diff --git a/content/en/docs/gpu_virtualization.md b/content/en/docs/gpu_virtualization.md
deleted file mode 100644
index 082231e9..00000000
--- a/content/en/docs/gpu_virtualization.md
+++ /dev/null
@@ -1,203 +0,0 @@
-+++
-title = "GPU Virtualization"
-
-date = 2025-05-29
-lastmod = 2025-05-29
-
-draft = false # Is this a draft? true/false
-toc = true # Show table of contents? true/false
-toc_depth = 5
-type = "docs" # Do not modify.
-
-# Add menu entry to sidebar.
-[menu.docs]
- parent = "features"
- weight = 8
-+++
-
-## Background Knowledge of GPU Sharing Modes in Volcano
-
-As AI applications proliferate, the demand for GPUs has surged due to their critical role in training complex models and executing high-performance inference. However, the high cost of GPUs necessitates maximizing their utilization in cloud-native environments. A common challenge is inefficient provisioning: a single GPU may be over-provisioned for smaller workloads or underutilized by larger ones that don't fully saturate its capacity.
-
-
-Volcano addresses this by providing robust virtual GPU (vGPU) scheduling capabilities, facilitating efficient sharing of physical GPUs among multiple containers and jobs. This significantly improves GPU utilization, reduces operational costs, and offers greater flexibility for diverse AI/ML workloads.
-
-
-Volcano simplifies GPU virtualization, enabling users to leverage advanced sharing mechanisms with minimal effort. Users simply define the desired GPU resources and preferred slicing method within their pod or job specifications, and Volcano manages the underlying orchestration.
-
-Volcano supports **two primary GPU sharing modes** to accommodate varying hardware capabilities and performance requirements.
-
-### 1. HAMI-core (Software-based vGPU)
-
-**Description**:
-Leverages **VCUDA**, a CUDA API hijacking technique to enforce GPU core and memory usage limits, enabling **software-level virtual GPU slicing**.
-
-**Use case**:
-Ideal for environments requiring **fine-grained GPU sharing**. Compatible with all GPU types.
-
-### 2. Dynamic MIG (Hardware-level GPU Slicing)
-
-**Description**:
-Utilizes **NVIDIA's MIG (Multi-Instance GPU)** technology to partition a physical GPU into isolated instances with **hardware-level performance guarantees**.
-
-**Use case**:
-Best for **performance-sensitive** workloads. Requires **MIG-capable GPUs** (e.g., A100, H100).
-
-> The design document of Dynamic MIG can refer to: [dynamic-mig](https://github.com/volcano-sh/volcano/blob/master/docs/design/dynamic-mig.md)
-
-> GPU Sharing mode is a node configuration. Volcano supports heterogeneous cluster(i.e a part of node uses HAMi-core while another part uses dynamic MIG), See [volcano-vgpu-device-plugin](https://github.com/Project-HAMi/volcano-vgpu-device-plugin) for configuration and details.
-
-## Installation
-
-To enable vGPU scheduling, the following components must be set up based on the selected mode:
-
-### Common Requirements
-
-**Prerequisites**:
-
- * NVIDIA driver > 440
- * nvidia-docker > 2.0
- * Docker configured with `nvidia` as the default runtime
- * Kubernetes >= 1.16
- * Volcano >= 1.9
-
-- **Install Volcano**:
-
- * Follow instructions in [Volcano Installer Guide](https://github.com/volcano-sh/volcano?tab=readme-ov-file#quick-start-guide)
-
-- **Install Device Plugin**:
-
- * Deploy [`volcano-vgpu-device-plugin`](https://github.com/Project-HAMi/volcano-vgpu-device-plugin)
-
- **Note:** the [vgpu device plugin yaml](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml) also includes the ***Node GPU mode*** and the ***MIG geometry*** configuration. Please refer to the [vgpu device plugin config](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/doc/config.md).
-
-- **Validate Setup**:
- Ensure node allocatable resources include:
-
-
- volcano.sh/vgpu-memory: "89424"
- volcano.sh/vgpu-number: "8"
-
-
-- **Scheduler Config Update**:
-
-
-kind: ConfigMap
-apiVersion: v1
-metadata:
- name: volcano-scheduler-configmap
- namespace: volcano-system
-data:
- volcano-scheduler.conf: |
- actions: "enqueue, allocate, backfill"
- tiers:
- - plugins:
- - name: predicates
- - name: deviceshare
- arguments:
- deviceshare.VGPUEnable: true # enable vgpu plugin
- deviceshare.SchedulePolicy: binpack # scheduling policy. binpack / spread
-
-
-Check with:
-
-```bash
-kubectl get node {node-name} -o yaml
-```
-
-### HAMI-core Usage
-
-**Pod Spec**:
-
-
-metadata:
- name: hami-pod
- annotations:
- volcano.sh/vgpu-mode: "hami-core"
-spec:
- schedulerName: volcano
- containers:
- - name: cuda-container
- image: nvidia/cuda:9.0-devel
- resources:
- limits:
- volcano.sh/vgpu-number: 1 # requesting 1 gpu cards
- volcano.sh/vgpu-cores: 50 # (optional)each vGPU uses 50%
- volcano.sh/vgpu-memory: 3000 # (optional)each vGPU uses 3G GPU memory
-
-
-### Dynamic MIG Usage
-
-- **Enable MIG Mode**:
-
-If you need to use MIG (Multi-Instance GPU), you must run the following command on the GPU node.
-
-```bash
-sudo nvidia-smi -mig 1
-```
-
-- **Geometry Config(Optional)**:
-
-The volcano-vgpu-device-plugin automatically generates an initial MIG configuration,
-which is stored in the `volcano-vgpu-device-config` ConfigMap under the `kube-system` namespace.
-You can customize this configuration as needed. For more details, refer to the [vgpu device plugin yaml](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml).
-
-- **Pod Spec with MIG Annotation**:
-
-
-metadata:
- name: mig-pod
- annotations:
- volcano.sh/vgpu-mode: "mig"
-spec:
- schedulerName: volcano
- containers:
- - name: cuda-container
- image: nvidia/cuda:9.0-devel
- resources:
- limits:
- volcano.sh/vgpu-number: 1
- volcano.sh/vgpu-memory: 3000
-
-
-Note: Actual memory allocated depends on best-fit MIG slice (e.g., request 3GB → 5GB slice used).
-
-## Scheduler Mode Selection
-
-* **Explicit Mode**:
-
- * Use annotation `volcano.sh/vgpu-mode` to force hami-core or MIG mode.
- * If annotation is absent, scheduler selects mode based on resource fit and policy.
-
-* **Scheduling Policy**:
-
- * Modes like `binpack` or `spread` influence node selection.
