alauda · fyuan1316 · Jul 13, 2026 · Jul 13, 2026
diff --git a/docs/en/learn/supported-configurations-for-2.x.md b/docs/en/learn/supported-configurations-for-2.x.md
@@ -22,7 +22,7 @@ This page lists the currently maintained Alauda AI versions in the component mat
 | Alauda Build of DCGM-Exporter                | Cluster Plugin | v4.2.3-413-1                       | v4.2.3-413-1                     |
 | Alauda Build of HAMi                         | Cluster Plugin | v2.8.1                             | v2.8.3                           |
 | Alauda Build of HAMi-WebUI                   | Cluster Plugin | v1.10.0                            | v1.10.0                          |
-| Alauda Build of Node Feature Discovery       | Cluster Plugin | v0.17.4                            | v0.17.4                          |
+| Alauda Build of Node Feature Discovery       | Cluster Plugin | v0.18.3                            | v0.17.4                          |
 | Alauda Build of Kueue                        | Cluster Plugin | v0.17.0                            | v0.17.0                          |
 | Alauda Build of LeaderWorkerSet              | Cluster Plugin | v0.8.0-1                           | v0.8.0-1                         |
 | Alauda Build of JobSet (1)                   | Operator       | -                                  | v0.12.0                          |
@@ -63,7 +63,7 @@ This page lists the currently maintained Alauda AI versions in the component mat
 | Alauda Build of NPU Operator (3)             | Cluster Plugin | v1.1.3                             | v1.2.4                           |
 | Alauda Build of HAMi                         | Cluster Plugin | v2.8.1                             | v2.8.3                           |
 | Alauda Build of HAMi-WebUI                   | Cluster Plugin | v1.10.0                            | v1.10.0                          |
-| Alauda Build of Node Feature Discovery       | Cluster Plugin | v0.17.4                            | v0.17.4                          |
+| Alauda Build of Node Feature Discovery       | Cluster Plugin | v0.18.3                            | v0.17.4                          |
 | Alauda Build of Kueue                        | Cluster Plugin | v0.17.0                            | v0.17.0                          |
 | Alauda Build of LeaderWorkerSet              | Cluster Plugin | v0.8.0-1                           | v0.8.0-1                         |
 | Alauda Build of JobSet (1)                   | Operator       | -                                  | v0.12.0                          |

diff --git a/docs/en/model_inference/inference_service/how_to/accurately_schedule.mdx b/docs/en/model_inference/inference_service/how_to/accurately_schedule.mdx
@@ -18,57 +18,74 @@ In a Kubernetes cluster, inconsistencies in GPU models and CUDA driver versions
 
 3. High maintenance overhead: Manually managing the CUDA version dependencies between nodes and applications increases operational complexity.
 
-This document provides a step-by-step guide for scheduling inference services based on the CUDA runtime version and Nvidia Driver version.
-With these settings, you can resolve the CUDA Runtime and CUDA Driver version mismatch at the Kubernetes scheduling level to ensure that applications are scheduled to compatible GPU nodes.
-
+This document provides a step-by-step guide for scheduling inference services
+based on the CUDA compatibility version reported by the NVIDIA driver. With
+these settings, you can reduce CUDA application and driver incompatibility by
+scheduling workloads to compatible GPU nodes.
 
