--- title: "General" slug: "server-gpuserver-vpc" tags: ["VPC", "GPU Server"] updated: 2026-10-08T06:54:07Z published: 2026-10-08T06:54:07Z canonical: "guide.ncloud-docs.com/server-gpuserver-vpc" --- > ## Documentation Index > Fetch the complete documentation index at: https://guide.ncloud-docs.com/llms.txt > Use this file to discover all available pages before exploring further. # General Available in VPC This guide describes how to create and manage a GPU server in the NAVER Cloud Platform console. For more information on how to create and manage KVM-based GPU servers, see [KVM GPU](/docs/en/server-gpuserver-vpc-kvm). Note - To ensure service continuity without interruption in the event of unexpected server failures or scheduled changes, it is recommended to configure servers with inter-zone redundancy by default. To configure redundancy, see [Load Balancer overview](/docs/en/networking-loadbalancer-loadbalanceroverview). - NAVER Cloud Platform provides a High Availability (HA) structure to prepare for failures in the physical server, such as memory, CPU, and power supply. HA is a policy for preventing hardware failures from expanding into the virtual machine (VM) server. It supports Live Migration, which automatically migrates the VM on the host server to another secure host server when a failure occurs in the aforementioned host server. However, the VM server is restarted if an error prevents Live Migration. If the service is operated on a single VM server, a VM restart may cause a service disruption. To reduce the frequency of failures, it is recommended to configure VM server redundancy as described above. ## Check server information You can view GPU server information in the same way as general server information. For more information, see [Check server information](/docs/en/server-screen-vpc). Caution In the case of GPU Servers, fees are charged even when the server is stopped. ## Create server You can create GPU servers from **Menu** > **All Services** > **Compute** > **Server** in the VPC environment of the console. For more information on how to create a server, see [Create server](/docs/en/server-create-vpc). Note Because GPU server resources are limited, GPU servers are provided after the request is reviewed. If you need to create a GPU server, see [FAQs](https://www.ncloud.com/support/faq/all/3178) and submit a request. ## Manage server You can manage and change GPU server settings in the same way as general servers. For more information, see [Manage server](/docs/en/server-manage-vpc). Note - The specifications of a GPU Server can only be changed to those of the same server type. - Once a GPU Server is created, it cannot be converted into a regular server by removing the GPU. To change to a regular server, you need to create a server image, then use the image to create a new regular server. - You can use the server image created in a regular server to create a GPU Server. ## Reinstall and upgrade the GPU driver/CUDA In the following situations during use of a GPU Server, you can reinstall the GPU driver and CUDA of the server: - If the OS kernel version is changed (updated) and is no longer compatible with the current GPU driver, reinstall the GPU driver only. - If you need to upgrade an older GPU driver currently in use to the latest driver provided by NAVER Cloud Platform - If you need to upgrade the driver to a specific version Note - You may not be able to receive official support with regard to problems occurring after you install drivers other than the default versions provided and installed by NAVER Cloud Platform. - We do not recommend downgrading the driver to a lower version than what is currently provided by NAVER Cloud Platform. - The default versions provided and installed by NAVER Cloud Platform are as follows: (For Windows Server 2016, the R525 version is provided because NVIDIA's service support ended.) | | Linux | Windows Server 2019 | Windows Server 2016 | | --- | --- | --- | --- | | GPU Driver | 535.161.08 | 537.13 | 527.4 | | CUDA | 12.2 | 12.2 | 12.0 | | cuDNN | 8.9.7 | 8.9.7 | 8.8.0 | See the following guides depending on your OS: - [Reinstall GPU driver (Linux)](/docs/en/server-gpuserver-vpc#GPU%EB%93%9C%EB%9D%BC%EC%9D%B4%EB%B2%84%EC%9E%AC%EC%84%A4%EC%B9%98Linux) - [Reinstall CUDA (Linux)](/docs/en/server-gpuserver-vpc#CUDA%EC%9E%AC%EC%84%A4%EC%B9%98Linux) - [Reinstall GPU driver (Windows)](/docs/en/server-gpuserver-vpc#GPU%EB%93%9C%EB%9D%BC%EC%9D%B4%EB%B2%84%EC%9E%AC%EC%84%A4%EC%B9%98Windows) - [Reinstall CUDA (Windows)](/docs/en/server-gpuserver-vpc#CUDA%EC%9E%AC%EC%84%A4%EC%B9%98Windows) ### Reinstall GPU driver (Linux) The driver versions provided by NAVER Cloud Platform can be reinstalled through scripts. If you plan to use a separate version, follow the manual reinstallation procedure. #### Reinstall through scripts To reinstall the GPU driver through scripts: 1. Enter the `wget http://init.ncloud.com/gpu/ncp_gpu_reinstall.sh` commands to download the script file. 2. Enter the `./ncp_gpu_reinstall.sh` commands to delete the existing GPU driver. 3. Reboot the server. 4. Re-enter the `./ncp_gpu_reinstall.sh` commands to reinstall the GPU driver. #### Manual reinstallation If you fail to reinstall the driver through scripts, or if you want to change and install the GPU driver version: 1. Download the driver file of the version you wish to reinstall or upgrade the driver to. - Access the [NVIDIA Driver download website](https://www.nvidia.com/download/index.aspx). 2. Enter the following command to delete the existing GPU driver: ``` # ./NVIDIA-Linux-x86_64-535.161.08.run -s --uninstall Verifying archive integrity... OK Uncompressing NVIDIA Accelerated Graphics Driver for Linux-x86_64 535.161.08.......................................................................................................... ``` - Example: Default version 535.161.08 provided by NAVER Cloud Platform. 3. Reboot the server. 4. Enter the following commands to install the new GPU driver: ``` # ./NVIDIA-Linux-x86_64-535.161.08.run -a --ui=none --no-questions --accept-license Verifying archive integrity... OK Uncompressing NVIDIA Accelerated Graphics Driver for Linux-x86_64 535.161.08............................................................................................................................................................ Welcome to the NVIDIA Software Installer for Unix/Linux (Omitted) Installation of the kernel module for the NVIDIA Accelerated Graphics Driver for Linux-x86_64 (version 535.161.08) is now complete. ``` 5. Reboot the server. 6. Enter the `nvidia-smi` commands to check the version of the successfully installed driver and the model and number of recognized GPU cards. ### Reinstall CUDA (Linux) CUDA operates properly only if cuDNN is reinstalled as well. To install it: 1. Access the [CUDA Toolkit download website](https://developer.nvidia.com/cuda-toolkit-archive). 2. Select the CUDA Runtime installation file for the version to install to bring up the download link. - For the installation type, select **runfile (local)**, which does not depend on the OS. 3. Check the symbolic link of the existing CUDA path, then delete the actual path directory of the existing version. ``` # ll /usr/local/cuda # lrwxrwxrwx 1 root root 21 Apr 16 15:42 /usr/local/cuda -> /usr/local/cuda-12.2/ # rm -rf /usr/local/cuda-12.2 ``` - The existing CUDA Toolkit and cuDNN are deleted. 4. Enter the following command to reinstall CUDA Toolkit: ``` # ./cuda_12.2.2_535.104.05_linux.run --toolkit --toolkitpath=/usr/local/cuda-12.2 --samples --samplespath=/usr/local/cuda-12.2/samples --silent ``` - Example: Default version 12.2 provided by NAVER Cloud Platform. 5. Check the version of the reinstalled CUDA. ``` # nvcc --version ``` 6. Access the [cuDNN download website](https://developer.nvidia.com/rdp/cudnn-archive) to bring up the download link. 7. Download cuDNN from the link. 8. cuDNN installation does not bring up any installer but is completed simply when the file is unzipped in the directory where CUDA is installed. See the following to install: ``` # cd /root # tar -xvf cudnn-linux-x86_64-8.9.7.29_cuda12-archive.tar.xz # mv cudnn-linux-x86_64-8.9.7.29_cuda12-archive cuda # cp cuda/include/cudnn* /usr/local/cuda/include # cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 # chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* ``` - Example: Default version 8.9.7 provided by NAVER Cloud Platform. 