-
-## Summary Table
-
-| Mode | Isolation | MIG GPU Required | Annotation | Core/Memory Control | Recommended For |
-| ----------- | ---------------- | ---------------- | ---------- | ------------------- | -------------------------- |
-| HAMI-core | Software (VCUDA) | No | No | Yes | General workloads |
-| Dynamic MIG | Hardware | Yes | Yes | MIG-controlled | Performance-sensitive jobs |
-
-
-## Monitoring
-
-* **Scheduler Metrics**:
-
-```bash
-curl http://:8080/metrics
-```
-
-* **Device Plugin Metrics**:
-
-```bash
-curl http://:9394/metrics
-```
-
-Metrics include GPU utilization, pod memory usage, and limits.
-
-## Issues and Contributions
-
-* File bugs: [Volcano Issues](https://github.com/volcano-sh/volcano/issues)
-* Contribute: [Pull Requests Guide](https://help.github.com/articles/using-pull-requests/)
diff --git a/content/en/docs/heterogeneous_devices_virtualization.md b/content/en/docs/heterogeneous_devices_virtualization.md
new file mode 100644
index 00000000..e7dcb5cb
--- /dev/null
+++ b/content/en/docs/heterogeneous_devices_virtualization.md
@@ -0,0 +1,501 @@
++++
+title = "Heterogeneous Devices Virtualization"
+
+date = 2025-05-29
+lastmod = 2025-05-29
+
+draft = false # Is this a draft? true/false
+toc = true # Show table of contents? true/false
+toc_depth = 5
+type = "docs" # Do not modify.
+
+# Add menu entry to sidebar.
+[menu.docs]
+ parent = "features"
+ weight = 8
++++
+
+# GPU Virtualization
+
+## Background Knowledge of GPU Sharing Modes in Volcano
+
+As AI applications proliferate, the demand for GPUs has surged due to their critical role in training complex models and executing high-performance inference. However, the high cost of GPUs necessitates maximizing their utilization in cloud-native environments. A common challenge is inefficient provisioning: a single GPU may be over-provisioned for smaller workloads or underutilized by larger ones that don't fully saturate its capacity.
+
+
+Volcano addresses this by providing robust virtual GPU (vGPU) scheduling capabilities, facilitating efficient sharing of physical GPUs among multiple containers and jobs. This significantly improves GPU utilization, reduces operational costs, and offers greater flexibility for diverse AI/ML workloads.
+
+
+Volcano simplifies GPU virtualization, enabling users to leverage advanced sharing mechanisms with minimal effort. Users simply define the desired GPU resources and preferred slicing method within their pod or job specifications, and Volcano manages the underlying orchestration.
+
+Volcano supports **two primary GPU sharing modes** to accommodate varying hardware capabilities and performance requirements.
+
+### 1. HAMI-core (Software-based vGPU)
+
+**Description**:
+Leverages **VCUDA**, a CUDA API hijacking technique to enforce GPU core and memory usage limits, enabling **software-level virtual GPU slicing**.
+
+**Use case**:
+Ideal for environments requiring **fine-grained GPU sharing**. Compatible with all GPU types.
+
+### 2. Dynamic MIG (Hardware-level GPU Slicing)
+
+**Description**:
+Utilizes **NVIDIA's MIG (Multi-Instance GPU)** technology to partition a physical GPU into isolated instances with **hardware-level performance guarantees**.
+
+**Use case**:
+Best for **performance-sensitive** workloads. Requires **MIG-capable GPUs** (e.g., A100, H100).
+
+> The design document of Dynamic MIG can refer to: [dynamic-mig](https://github.com/volcano-sh/volcano/blob/master/docs/design/dynamic-mig.md)
+
+> GPU Sharing mode is a node configuration. Volcano supports heterogeneous cluster(i.e a part of node uses HAMi-core while another part uses dynamic MIG), See [volcano-vgpu-device-plugin](https://github.com/Project-HAMi/volcano-vgpu-device-plugin) for configuration and details.
+
+## Installation
+
+To enable vGPU scheduling, the following components must be set up based on the selected mode:
+
+### Common Requirements
+
+**Prerequisites**:
+
+ * NVIDIA driver > 440
+ * nvidia-docker > 2.0
+ * Docker configured with `nvidia` as the default runtime
+ * Kubernetes >= 1.16
+ * Volcano >= 1.9
+
+- **Install Volcano**:
+
+ * Follow instructions in [Volcano Installer Guide](https://github.com/volcano-sh/volcano?tab=readme-ov-file#quick-start-guide)
+
+- **Install Device Plugin**:
+
+ * Deploy [`volcano-vgpu-device-plugin`](https://github.com/Project-HAMi/volcano-vgpu-device-plugin)
+
+ **Note:** the [vgpu device plugin yaml](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml) also includes the ***Node GPU mode*** and the ***MIG geometry*** configuration. Please refer to the [vgpu device plugin config](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/doc/config.md).
+
+- **Validate Setup**:
+ Ensure node allocatable resources include:
+
+
+ volcano.sh/vgpu-memory: "89424"
+ volcano.sh/vgpu-number: "8"
+
+
+- **Scheduler Config Update**:
+
+
+kind: ConfigMap
+apiVersion: v1
+metadata:
+ name: volcano-scheduler-configmap
+ namespace: volcano-system
+data:
+ volcano-scheduler.conf: |
+ actions: "enqueue, allocate, backfill"
+ tiers:
+ - plugins:
+ - name: predicates
+ - name: deviceshare
+ arguments:
+ deviceshare.VGPUEnable: true # enable vgpu plugin
+ deviceshare.SchedulePolicy: binpack # scheduling policy. binpack / spread
+
+
+Check with:
+
+```bash
+kubectl get node {node-name} -o yaml
+```
+
+### HAMI-core Usage
+
+**Pod Spec**:
+
+
+metadata:
+ name: hami-pod
+ annotations:
+ volcano.sh/vgpu-mode: "hami-core"
+spec:
+ schedulerName: volcano
+ containers:
+ - name: cuda-container
+ image: nvidia/cuda:9.0-devel
+ resources:
+ limits:
+ volcano.sh/vgpu-number: 1 # requesting 1 gpu cards
+ volcano.sh/vgpu-cores: 50 # (optional)each vGPU uses 50%
+ volcano.sh/vgpu-memory: 3000 # (optional)each vGPU uses 3G GPU memory
+
+
+### Dynamic MIG Usage
+
+- **Enable MIG Mode**:
+
+If you need to use MIG (Multi-Instance GPU), you must run the following command on the GPU node.
+
+```bash
+sudo nvidia-smi -mig 1
+```
+
+- **Geometry Config(Optional)**:
+
+The volcano-vgpu-device-plugin automatically generates an initial MIG configuration,
+which is stored in the `volcano-vgpu-device-config` ConfigMap under the `kube-system` namespace.
+You can customize this configuration as needed. For more details, refer to the [vgpu device plugin yaml](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml).
+
+- **Pod Spec with MIG Annotation**:
+
+
+metadata:
+ name: mig-pod
+ annotations:
+ volcano.sh/vgpu-mode: "mig"
+spec:
+ schedulerName: volcano
+ containers:
+ - name: cuda-container
+ image: nvidia/cuda:9.0-devel
+ resources:
+ limits:
+ volcano.sh/vgpu-number: 1
+ volcano.sh/vgpu-memory: 3000
+
+
+Note: Actual memory allocated depends on best-fit MIG slice (e.g., request 3GB → 5GB slice used).