 ## Steps
+
 <Steps>
 ### Adding CUDA version in node labels
 
-1. On each GPU node, run the following command to retrieve the supported CUDA runtime version:
+1. On each GPU node, run the following command to retrieve the CUDA
+   compatibility version reported by the NVIDIA driver:
+
    ```bash
    nvidia-smi | sed -n 's/.*CUDA Version: \([0-9.]\+\).*/\1/p'
    ```
-    For example, the output might be 12.4.
+
+   For example, the output might be 12.4.
+
+   :::note
+   The **CUDA Version** displayed by `nvidia-smi` is the latest CUDA version
+   supported by the installed NVIDIA driver. It does not confirm that a CUDA
+   toolkit or runtime of that version is installed on the node.
+   :::
 
 2. On the control node, label the GPU node with the corresponding major and minor version:
-    ```bash
-    kubectl label node <node-name> \
-    nvidia.com/cuda.runtime.major=12 \
-    nvidia.com/cuda.runtime.minor=4
-    ```
+   ```bash
+   kubectl label node <node-name> \
+   nvidia.com/cuda.runtime.major=12 \
+   nvidia.com/cuda.runtime.minor=4
+   ```
 
 :::tip
-If your cluster has many GPU nodes, it is difficult to label them manually. You can install the `Node Feature Discovery` cluster plugin.
-By deploying the Node Feature Discovery(NFD) cluster plugin and turning on the GFD extension, GPU nodes will automatically be labeled with the CUDA version.
-`Node Feature Discovery` cluster plugin can be retrieved from Customer Portal. Please contact Consumer Support for more information.
+For clusters with many GPU nodes, install
+[Alauda Build of Node Feature Discovery](../../../nfd/install) and enable its
+**gfd Extension**. The GFD extension generates the
+`nvidia.com/cuda.runtime.major` and `nvidia.com/cuda.runtime.minor` labels from
+the CUDA compatibility version reported by the NVIDIA driver.
 :::
 
+### Schedule inference services based on the CUDA version
 
-#### Scheduling Inference Services based on the CUDA version
-  Starting from Alauda AI 1.5, the product will automatically schedule pod of inference services by the CUDA version. For earlier versions, you can follow the following steps:
-  1. Determine which ClusterServingRuntime you need to select when creating an inference service.
-  2. Parse the ClusterServingRuntime label:
-    If `cpaas.io/accelerator-type` is nvidia, further parse `cpaas.io/cuda-version` (example 11.8).
-  3. Add nodeAffinity field in the inference service, example:
-      ```yaml
-      apiVersion: serving.kserve.io/v1beta1
-      kind: InferenceService
-      spec:
-        predictor:
-          affinity:
-            nodeAffinity:
-              preferredDuringSchedulingIgnoredDuringExecution:
-              - weight: 100
-                preference:
-                  matchExpressions:
-                  - key: nvidia.com/cuda.runtime.major
-                    operator: In
-                    values: ["11"]
-                  - key: nvidia.com/cuda.runtime.minor
-                    operator: Gt
-                    values: ["7"] # Since the k8s operator only supports Gt, which means greater than but not equal to, we use the rt version minus one to meet the requirements.
-      ```
+Alauda AI 1.5 and later automatically schedule inference service pods based on
+the CUDA version. For earlier versions, complete the following steps:
+
+1. Determine which ClusterServingRuntime you need to select when creating an inference service.
+2. Parse the ClusterServingRuntime label:
+   If `cpaas.io/accelerator-type` is `nvidia`, also read the
+   `cpaas.io/cuda-version` label, for example `11.8`.
+3. Add a `nodeAffinity` field to the inference service. The following example
+   selects nodes reporting CUDA major version 11 and minor version 8 or later:
+   ```yaml
+   apiVersion: serving.kserve.io/v1beta1
+   kind: InferenceService
+   spec:
+     predictor:
+       affinity:
+         nodeAffinity:
+           preferredDuringSchedulingIgnoredDuringExecution:
+             - weight: 100
+               preference:
+                 matchExpressions:
+                   - key: nvidia.com/cuda.runtime.major
+                     operator: In
+                     values: ['11']
+                   - key: nvidia.com/cuda.runtime.minor
+                     operator: Gt
+                     values: ['7'] # Gt is strict, so 7 expresses a minimum minor version of 8.
+   ```
 
 </Steps>
diff --git a/docs/en/nfd/index.mdx b/docs/en/nfd/index.mdx
@@ -0,0 +1,7 @@
+---
+weight: 91
+---
+
+# Alauda Build of Node Feature Discovery
+
+<Overview />
