-1a 3. node.kubernetes.io/instance-type=t3.medium These labels are useful for **scheduling**, **automation**, and **workload placement**. For instance, you can schedule a Pod to run only in a certain region or on a specific instance type. However, notice that these default labels donโ€™t include any details about the **hardware**, **kernel**, or **device features** of the node. Thats where **Node Feature Discovery (NFD)** comes in. **Node Feature Discovery** is a Kubernetes add-on that automatically detects and labels node-level features such as CPU flags, memory, kernel version, network adapters, and GPU capabilities. If you have used **Ansible facts** before, NFD works in a similar way. Just like Ansible gathers system information and provides it as metadata, **NFD collects system details** from Kubernetes nodes and exposes them as **node labels**. ## Node Feature Discovery Architecture Node Feature Discovery has 4 components: - nfd-master - nfd-worker - nfd-topology-updater - nfd-gc The workflow overview for Node Feature Discovery is given below. ![Kubernetes Node Feature Discovery Workflow](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2025/10/nfd.webp) Here is how it all works together. - **nfd-worker** runs as a DaemonSet on each node. It scans multiple sources (CPU, memory, PCI, etc.) and writes raw feature data into **NodeFeature** custom resources. - At regular intervals (default \~60s), workers update NodeFeature CRs to reflect changes. - **nfd-master** runs as a Deployment (usually a single replica). It watches these NodeFeature CRs, applies any filtering or rules, and updates the actual Node objects (labels, taints, extended resources) via the Kubernetes API. - **nfd-topology-updater** (if enabled) also runs as a DaemonSet. It collects NUMA, CPU, memory, and device topology from nodes and publishes NodeResourceTopology objects so that Topology Manager can do NUMA-aware scheduling. - **nfd-gc** (Garbage Collector) ensures that for any node that has left the cluster, its corresponding NodeFeature or NodeResourceTopology objects are removed. The default garbage collection interval is 1 hour. ## Prerequisites Below are the prerequisites required before going to the next section. - A running [Kubernetes cluster](https://devopscube.com/production-ready-kubernetes-cluster/) - [Helm](https://devopscube.com/create-helm-chart/) is installed in your system ## Install Node Feature Discovery We are going to install Node Feature Discovery using Helm. Before installing Node Feature Discovery, check the current labels on your node. ```bash kubectl get no --show-labels | grep multi-node-cluster-worker2 ``` I have given one of the node's names, update it to your nodes name before running the command. ```bash $ kubectl get no --show-labels | grep multi-node-cluster-worker2 multi-node-cluster-worker2 Ready 8m10s v1.33.0 beta.kubernetes.io/arch=amd64,beta.kubernetes.io/os=linux,kubernetes.io/arch=amd64,kubernetes.io/hostname=multi-node-cluster-worker2,kubernetes.io/os=linux ``` Now, run the following command to install Node Feature Discovery. ```bash helm install -n node-feature-discovery --create-namespace nfd oci://registry.k8s.io/nfd/charts/node-feature-discovery --version 0.18.1 ``` If you want to deploy the `topologyUpdater` pod, add the flags `--set topologyUpdater.createCRDs=true` and `--set topologyUpdater.enable=true` in the above command. For example, ```bash helm install -n node-feature-discovery --create-namespace nfd oci://registry.k8s.io/nfd/charts/node-feature-discovery --version 0.18.1 --set topologyUpdater.createCRDs=true --set topologyUpdater.enable=true ``` For other installation options, refer to the [official documentation](https://kubernetes-sigs.github.io/node-feature-discovery/v0.18/deployment/?ref=devopscube.com). Run the following command to check if the pods are created and running. ```bash kubectl get po -n node-feature-discovery ``` You will get the following output. ```bash NAME READY STATUS RESTARTS AGE nfd-node-feature-discovery-gc-b765d6879-drf85 1/1 Running 0 75s nfd-node-feature-discovery-master-78b957b755-jbncc 1/1 Running 0 75s nfd-node-feature-discovery-worker-9vgt7 1/1 Running 0 75s nfd-node-feature-discovery-worker-w476p 1/1 Running 0 75s ``` Once the pods start running, run the following command to check the nodes labels again. ```bash $ kubectl get no --show-labels | grep