+
+## Scheduler Mode Selection
+
+* **Explicit Mode**:
+
+ * Use annotation `volcano.sh/vgpu-mode` to force hami-core or MIG mode.
+ * If annotation is absent, scheduler selects mode based on resource fit and policy.
+
+* **Scheduling Policy**:
+
+ * Modes like `binpack` or `spread` influence node selection.
+
+## Summary Table
+
+| Mode | Isolation | MIG GPU Required | Annotation | Core/Memory Control | Recommended For |
+| ----------- | ---------------- | ---------------- | ---------- | ------------------- | -------------------------- |
+| HAMI-core | Software (VCUDA) | No | No | Yes | General workloads |
+| Dynamic MIG | Hardware | Yes | Yes | MIG-controlled | Performance-sensitive jobs |
+
+
+## Monitoring
+
+* **Scheduler Metrics**:
+
+```bash
+curl http://:8080/metrics
+```
+
+* **Device Plugin Metrics**:
+
+```bash
+curl http://:9394/metrics
+```
+
+Metrics include GPU utilization, pod memory usage, and limits.
+
+## Issues and Contributions
+
+* File bugs: [Volcano Issues](https://github.com/volcano-sh/volcano/issues)
+* Contribute: [Pull Requests Guide](https://help.github.com/articles/using-pull-requests/)
+
+
+
+# NPU Virtualization
+
+## Introduction
+
+Volcano supports **two vNPU modes** for sharing Ascend devices:
+
+---
+
+### 1. MindCluster mode
+
+**Description**:
+
+The initial version of [MindCluster](https://gitcode.com/Ascend/mind-cluster)—the official Ascend cluster scheduling add-on—required custom modifications and recompilation of Volcano. Furthermore, it was limited to Volcano release1.7 and release1.9, which complicated its use and restricted access to newer Volcano features.
+
+To address this, we have integrated its core scheduling logic for Ascend vNPU into Volcano's native device-share plugin, which is designed specifically for scheduling and sharing heterogeneous resources like GPUs and NPUs. This integration provides seamless access to vNPU capabilities through the procedure below, while maintaining full compatibility with the latest Volcano features.
+
+**Use case**:
+
+vNPU cluster for Ascend 310 series
+with support for more chip types to come
+
+---
+
+### 2. HAMi mode
+
+**Description**:
+
+This mode is developed by a third-party community 'HAMi', which is the developer of [volcano-vgpu](./how_to_use_volcano_vgpu.md) feature. It supports vNPU feature for both Ascend 310 and Ascend 910. It also supports managing heterogeneous Ascend cluster(Cluster with multiple Ascend types, i.e. 910A,910B2,910B3,310P)
+
+**Use case**:
+
+NPU and vNPU cluster for Ascend 910 series
+NPU and vNPU cluster for Ascend 310 series
+Heterogeneous Ascend cluster
+
+---
+
+## Installation
+
+To enable vNPU scheduling, the following components must be set up based on the selected mode:
+
+
+**Prerequisites**:
+
+Kubernetes >= 1.16
+Volcano >= 1.14
+[ascend-docker-runtime](https://gitcode.com/Ascend/mind-cluster/tree/master/component/ascend-docker-runtime) (for HAMi Mode)
+
+### Install Volcano:
+
+Follow instructions in Volcano Installer Guide
+
+ * Follow instructions in [Volcano Installer Guide](https://github.com/volcano-sh/volcano?tab=readme-ov-file#quick-start-guide)
+
+### Install ascend-device-plugin and third-party components
+
+In this step, you need to select different ascend-device-plugin based on the vNPU mode you selected. MindCluster mode requires additional components from Ascend to be installed.
+
+---
+
+#### MindCluster Mode
+
+##### Install Third-Party Components
+
+Follow the official [Ascend documentation](https://www.hiascend.com/document/detail/zh/mindcluster/72rc1/clustersched/dlug/mxdlug_start_006.html#ZH-CN_TOPIC_0000002470358262__section1837511531098) to install the following components:
+- NodeD
+- Ascend Device Plugin
+- Ascend Docker Runtime
+- ClusterD
+- Ascend Operator
+
+> **Note:** Skip the installation of `ascend-volcano` mentioned in the document above, as we have already installed the native Volcano from the Volcano community in the **Prerequisites** part.
+
+**Configuration Adjustment for Ascend Device Plugin:**
+
+When installing `ascend-device-plugin`, you must set the `presetVirtualDevice` parameter to `"false"` in the `device-plugin-310P-volcano-v{version}.yaml` file to enable dynamic virtualization of 310P:
+
+```yaml
+...
+args: [
+ "device-plugin",
+ "-useAscendDocker=true",
+ "-volcanoType=true",
+ "-presetVirtualDevice=false",
+ "-logFile=/var/log/mindx-dl/devicePlugin/devicePlugin.log",
+ "-logLevel=0"
+]
+...
+```
+For detailed information, please consult the official [Ascend MindCluster documentation.](https://www.hiascend.com/document/detail/zh/mindcluster/72rc1/clustersched/dlug/cpaug_0020.html)
+
+##### Scheduler Config Update
+```yaml
+kind: ConfigMap
+apiVersion: v1
+metadata:
+ name: volcano-scheduler-configmap
+ namespace: volcano-system
+data:
+ volcano-scheduler.conf: |
+ actions: "enqueue, allocate, backfill"
+ tiers:
+ - plugins:
+ - name: predicates
+ - name: deviceshare
+ arguments:
+ deviceshare.AscendMindClusterVNPUEnable: true # enable ascend vnpu
+ configurations:
+ ...
+ - name: init-params
+ arguments: {"grace-over-time":"900","presetVirtualDevice":"false"} # to enable dynamic virtulization, presetVirtualDevice need to be set false
+```
+
+---
+
+#### HAMi mode
+
+##### Label the Node with `ascend=on`
+
+```
+kubectl label node {ascend-node} ascend=on
+```
+
+##### Deploy `hami-scheduler-device` ConfigMap
+
+```
+kubectl apply -f https://raw.githubusercontent.com/Project-HAMi/ascend-device-plugin/refs/heads/main/ascend-device-configmap.yaml
+```
+
+##### Deploy ascend-device-plugin
+
+```
+kubectl apply -f https://raw.githubusercontent.com/Project-HAMi/ascend-device-plugin/refs/heads/main/ascend-device-plugin.yaml
+```
+
+For more information, refer to the [ascend-device-plugin documentation](https://github.com/Project-HAMi/ascend-device-plugin).