diff --git a/docs/en/nfd/install.mdx b/docs/en/nfd/install.mdx
@@ -0,0 +1,79 @@
+---
+weight: 20
+---
+
+# Install Node Feature Discovery
+
+## Supported configurations
+
+The `v0.18.3` Cluster Plugin package is published for:
+
+| Requirement               | Supported value                    |
+| ------------------------- | ---------------------------------- |
+| Alauda AI                 | v2.3 Stable                        |
+| Alauda Container Platform | v4.0.x, v4.1.x, v4.2.x, and v4.3.x |
+| Architecture              | x86_64 (amd64) and ARM64 (arm64)   |
+
+## Download the Cluster Plugin
+
+:::info
+
+The **Alauda Build of Node Feature Discovery** Cluster Plugin can be retrieved
+from Customer Portal. Contact Customer Support for more information.
+
+:::
+
+## Upload the Cluster Plugin
+
+For more information, see the following documentation:
+
+<ExternalSiteLink
+  name="acp"
+  href="extend/upload_package.html#upload-a-cluster-plugin"
+  children="Uploading Cluster Plugins"
+/>
+
+## Install the Cluster Plugin
+
+1. Go to **Administrator** > **Marketplace** > **Cluster Plugins**.
+2. Switch to the target cluster and deploy **Alauda Build of Node
+   Feature Discovery**.
+3. Keep **gfd Extension** disabled unless NVIDIA GPU or CUDA-related discovery
+   labels are required.
+4. Wait until the plugin status is **Installed**.
+
+## Verify the installation
+
+Verify that the NFD master, worker, and garbage collector workloads are ready:
+
+```bash
+kubectl -n kube-system get deployment node-feature-discovery-master node-feature-discovery-gc
+kubectl -n kube-system get daemonset node-feature-discovery-worker
+```
+
+Verify that NFD is publishing discovered features:
+
+```bash
+kubectl get nodefeatures.nfd.k8s-sigs.io --all-namespaces
+kubectl get node <node-name> --show-labels
+```
+
+The target node should contain labels with the
+`feature.node.kubernetes.io/` prefix. If the GFD extension is enabled, the node
+can also contain NVIDIA GPU or CUDA-related discovery labels.
+
+## Upgrade to v0.18.3
+
+Version `v0.18.3` updates the upstream NFD base and provides Alauda
+multi-architecture images. The existing optional GFD integration is preserved,
+and no user configuration migration is required.
+
+1. Upload the `v0.18.3` plugin package.
+2. Go to **Administrator** > **Clusters** > **Target Cluster** > **Functional
+   Components**.
+3. Upgrade **Alauda Build of Node Feature Discovery** to `v0.18.3`.
+4. Repeat the installation verification commands.
+
+The NFD upgrade does not change separately managed accelerator driver or Device
+Plugin versions. After the upgrade, verify that the node labels required by
+your workloads remain present.
diff --git a/docs/en/nfd/intro.mdx b/docs/en/nfd/intro.mdx
@@ -0,0 +1,45 @@
+---
+weight: 10
+---
+
+# Introduction
+
+**Alauda Build of Node Feature Discovery (NFD)** is based on the Kubernetes SIG
+[Node Feature Discovery](https://kubernetes-sigs.github.io/node-feature-discovery/v0.18/)
+project. It detects hardware features and system configuration on Kubernetes
+nodes and publishes the discovered information as node labels and `NodeFeature`
+resources.
+
+NFD provides a shared node-discovery layer for AI workloads and other
+components that select nodes by operating system, architecture, kernel, CPU,
+PCI, or other hardware characteristics.
+
+## Scope
+
+NFD discovers and labels node capabilities. It does not:
+
+- install or manage hardware drivers;
+- expose GPU or NPU resources to Kubernetes;
+- replace a vendor Device Plugin, DRA driver, or accelerator operator.
+
+Install the plugin once in each target cluster that requires these node labels.
+Other Cluster Plugins and workloads can consume NFD labels, while drivers and
+resource-management components remain independently installed and managed.
+
+## Optional GFD extension
+
+The deployment form includes a **gfd Extension** switch, which is disabled by
+default. Enable it only when workloads or other components need additional
+NVIDIA GPU and CUDA-related labels.
+
+The GFD extension adds discovery labels. It does not install the NVIDIA driver
+or replace the NVIDIA GPU Device Plugin.
+
+For platform installation and upgrade instructions, see
+[Install Node Feature Discovery](./install).
+
+## Related information
+
+- [Supported configurations](../learn/supported-configurations-for-2.x)
+- [Schedule inference services based on the CUDA version](../model_inference/inference_service/how_to/accurately_schedule)
+- [Upstream Node Feature Discovery documentation](https://kubernetes-sigs.github.io/node-feature-discovery/v0.18/)