multi-node-cluster-worker2 multi-node-cluster-worker2 Ready 11m v1.33.0 beta.kubernetes.io/arch=amd64,beta.kubernetes.io/os=linux,feature.node.kubernetes.io/cpu-cpuid.ADX=true,feature.node.kubernetes.io/cpu-cpuid.AESNI=true,feature.node.kubernetes.io/cpu-cpuid.AVX2=true,feature.node.kubernetes.io/cpu-cpuid.AVX=true,feature.node.kubernetes.io/cpu-cpuid.CMPXCHG8=true,feature.node.kubernetes.io/cpu-cpuid.FMA3=true,feature.node.kubernetes.io/cpu-cpuid.FXSR=true,feature.node.kubernetes.io/cpu-cpuid.FXSROPT=true,feature.node.kubernetes.io/cpu-cpuid.HYPERVISOR=true,feature.node.kubernetes.io/cpu-cpuid.LAHF=true,feature.node.kubernetes.io/cpu-cpuid.MOVBE=true,feature.node.kubernetes.io/cpu-cpuid.OSXSAVE=true,feature.node.kubernetes.io/cpu-cpuid.SYSCALL=true,feature.node.kubernetes.io/cpu-cpuid.SYSEE=true,feature.node.kubernetes.io/cpu-cpuid.X87=true,feature.node.kubernetes.io/cpu-cpuid.XGETBV1=true,feature.node.kubernetes.io/cpu-cpuid.XSAVE=true,feature.node.kubernetes.io/cpu-cpuid.XSAVEC=true,feature.node.kubernetes.io/cpu-cpuid.XSAVEOPT=true,feature.node.kubernetes.io/cpu-hardware_multithreading=false,feature.node.kubernetes.io/cpu-model.family=6,feature.node.kubernetes.io/cpu-model.id=158,feature.node.kubernetes.io/cpu-model.vendor_id=Intel,feature.node.kubernetes.io/kernel-config.NO_HZ=true,feature.node.kubernetes.io/kernel-config.NO_HZ_FULL=true,feature.node.kubernetes.io/kernel-version.full=6.10.14-linuxkit,feature.node.kubernetes.io/kernel-version.major=6,feature.node.kubernetes.io/kernel-version.minor=10,feature.node.kubernetes.io/kernel-version.revision=14,feature.node.kubernetes.io/memory-swap=true,feature.node.kubernetes.io/storage-nonrotationaldisk=true,feature.node.kubernetes.io/system-os_release.ID=debian,feature.node.kubernetes.io/system-os_release.VERSION_ID.major=12,feature.node.kubernetes.io/system-os_release.VERSION_ID=12,kubernetes.io/arch=amd64,kubernetes.io/hostname=multi-node-cluster-worker2,kubernetes.io/os=linux ``` ## Advance Node Label Filtering By default NFD gets all available features and updates in the node. This leads to label explosion. Meaning, **too many labels are created** on your Kubernetes nodes. Too many labels make node metadata big, which can slow down API responses. To mitigate this, you can **limit or filter** what NFD labels. During the NFD installation, you can control the label filtering using the `--set worker.config.core.labelSources` flag. Lets say, you only need to label the available features on `cpu`, you can use the flag `--set worker.config.core.labelSources="{cpu}"` to filter the components labels you need. For example, ```bash helm install -n node-feature-discovery --create-namespace nfd oci://registry.k8s.io/nfd/charts/node-feature-discovery --version 0.18.1 --set worker.config.core.labelSources="{cpu}" ``` For advanced filtering, you need to download the Helm values file. ```bash helm show values oci://registry.k8s.io/nfd/charts/node-feature-discovery --version 0.18.1 -n node-feature-discovery > values.yaml ``` Then go to the master configuration section and add the following. ๐Ÿ’ก You can do the same configuration on the workers configmap as well, but if you need any changes, you need to restart every worker pod on every node. For example, I am going to whitelist only the labels for CPU and memory. ```bash config: extraLabelNs: - "feature.node.kubernetes.io" labelWhiteList: "^(cpu|memory).*" ``` Once the configuration is added under the master, as shown below. ![filterting labels](https://storage.ghost.io/c/5f/2f/5f2f4d20-2abf-4534-8d40-7aa233aedd43/content/images/2025/10/image-136.png) Run the following Helm upgrade command. ```bash helm upgrade nfd oci://registry.k8s.io/nfd/charts/node-feature-discovery --version 0.18.1 -n node-feature-discovery -f values.yaml ``` This will restart the master pod and add only the labels of CPU and Memory to the nodes. ```bash $ kubectl get no --show-labels | grep multi-node-cluster-worker2 multi-node-cluster-worker2 Ready 172m v1.33.0 