+
+##### Scheduler Config Update
+```yaml
+kind: ConfigMap
+apiVersion: v1
+metadata:
+ name: volcano-scheduler-configmap
+ namespace: volcano-system
+data:
+ volcano-scheduler.conf: |
+ actions: "enqueue, allocate, backfill"
+ tiers:
+ - plugins:
+ - name: predicates
+ - name: deviceshare
+ arguments:
+ deviceshare.AscendHAMiVNPUEnable: true # enable ascend vnpu
+ deviceshare.SchedulePolicy: binpack # scheduling policy. binpack / spread
+ deviceshare.KnownGeometriesCMNamespace: kube-system
+ deviceshare.KnownGeometriesCMName: hami-scheduler-device
+```
+
+ **Note:** You may notice that, 'volcano-vgpu' has its own GeometriesCMName and GeometriesCMNamespace, which means if you want to use both vNPU and vGPU in a same volcano cluster, you need to merge the configMap from both sides and set it here.
+
+## Usage
+
+Usage is different depending on the mode you selected
+
+---
+
+### MindCluster mode
+
+```yaml
+apiVersion: batch.volcano.sh/v1alpha1
+kind: Job
+metadata:
+ name: mindx-dls
+ namespace: vnpu
+ labels:
+ ring-controller.atlas: ascend-310P
+spec:
+ minAvailable: 1
+ schedulerName: volcano
+ policies:
+ - event: PodEvicted
+ action: RestartJob
+ plugins:
+ ssh: []
+ env: []
+ svc: []
+ maxRetry: 3
+ queue: default
+ tasks:
+ - name: "default-test"
+ replicas: 1
+ template:
+ metadata:
+ labels:
+ app: infers
+ ring-controller.atlas: ascend-310P
+ vnpu-dvpp: "null"
+ vnpu-level: low
+ spec:
+ schedulerName: volcano
+ containers:
+ - name: resnet50infer
+ image: swr.cn-south-1.myhuaweicloud.com/ascendhub/mindie:2.1.RC1-300I-Duo-py311-openeuler24.03-lts
+ imagePullPolicy: IfNotPresent
+ securityContext:
+ privileged: false
+ command: ["/bin/bash", "-c", "tail -f /dev/null"]
+ resources:
+ requests:
+ huawei.com/npu-core: 8
+ limits:
+ huawei.com/npu-core: 8
+ nodeSelector:
+ host-arch: huawei-arm
+
+```
+
+The supported Ascend chips and their `ResourceNames` are shown in the following table:
+
+| ChipName | JobLabel and TaskLabel | ResourceName |
+|-------|------------------------------------|-------|
+| 310P3 | ring-controller.atlas: ascend-310P | huawei.com/npu-core |
+
+**Description of Labels in the Virtualization Task YAML**
+
+| **Key** | **Value** | **Description** |
+| ------------------------- | --------------- |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **vnpu-level** | **low** | Low configuration (default). Selects the lowest-configuration "virtualized instance template." |
+| | **high** | Performance-first. When cluster resources are sufficient, the scheduler will choose the highest-configured virtualized instance template possible. When most physical NPUs in the cluster are already in use and only a few AI Cores remain on each device, the scheduler will allocate templates that match the remaining AI Core count rather than forcing high-profile templates. For details, refer to the table below. |
+| **vnpu-dvpp** | **yes** | The Pod uses DVPP. |
+| | **no** | The Pod does not use DVPP. |
+| | **null** | Default value. DVPP usage is not considered. |
+| **ring-controller.atlas** | **ascend-310P** | Indicates that the task uses products from the Atlas inference series. |
+
+**Effect of DVPP and Level Configurations**
+
+| **Product Model** | **Requested AI Core Count** | **vnpu-dvpp** | **vnpu-level** | **Downgrade** | **Selected Template** |
+| --------------------------------------- | --------------------------- |---------------| -------------------- | ------------- | --------------------- |
+| **Atlas Inference Series (8 AI Cores)** | **1** | `null` | Any value | – | `vir01` |
+| | **2** | `null` | `low` / other values | – | `vir02_1c` |
+| | **2** | `null` | `high` | No | `vir02` |
+| | **2** | `null` | `high` | Yes | `vir02_1c` |
+| | **4** | `yes` | `low` / other values | – | `vir04_4c_dvpp` |
+| | **4** | `no` | `low` / other values | – | `vir04_3c_ndvpp` |
+| | **4** | `null` | `low` / other values | – | `vir04_3c` |
+| | **4** | `yes` | `high` | – | `vir04_4c_dvpp` |
+| | **4** | `no` | `high` | – | `vir04_3c_ndvpp` |
+| | **4** | `null` | `high` | No | `vir04` |
+| | **4** | `null` | `high` | Yes | `vir04_3c` |
+| | **8 or multiples of 8** | Any value | Any value | – | – |
+
+
+**Notice**
+
+For **chip virtualization (non-full card usage)**, the value of `vnpu-dvpp` must strictly match the corresponding value listed in the above table.
+Any other values will cause the task to fail to be dispatched.
+
+
+For detailed information, please consult the official [Ascend MindCluster documentation.](https://www.hiascend.com/document/detail/zh/mindcluster/72rc1/clustersched/dlug/cpaug_0020.html)
+
+---
+
+### HAMi mode
+
+```yaml
+apiVersion: v1
+kind: Pod
+metadata:
+ name: ascend-pod
+spec:
+ schedulerName: volcano
+ containers:
+ - name: ubuntu-container
+ image: swr.cn-south-1.myhuaweicloud.com/ascendhub/ascend-pytorch:24.0.RC1-A2-1.11.0-ubuntu20.04
+ command: ["sleep"]
+ args: ["100000"]
+ resources:
+ limits:
+ huawei.com/Ascend310P: "1"
+ huawei.com/Ascend310P-memory: "4096"
+
+```
+
+The supported Ascend chips and their `ResourceNames` are shown in the following table:
+
+| ChipName | ResourceName | ResourceMemoryName |
+|-------|-------|-------|
+| 910A | huawei.com/Ascend910A | huawei.com/Ascend910A-memory |
+| 910B2 | huawei.com/Ascend910B2 | huawei.com/Ascend910B2-memory |
+| 910B3 | huawei.com/Ascend910B3 | huawei.com/Ascend910B3-memory |
+| 910B4 | huawei.com/Ascend910B4 | huawei.com/Ascend910B4-memory |
+| 910B4-1 | huawei.com/Ascend910B4-1 | huawei.com/Ascend910B4-1-memory |
+| 310P3 | huawei.com/Ascend310P | huawei.com/Ascend310P-memory |
\ No newline at end of file
diff --git a/content/zh/docs/gpu_virtualization.md b/content/zh/docs/gpu_virtualization.md
deleted file mode 100644
index f8dc916d..00000000
--- a/content/zh/docs/gpu_virtualization.md
+++ /dev/null
@@ -1,202 +0,0 @@
-+++
-title = "GPU虚拟化"
-
-date = 2025-05-29
-lastmod = 2025-05-29
-
-draft = false # Is this a draft? true/false
-toc = true # Show table of contents? true/false
-toc_depth = 5
-type = "docs" # Do not modify.
-
-# Add menu entry to sidebar.
-[menu.docs]
- parent = "features"
- weight = 8
-+++
-
-## 背景
-
-随着AI应用的日益普及，对GPU的需求也随之激增。GPU作为模型训练与推理任务的核心组件，其重要性不言而喻。然而，GPU成本高昂，如何在云原生环境下最大化其利用率，已成为业界关注的焦点。在实际应用中，常出现以下情况：对于小型工作负载，单个GPU可能造成资源浪费；而对于大型工作负载，单个GPU的算力又可能未被充分挖掘。
-
-为应对这一挑战，Volcano通过提供强大的虚拟GPU (vGPU)调度能力，实现了物理GPU在多个容器和作业间的有效共享。这不仅能显著提升GPU利用率、降低运营成本，也为各类AI/ML工作负载带来了更灵活的资源调度方案。
-
-Volcano致力于简化GPU虚拟化的复杂度，使用户能便捷地运用这些高级共享机制。用户只需在Pod或作业的配置中声明所需的GPU资源及期望的切分方式，Volcano便能自动完成底层的资源编排工作。
-
-Volcano主要支持以下两种GPU共享模式，用以实现vGPU调度并满足不同的硬件能力与性能需求：
-
-
-### 1. HAMI-core（基于软件的vGPU）
-
-**描述**：
-通过VCUDA (一种CUDA API劫持技术) 对GPU核心与显存的使用进行限制，从而实现软件层面的虚拟GPU切片。
-
-**使用场景**：
-适用于需要细粒度GPU共享的场景，兼容所有类型的GPU。
-
-### 2. Dynamic MIG（硬件级GPU切片）
-
-**描述**：
-采用NVIDIA的MIG (Multi-Instance GPU)技术，可将单个物理GPU分割为多个具备硬件级性能保障的隔离实例。
-
-**使用场景**：
-尤其适用于对性能敏感的工作负载，要求GPU支持MIG特性（如A100、H100系列）。
-
-> Dynamic MIG的设计文档请参考：[dynamic-mig](https://github.com/volcano-sh/volcano/blob/master/docs/design/dynamic-mig.md)
-
-> GPU共享模式属于节点级别的配置。Volcano支持异构集群，即集群中可同时包含采用HAMI-core模式的节点和采用Dynamic MIG模式的节点。更多配置及详情，请参阅[volcano-vgpu-device-plugin](https://github.com/Project-HAMi/volcano-vgpu-device-plugin)。
-
-## 安装
-
-若需启用vGPU调度功能，请根据所选模式配置以下组件：
-
-### 通用要求
-
-**环境依赖**：
-
- * NVIDIA驱动 > 440
- * nvidia-docker > 2.0
- * Docker已配置`nvidia`为默认运行时
- * Kubernetes >= 1.16
- * Volcano >= 1.9
-
-- **安装Volcano**：
-
- * 具体步骤请参照[Volcano安装指南](https://github.com/volcano-sh/volcano?tab=readme-ov-file#quick-start-guide)。
-
-- **安装设备插件**：
-
- * 部署[`volcano-vgpu-device-plugin`](https://github.com/Project-HAMi/volcano-vgpu-device-plugin)。
-
- **说明：**[vgpu设备插件的YAML文件](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml)中亦包含***节点GPU模式***及***MIG实例规格***等相关配置。详情请参阅[vgpu设备插件配置文档](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/doc/config.md)。
-
-- **验证部署**：
- 请确保节点的可分配资源（Allocatable Resources）中包含以下信息：
-
-
- volcano.sh/vgpu-memory: "89424"
- volcano.sh/vgpu-number: "8"
-
-
-- **更新调度器配置**：
-
-
-kind: ConfigMap
-apiVersion: v1
-metadata:
- name: volcano-scheduler-configmap
- namespace: volcano-system
-data:
- volcano-scheduler.conf: |
- actions: "enqueue, allocate, backfill"
- tiers:
- - plugins:
- - name: predicates
- - name: deviceshare
- arguments:
- deviceshare.VGPUEnable: true # 启用vgpu插件
- deviceshare.SchedulePolicy: binpack # 调度策略：binpack / spread
-
-
-可通过以下命令检查：
-
-```bash
-kubectl get node {node-name} -o yaml
-```
-
-### HAMI-core使用方法
-
-**Pod配置示例**：
-
-
-metadata:
- name: hami-pod
- annotations:
- volcano.sh/vgpu-mode: "hami-core"
-spec:
- schedulerName: volcano
- containers:
- - name: cuda-container
- image: nvidia/cuda:9.0-devel
- resources:
- limits:
- volcano.sh/vgpu-number: 1 # 请求1张GPU卡
- volcano.sh/vgpu-cores: 50 # (可选)每个vGPU使用50%核心
- volcano.sh/vgpu-memory: 3000 # (可选)每个vGPU使用3G显存
-
-
-### Dynamic MIG使用方法
-
-- **启用MIG模式**：
-
-若需启用MIG (Multi-Instance GPU)模式，请在目标GPU节点上执行以下命令：
-
-```bash
-sudo nvidia-smi -mig 1
-```
-
-- **MIG实例规格配置（可选）**：
-
-`volcano-vgpu-device-plugin`会自动生成一套初始MIG配置，并存储于`kube-system`命名空间下的`volcano-vgpu-device-config` ConfigMap中。用户可按需自定义此配置。更多详情请参阅[vgpu设备插件YAML文件](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml)。
-
-- **带MIG注解的Pod配置示例**：
-
-
-metadata:
- name: mig-pod
- annotations:
- volcano.sh/vgpu-mode: "mig"
-spec:
- schedulerName: volcano
- containers:
- - name: cuda-container
- image: nvidia/cuda:9.0-devel
- resources:
- limits:
- volcano.sh/vgpu-number: 1
- volcano.sh/vgpu-memory: 3000
-
-
-注意：实际分配的显存大小取决于最匹配的MIG实例规格（例如：请求3GB显存，可能会分配到规格为5GB的MIG实例）。
-
-
-## 调度器模式选择
-
-* **显式指定模式**：
-
- * 通过Pod注解`volcano.sh/vgpu-mode`来强制指定HAMI-core或MIG模式。
- * 若未指定该注解，调度器将依据资源匹配度及预设策略自动选择合适的模式。
-
-* **调度策略影响**：
-
- * 调度策略（如`binpack`或`spread`）会影响vGPU Pod在节点间的分布。
-
-## 总结表
-
-| 模式 | 隔离级别 | 是否依赖MIG GPU | 需注解指定模式 | 核心/显存控制方式 | 推荐应用场景 |
-| ----------- | -------------- | --------------- | ------------ | ------------------- | -------------------------- |
-| HAMI-core | 软件 (VCUDA) | 否 | 否 | 用户自定义 (核心/显存) | 通用型工作负载 |
-| Dynamic MIG | 硬件 (MIG) | 是 | 是 | MIG实例规格决定 | 对性能敏感的工作负载 |
-
-
-## 监控
-
-* **调度器监控指标**：
-
-```bash
-curl http://:8080/metrics
-```
-
-* **设备插件监控指标**：
-
-```bash
-curl http://:9394/metrics
-```
-
-监控指标包括GPU利用率、各Pod的显存使用量及限制等。
-
-
-## 问题和贡献
-
-* 提交Issue：[Volcano Issues](https://github.com/volcano-sh/volcano/issues)
-* 贡献代码：[Pull Request指南](https://help.github.com/articles/using-pull-requests/)
diff --git a/content/zh/docs/heterogeneous_devices_virtualization.md b/content/zh/docs/heterogeneous_devices_virtualization.md
new file mode 100644
index 00000000..03a75d67
--- /dev/null
+++ b/content/zh/docs/heterogeneous_devices_virtualization.md
@@ -0,0 +1,499 @@
++++
+title = "异构设备虚拟化"
+
+date = 2025-05-29
+lastmod = 2025-05-29
+
+draft = false # Is this a draft? true/false
+toc = true # Show table of contents? true/false
+toc_depth = 5
+type = "docs" # Do not modify.