beta.kubernetes.io/arch=amd64,beta.kubernetes.io/os=linux,feature.node.kubernetes.io/cpu-cpuid.ADX=true,feature.node.kubernetes.io/cpu-cpuid.AESNI=true,feature.node.kubernetes.io/cpu-cpuid.AVX2=true,feature.node.kubernetes.io/cpu-cpuid.AVX=true,feature.node.kubernetes.io/cpu-cpuid.CMPXCHG8=true,feature.node.kubernetes.io/cpu-cpuid.FMA3=true,feature.node.kubernetes.io/cpu-cpuid.FXSR=true,feature.node.kubernetes.io/cpu-cpuid.FXSROPT=true,feature.node.kubernetes.io/cpu-cpuid.HYPERVISOR=true,feature.node.kubernetes.io/cpu-cpuid.LAHF=true,feature.node.kubernetes.io/cpu-cpuid.MOVBE=true,feature.node.kubernetes.io/cpu-cpuid.OSXSAVE=true,feature.node.kubernetes.io/cpu-cpuid.SYSCALL=true,feature.node.kubernetes.io/cpu-cpuid.SYSEE=true,feature.node.kubernetes.io/cpu-cpuid.X87=true,feature.node.kubernetes.io/cpu-cpuid.XGETBV1=true,feature.node.kubernetes.io/cpu-cpuid.XSAVE=true,feature.node.kubernetes.io/cpu-cpuid.XSAVEC=true,feature.node.kubernetes.io/cpu-cpuid.XSAVEOPT=true,feature.node.kubernetes.io/cpu-hardware_multithreading=false,feature.node.kubernetes.io/cpu-model.family=6,feature.node.kubernetes.io/cpu-model.id=158,feature.node.kubernetes.io/cpu-model.vendor_id=Intel,feature.node.kubernetes.io/memory-swap=true,kubernetes.io/arch=amd64,kubernetes.io/hostname=multi-node-cluster-worker2,kubernetes.io/os=linux ``` ## How to deploy pods in a specific Node using NFD Labels? You can use any of the NFD generated labels in the `nodeSelector` to deploy the workloads in this node. As an example, let's deploy the [nginx](https://devopscube.com/setup-ingress-kubernetes-nginx-controller/) pod in the node that has [swap](https://devopscube.com/kubernetes-swap/) enabled. ๐Ÿ“Œ One of my node `multi-node-cluster-worker2` already has swap enabled and NFD has enabled the swap label. First,c reate a YAML file `nfd.yaml` and copy the following content. ```yaml apiVersion: v1 kind: Pod metadata: name: nginx spec: containers: - name: nginx image: nginx:latest nodeSelector: feature.node.kubernetes.io/memory-swap: "true" ``` Use the command below to apply the manifest. ```bash kubectl apply -f nfd.yaml ``` Then, run the following command to check, in which node the pod is deployed. ```bash kubectl get po -o wide ``` You will get the following output. ```bash NAME READY STATUS RESTARTS AGE IP NODE nginx 1/1 Running 0 4m 10.244.1.3 multi-node-cluster-worker2 ``` You can see the pod is deployed in the node, which has swap enabled. ## Clean Up If you no longer need this, run the following command. ```bash helm delete -n node-feature-discovery nfd ``` ## NFD Real World Use Cases Lets look at three key use cases for NFD so that you understand it better. #### 1\. Detecting Nodes with Swap Enabled One simple and practical use case of NFD is to automatically detect nodes with swap enabled. In our [Kubernetes Swap newsletter](https://newsletter.devopscube.com/p/kubernetes-swap?ref=devopscube.com), I explained how NFD can label such nodes. Once installed, NFD adds labels that indicate whether swap is turned on or off for each node. This makes it easy to schedule or monitor Pods based on swap status. #### 2\. Scheduling AI and GPU Workloads Another common use case is AI and GPU workload scheduling. For [Kubernetes AI/ML](https://devopscube.com/kubernetes-ai-ml-features/) use cases, NFD works together with the Kubernetes Device Plugin framework (for example, the [NVIDIA device plugin](https://newsletter.devopscube.com/p/kubernetes-built-in-features-for-ai-ml?ref=devopscube.com)) to Detect GPU presence and capabilities and ddd GPU-related labels to nodes This makes it possible to schedule machine learning or deep learning workloads only on GPU-enabled nodes. #### 3\. NUMA-Aware Scheduling and CPU Pinning For high-performance or latency-sensitive applications, NFD provides the **detailed hardware topology data** needed for the following. - NUMA-aware scheduling, where Pods are placed close to the memory and devices they use. (Typically used in AI/ML inference, Databases etc) - CPU pinning, where specific CPU cores are dedicated to certain workloads (Used in Real-time applications like VoIP, Telecom workloads etc) This helps achieve better performance and resource efficiency in compute-heavy environments. In short, NFD acts as the hardware awareness layer for Kubernetes. You can use NFD for any scenario where your application requires special hardware features such as certain GPUs, CPUs, kernel modules, or network devices. ## Conclusion NFD is not required for every Kubernetes setup. But when you are running **AI/ML, or network-heavy workloads**, it can come in handy. It helps Kubernetes understand **what your nodes are truly capable of**, so pods land exactly where they perform best. Over to you! Try out NFD and see if it adds value to your projects.