+
+# Add menu entry to sidebar.
+[menu.docs]
+ parent = "features"
+ weight = 8
++++
+
+# GPU 虚拟化
+
+## 背景
+
+随着AI应用的日益普及，对GPU的需求也随之激增。GPU作为模型训练与推理任务的核心组件，其重要性不言而喻。然而，GPU成本高昂，如何在云原生环境下最大化其利用率，已成为业界关注的焦点。在实际应用中，常出现以下情况：对于小型工作负载，单个GPU可能造成资源浪费；而对于大型工作负载，单个GPU的算力又可能未被充分挖掘。
+
+为应对这一挑战，Volcano通过提供强大的虚拟GPU (vGPU)调度能力，实现了物理GPU在多个容器和作业间的有效共享。这不仅能显著提升GPU利用率、降低运营成本，也为各类AI/ML工作负载带来了更灵活的资源调度方案。
+
+Volcano致力于简化GPU虚拟化的复杂度，使用户能便捷地运用这些高级共享机制。用户只需在Pod或作业的配置中声明所需的GPU资源及期望的切分方式，Volcano便能自动完成底层的资源编排工作。
+
+Volcano主要支持以下两种GPU共享模式，用以实现vGPU调度并满足不同的硬件能力与性能需求：
+
+
+### 1. HAMI-core（基于软件的vGPU）
+
+**描述**：
+通过VCUDA (一种CUDA API劫持技术) 对GPU核心与显存的使用进行限制，从而实现软件层面的虚拟GPU切片。
+
+**使用场景**：
+适用于需要细粒度GPU共享的场景，兼容所有类型的GPU。
+
+### 2. Dynamic MIG（硬件级GPU切片）
+
+**描述**：
+采用NVIDIA的MIG (Multi-Instance GPU)技术，可将单个物理GPU分割为多个具备硬件级性能保障的隔离实例。
+
+**使用场景**：
+尤其适用于对性能敏感的工作负载，要求GPU支持MIG特性（如A100、H100系列）。
+
+> Dynamic MIG的设计文档请参考：[dynamic-mig](https://github.com/volcano-sh/volcano/blob/master/docs/design/dynamic-mig.md)
+
+> GPU共享模式属于节点级别的配置。Volcano支持异构集群，即集群中可同时包含采用HAMI-core模式的节点和采用Dynamic MIG模式的节点。更多配置及详情，请参阅[volcano-vgpu-device-plugin](https://github.com/Project-HAMi/volcano-vgpu-device-plugin)。
+
+## 安装
+
+若需启用vGPU调度功能，请根据所选模式配置以下组件：
+
+### 通用要求
+
+**环境依赖**：
+
+ * NVIDIA驱动 > 440
+ * nvidia-docker > 2.0
+ * Docker已配置`nvidia`为默认运行时
+ * Kubernetes >= 1.16
+ * Volcano >= 1.9
+
+- **安装Volcano**：
+
+ * 具体步骤请参照[Volcano安装指南](https://github.com/volcano-sh/volcano?tab=readme-ov-file#quick-start-guide)。
+
+- **安装设备插件**：
+
+ * 部署[`volcano-vgpu-device-plugin`](https://github.com/Project-HAMi/volcano-vgpu-device-plugin)。
+
+ **说明：**[vgpu设备插件的YAML文件](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml)中亦包含***节点GPU模式***及***MIG实例规格***等相关配置。详情请参阅[vgpu设备插件配置文档](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/doc/config.md)。
+
+- **验证部署**：
+ 请确保节点的可分配资源（Allocatable Resources）中包含以下信息：
+
+
+ volcano.sh/vgpu-memory: "89424"
+ volcano.sh/vgpu-number: "8"
+
+
+- **更新调度器配置**：
+
+
+kind: ConfigMap
+apiVersion: v1
+metadata:
+ name: volcano-scheduler-configmap
+ namespace: volcano-system
+data:
+ volcano-scheduler.conf: |
+ actions: "enqueue, allocate, backfill"
+ tiers:
+ - plugins:
+ - name: predicates
+ - name: deviceshare
+ arguments:
+ deviceshare.VGPUEnable: true # 启用vgpu插件
+ deviceshare.SchedulePolicy: binpack # 调度策略：binpack / spread
+
+
+可通过以下命令检查：
+
+```bash
+kubectl get node {node-name} -o yaml
+```
+
+### HAMI-core使用方法
+
+**Pod配置示例**：
+
+
+metadata:
+ name: hami-pod
+ annotations:
+ volcano.sh/vgpu-mode: "hami-core"
+spec:
+ schedulerName: volcano
+ containers:
+ - name: cuda-container
+ image: nvidia/cuda:9.0-devel
+ resources:
+ limits:
+ volcano.sh/vgpu-number: 1 # 请求1张GPU卡
+ volcano.sh/vgpu-cores: 50 # (可选)每个vGPU使用50%核心
+ volcano.sh/vgpu-memory: 3000 # (可选)每个vGPU使用3G显存
+
+
+### Dynamic MIG使用方法
+
+- **启用MIG模式**：
+
+若需启用MIG (Multi-Instance GPU)模式，请在目标GPU节点上执行以下命令：
+
+```bash
+sudo nvidia-smi -mig 1
+```
+
+- **MIG实例规格配置（可选）**：
+
+`volcano-vgpu-device-plugin`会自动生成一套初始MIG配置，并存储于`kube-system`命名空间下的`volcano-vgpu-device-config` ConfigMap中。用户可按需自定义此配置。更多详情请参阅[vgpu设备插件YAML文件](https://github.com/Project-HAMi/volcano-vgpu-device-plugin/blob/main/volcano-vgpu-device-plugin.yml)。
+
+- **带MIG注解的Pod配置示例**：
+
+
+metadata:
+ name: mig-pod
+ annotations:
+ volcano.sh/vgpu-mode: "mig"
+spec:
+ schedulerName: volcano
+ containers:
+ - name: cuda-container
+ image: nvidia/cuda:9.0-devel
+ resources:
+ limits:
+ volcano.sh/vgpu-number: 1
+ volcano.sh/vgpu-memory: 3000
+
+
+注意：实际分配的显存大小取决于最匹配的MIG实例规格（例如：请求3GB显存，可能会分配到规格为5GB的MIG实例）。
+
+
+## 调度器模式选择
+
+* **显式指定模式**：
+
+ * 通过Pod注解`volcano.sh/vgpu-mode`来强制指定HAMI-core或MIG模式。
+ * 若未指定该注解，调度器将依据资源匹配度及预设策略自动选择合适的模式。
+
+* **调度策略影响**：
+
+ * 调度策略（如`binpack`或`spread`）会影响vGPU Pod在节点间的分布。
+
+## 总结表
+
+| 模式 | 隔离级别 | 是否依赖MIG GPU | 需注解指定模式 | 核心/显存控制方式 | 推荐应用场景 |
+| ----------- | -------------- | --------------- | ------------ | ------------------- | -------------------------- |
+| HAMI-core | 软件 (VCUDA) | 否 | 否 | 用户自定义 (核心/显存) | 通用型工作负载 |
+| Dynamic MIG | 硬件 (MIG) | 是 | 是 | MIG实例规格决定 | 对性能敏感的工作负载 |
+
+
+## 监控
+
+* **调度器监控指标**：
+
+```bash
+curl http://:8080/metrics
+```
+
+* **设备插件监控指标**：
+
+```bash
+curl http://:9394/metrics
+```
+
+监控指标包括GPU利用率、各Pod的显存使用量及限制等。
+
+
+## 问题和贡献
+
+* 提交Issue：[Volcano Issues](https://github.com/volcano-sh/volcano/issues)
+* 贡献代码：[Pull Request指南](https://help.github.com/articles/using-pull-requests/)
+
+
+# NPU 虚拟化
+
+## 简介
+
+Volcano 支持 **两种 vNPU 模式** 来共享 Ascend 设备：
+
+---
+
+### 1. MindCluster 模式
+
+**说明**:
+
+早期版本的 [MindCluster](https://gitcode.com/Ascend/mind-cluster)（官方 Ascend 集群调度插件）需要对 Volcano 进行自定义修改并重新编译，并且仅兼容 Volcano release1.7 和 release1.9。这使得其使用较为复杂，并限制了用户获取新版 Volcano 的功能。
+
+为了解决这些问题，我们已将其针对 Ascend vNPU 的核心调度逻辑集成到 Volcano 的原生 device-share 插件中。该插件专门用于调度和共享异构资源（如 GPU 和 NPU）。这种集成方式可以通过以下流程直接使用 vNPU 功能，同时保持与最新 Volcano 功能的完全兼容性。
+
+**使用场景**:
+
+适用于 Ascend 310 系列 vNPU 集群
+后续将支持更多芯片类型
+
+---
+
+### 2. HAMi 模式
+
+**说明**：
+
+该模式由第三方社区 “HAMi” 开发，该社区也是 [volcano-vgpu](./how_to_use_volcano_vgpu.md) 功能的开发者。
+HAMi 模式支持 Ascend 310 与 Ascend 910 的 vNPU 功能，并支持异构 Ascend 集群（即包含多种 Ascend 芯片型号，如 910A、910B2、910B3、310P）。
+
+**使用场景**：
+
+Ascend 910 系列的 NPU 和 vNPU 集群
+Ascend 310 系列的 NPU 和 vNPU 集群
+异构 Ascend 集群
+
+---
+
+## 安装
+
+要启用 vNPU 调度功能，需要根据选择的模式部署以下组件：
+
+
+**环境要求**:
+
+Kubernetes >= 1.16
+Volcano >= 1.14
+[ascend-docker-runtime](https://gitcode.com/Ascend/mind-cluster/tree/master/component/ascend-docker-runtime) (for HAMi Mode)
+
+### 安装 Volcano:
+
+请参考 Volcano [安装指南](https://github.com/volcano-sh/volcano?tab=readme-ov-file#quick-start-guide)
+
+### 安装 ascend-device-plugin 与第三方组件
+
+在此步骤中，需要根据选择的 vNPU 模式选择不同的 ascend-device-plugin。
+MindCluster 模式需要额外安装来自 Ascend 的组件。
+
+---
+
+#### MindCluster 模式
+
+##### 安装第三方组件
+
+请参考官方 [Ascend 文档](https://www.hiascend.com/document/detail/zh/mindcluster/72rc1/clustersched/dlug/mxdlug_start_006.html#ZH-CN_TOPIC_0000002470358262__section1837511531098)安装以下组件：
+
+- NodeD
+- Ascend Device Plugin
+- Ascend Docker Runtime
+- ClusterD
+- Ascend Operator
+
+> **Note:** 请跳过上述文档中提到的 `ascend-volcano` 安装步骤，因为我们已在上面安装了来自 Volcano 社区的原生 Volcano。
+
+**Ascend Device Plugin 配置修改:**
+
+安装 `ascend-device-plugin` 时，需要在 `device-plugin-310P-volcano-v{version}.yaml` 文件中将 `presetVirtualDevice` 参数置为 `"false"` 以启用 310P 的虚拟化:
+
+```yaml
+...
+args: [
+ "device-plugin",
+ "-useAscendDocker=true",
+ "-volcanoType=true",
+ "-presetVirtualDevice=false",
+ "-logFile=/var/log/mindx-dl/devicePlugin/devicePlugin.log",
+ "-logLevel=0"
+]
+...
+```
+详细信息请参考官方 [Ascend MindCluster 文档](https://www.hiascend.com/document/detail/zh/mindcluster/72rc1/clustersched/dlug/cpaug_0020.html)
+
+##### 调度器配置更新
+```yaml
+kind: ConfigMap
+apiVersion: v1
+metadata:
+ name: volcano-scheduler-configmap
+ namespace: volcano-system
+data:
+ volcano-scheduler.conf: |
+ actions: "enqueue, allocate, backfill"
+ tiers:
+ - plugins:
+ - name: predicates
+ - name: deviceshare
+ arguments:
+ deviceshare.AscendMindClusterVNPUEnable: true # enable ascend vnpu
+ configurations:
+ ...
+ - name: init-params
+ arguments: {"grace-over-time":"900","presetVirtualDevice":"false"} # 启用动态虚拟化需要将presetVirtualDevice设置为false
+```
+
+---
+
+#### HAMi 模式
+
+##### 给节点打标签 `ascend=on`
+
+```
+kubectl label node {ascend-node} ascend=on
+```
+
+##### 部署 `hami-scheduler-device` ConfigMap
+
+```
+kubectl apply -f https://raw.githubusercontent.com/Project-HAMi/ascend-device-plugin/refs/heads/main/ascend-device-configmap.yaml
+```
+
+##### 部署 ascend-device-plugin
+
+```
+kubectl apply -f https://raw.githubusercontent.com/Project-HAMi/ascend-device-plugin/refs/heads/main/ascend-device-plugin.yaml
+```
+
+更多信息请参考 [ascend-device-plugin 文档](https://github.com/Project-HAMi/ascend-device-plugin).
+
+##### 调度器配置更新
+```yaml
+kind: ConfigMap
+apiVersion: v1
+metadata:
+ name: volcano-scheduler-configmap
+ namespace: volcano-system
+data:
+ volcano-scheduler.conf: |
+ actions: "enqueue, allocate, backfill"
+ tiers:
+ - plugins:
+ - name: predicates
+ - name: deviceshare
+ arguments:
+ deviceshare.AscendHAMiVNPUEnable: true # enable ascend vnpu
+ deviceshare.SchedulePolicy: binpack # scheduling policy. binpack / spread
+ deviceshare.KnownGeometriesCMNamespace: kube-system
+ deviceshare.KnownGeometriesCMName: hami-scheduler-device
+```
+
+ **注意：** 你可能注意到 `volcano-vgpu` 也有自己的 `GeometriesCMName` 和 `GeometriesCMNamespace`。如果希望在同一 Volcano 集群中同时使用 vNPU 和 vGPU，需要将双方的 configMap 合并，并在此处设置。
+
+## 使用方法
+
+不同模式下的使用方法有所不同
+
+---
+
+### MindCluster 模式
+
+```yaml
+apiVersion: batch.volcano.sh/v1alpha1
+kind: Job
+metadata:
+ name: mindx-dls
+ namespace: vnpu
+ labels:
+ ring-controller.atlas: ascend-310P
+spec:
+ minAvailable: 1
+ schedulerName: volcano
+ policies:
+ - event: PodEvicted
+ action: RestartJob
+ plugins:
+ ssh: []
+ env: []
+ svc: []
+ maxRetry: 3
+ queue: default
+ tasks:
+ - name: "default-test"
+ replicas: 1
+ template:
+ metadata:
+ labels:
+ app: infers
+ ring-controller.atlas: ascend-310P
+ vnpu-dvpp: "null"
+ vnpu-level: low
+ spec:
+ schedulerName: volcano
+ containers:
+ - name: resnet50infer
+ image: swr.cn-south-1.myhuaweicloud.com/ascendhub/mindie:2.1.RC1-300I-Duo-py311-openeuler24.03-lts
+ imagePullPolicy: IfNotPresent
+ securityContext:
+ privileged: false
+ command: ["/bin/bash", "-c", "tail -f /dev/null"]
+ resources:
+ requests:
+ huawei.com/npu-core: 8
+ limits:
+ huawei.com/npu-core: 8
+ nodeSelector:
+ host-arch: huawei-arm
+
+```
+
+支持的 Ascend 芯片及其 `ResourceName` 如下表所示:
+
+| 芯片型号 | JobLabel 和 TaskLabel | ResourceName |
+|-------|------------------------------------|-------|
+| 310P3 | ring-controller.atlas: ascend-310P | huawei.com/npu-core |
+
+**虚拟化任务 YAML 中的标签说明**
+
+| **健** | **值** | **说明** |
+| ------------------------- | --------------- |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **vnpu-level** | **low** | 低配置（默认）。选择最低配置的虚拟实例模板 |
+| | **high** | 性能优先。当集群资源充足时，调度器会选择最高配置的虚拟实例模板。当集群中大多数物理 NPU 已部分被使用，仅剩少量 AI Core 时，调度器会根据剩余可用 Core 数自动选择合适的模板，而不是强制使用高规格模板。 |
+| **vnpu-dvpp** | **yes** | Pod 使用 DVPP |
+| | **no** | Pod 不使用 DVPP |
+| | **null** | 默认值。不考虑 DVPP 使用情况 |
+| **ring-controller.atlas** | **ascend-310P** | 表示任务使用 Atlas 推理系列产品 |
+
+**DVPP 与 Level 组合配置的效果**
+
+| **产品型号** | **请求 AI Core 数** | **vnpu-dvpp** | **vnpu-level** | **降级** | **选择模板** |
+| --------------------------------------- | --------------------------- |---------------| -------------------- | ------------- | --------------------- |
+| **Atlas Inference Series (8 AI Cores)** | **1** | `null` | Any value | – | `vir01` |
+| | **2** | `null` | `low` / other values | – | `vir02_1c` |
+| | **2** | `null` | `high` | No | `vir02` |
+| | **2** | `null` | `high` | Yes | `vir02_1c` |
+| | **4** | `yes` | `low` / other values | – | `vir04_4c_dvpp` |
+| | **4** | `no` | `low` / other values | – | `vir04_3c_ndvpp` |
+| | **4** | `null` | `low` / other values | – | `vir04_3c` |
+| | **4** | `yes` | `high` | – | `vir04_4c_dvpp` |
+| | **4** | `no` | `high` | – | `vir04_3c_ndvpp` |
+| | **4** | `null` | `high` | No | `vir04` |
+| | **4** | `null` | `high` | Yes | `vir04_3c` |
+| | **8 or multiples of 8** | Any value | Any value | – | – |
+
+
+**注意**
+
+对于 **芯片虚拟化（非整卡使用）**，`vnpu-dvpp` 的值必须与上表中对应项严格匹配。否则，任务将无法被调度。
+
+
+详细信息请参考官方 [Ascend MindCluster 文档](https://www.hiascend.com/document/detail/zh/mindcluster/72rc1/clustersched/dlug/cpaug_0020.html)
+
+---
+
+### HAMi 模式
+
+```yaml
+apiVersion: v1
+kind: Pod
+metadata:
+ name: ascend-pod
+spec:
+ schedulerName: volcano
+ containers:
+ - name: ubuntu-container
+ image: swr.cn-south-1.myhuaweicloud.com/ascendhub/ascend-pytorch:24.0.RC1-A2-1.11.0-ubuntu20.04
+ command: ["sleep"]
+ args: ["100000"]
+ resources:
+ limits:
+ huawei.com/Ascend310P: "1"
+ huawei.com/Ascend310P-memory: "4096"
+
+```
+
+支持的 Ascend 芯片及其 `ResourceNames` 如下表所示:
+
+| 芯片型号 | ResourceName | ResourceMemoryName |
+|-------|-------|-------|
+| 910A | huawei.com/Ascend910A | huawei.com/Ascend910A-memory |
+| 910B2 | huawei.com/Ascend910B2 | huawei.com/Ascend910B2-memory |
+| 910B3 | huawei.com/Ascend910B3 | huawei.com/Ascend910B3-memory |
+| 910B4 | huawei.com/Ascend910B4 | huawei.com/Ascend910B4-memory |
+| 910B4-1 | huawei.com/Ascend910B4-1 | huawei.com/Ascend910B4-1-memory |
+| 310P3 | huawei.com/Ascend310P | huawei.com/Ascend310P-memory |
\ No newline at end of file