9. Check the version of the cuDNN installed. ``` # cat /usr/local/cuda/include/cudnn_version.h | grep -A2 MAJOR #define CUDNN_MAJOR 8 #define CUDNN_MINOR 9 #define CUDNN_PATCHLEVEL 7 ``` - How to check cuDNN 8.x criteria ### Reinstall GPU driver (Windows) To reinstall the GPU driver, you can simply run the script for auto installation. If automatic reinstallation fails, you can reinstall the driver manually. #### Automatic reinstallation To download and run a script file for automatic reinstallation of the GPU driver: 1. Enter the following command to download the script file: ``` Start-BitsTransfer -Source "http://init.ncloud.com/win_gpu/install_gpu.exe" -Destination "c:\install_gpu.exe" ``` 2. Run the install_gpu.exe file. - The NVIDIA GPU driver installation popup appears, and the installation takes about 10–15 minutes. 3. When the installation complete popup window appears, reboot the server. 4. Enter the `run - devmgmt.msc` command to open the device manager console. 5. In the device manager console, double click the NVIDIA graphics card under **Display Adapters**. 6. On the **[Driver]** tab in the properties popup window, check the driver version. 7. Open the cmd window and enter `cd C:\Program Files\NVIDIA Corporation\NVSMI` to relocate the driver, then enter `nvidia-smi`. - You can see that the graphics card has been recognized. #### Manual reinstallation If you are unable to run automatic reinstallation using the script, to reinstall the GPU driver manually: 1. Download the driver file of the version you wish to reinstall or upgrade the driver to from the [GPU driver download website](https://www.nvidia.co.kr/Download/index.aspx?lang=kr). - Example: [Default version 461.33 provided by NAVER Cloud Platform](https://www.nvidia.com/download/driverResults.aspx/170739/en-us/). 2. Run the downloaded GPU driver EXE file to install the driver. - Follow the instructions on the installer popup window. - You must agree to the software's terms of service to use it. - For the installation option, select **Express**. 3. Reboot the server. 4. Enter the `run - devmgmt.msc` command to open the device manager console. 5. In the device manager console, double click the NVIDIA graphics card under **Display Adapters**. 6. On the **[Driver]** tab in the properties popup window, check the driver version. ### Reinstall CUDA (Windows) CUDA operates properly only if cuDNN is reinstalled as well. To install it: 1. Access the [CUDA Toolkit download website](https://developer.nvidia.com/cuda-toolkit-archive). 2. Set the platform, then click the link to download the EXE file. 3. Run the downloaded CUDA EXE file to install CUDA. - Follow the instructions on the installer popup window. - You must agree to the software's terms of service to use it. - For the installation option, select **Express**. 4. Log in to the [cuDNN download website](https://developer.nvidia.com/cudnn-download-survey), then download cuDNN file of the desired version. Note Only members can download cuDNN. If you have no account, sign up as a member, then log in. 5. Unzip the downloaded ZIP file, then replace the bin, include, and lib folders in the CUDA 11.2.2 installation path with folders of the same names from the ZIP file. 6. Open the cmd window and enter `cd C:\Program Files\NVIDIA Corporation\NVSMI` to relocate the driver, then enter `nvidia-smi`. - You can see that the graphics card has been recognized. Note When you run the `nvidia-smi` command, the following information is output: | Item | Description | | --- | --- | | Driver Version | Version of the installed driver | | CUDA Version | CUDA API version supported by the driver | | Name | GPU model name | | Temp | GPU core temperature | | Perf | GPU's performance state - Changes flexibly according to the GPU temperature and power usage | | Pwr:Usage/Cap | Current level of power being used by the GPU | | Memory-Usage | Memory usage by the GPU (current usage/GPU memory capacity) | | Volatile GPU-Util | GPU utilization | | Uncorr. ECC | Number of Uncorrectable Error Correction Code (ECC) occurrences | | MIG M. | Multi Instance GPU (MIG) Mode status - Not applicable to P40, T4, and V100 GPU provided by NAVER Cloud Platform | | Processes | Information of the processes currently using the GPU - GPU: GPU number on which the process is running. - GI ID/CI ID: Information on the GPU instances and compute instances sliced with the Multi-Instance GPU (MIG) feature. - PID, Process name: ID and name of the process. - Type: CUDA/OpenCL processes are displayed as C (Compute), and DirectX/OpenGL processes are displayed as G (Graphics). - GPU Memory Usage: GPU memory usage of the process. | ## Collect/forward diagnostic data through NTK You can collect and forward the NVIDIA debug logs of GPU VM through Ncloud Tool Kit (NTK). The process of collecting and forwarding the debug logs is as follows: [1. Run NTK](/docs/en/server-gpuserver-vpc#1NTK%EC%8B%A4%ED%96%89) [2. Collect GPU debug logs](/docs/en/server-gpuserver-vpc#2GPU%EB%94%94%EB%B2%84%EA%B7%B8%EB%A1%9C%EA%B7%B8%EC%88%98%EC%A7%91) Note For more information on NTK, see Ncloud Tool Kit ([Linux](/docs/en/server-ntk-lin)/[Windows](/docs/en/server-ntk-win)). ### 1. Run NTK To run NTK on a Linux server: 1. Enter the `cd /usr/local/etc` command. - Move to the path where NTK is located. 2. Enter the `tar zxvf ntk.tar.gz` command. - The NTK file is unzipped. - If no ntk.tar.gz file exists or if you wish to replace the existing file with the latest version, enter `wget -P /usr/local/etc http://init.ncloud.com/server/ntk/linux/xen/ntk.tar.gz` to download the file. 3. Enter the `/usr/local/etc/ntk/ntk` commands to run NTK. ### 2. Collect GPU debug logs To collect GPU debug logs in NTK: 1. On the main interface of NTK, select **E EXECUTE - << Run System Apps >>**. ![gpu-server-createvpc_guide28_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide28_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) 2. Select **G GPU DEBUG COLLECTING - FOR LOG COLLECT >>**. ![gpu-server-createvpc_guide29_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide29_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) 3. Select **Yes** to run the log collection script. ![gpu-server-createvpc_guide30_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide30_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) 4. When a log collection success message and the log file storage path are displayed, check the details, then select **OK**. ![gpu-server-createvpc_guide31_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide31_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) 5. Select whether to transfer the log file to NAVER Cloud's technical support center. - If you want to transfer the log file, select **Yes**. The file transfer starts right away. If the file is transferred, a success message and the ShortURL where you can download the log are displayed. - If you do not want to transfer the log file, select **No** to end it. ![gpu-server-createvpc_guide32_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide32_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) ### Transfer created logs To transfer the created logs to NAVER Cloud's technical support center: Note If you are unable to transfer the log file to NAVER Cloud's technical support center due to a network issue, attach and forward the log file stored in the VM. - Log file storage path: /usr/local/etc/ntk/logs/gpu_get_log 1. On the main interface of NTK, select **V VIEW - << View & Upload Logs >>**. ![gpu-server-createvpc_guide34_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide34_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) 2. Select **G - GPU DEBUG FILES**. ![gpu-server-createvpc_guide35_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide35_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) 3. Check the list of log files created, then select the log files to transfer to NAVER Cloud's technical support center. ![gpu-server-createvpc_guide36_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide36_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) 4. Select **Yes**. - The file transfer starts right away. If the file is transferred, a success message and the ShortURL where you can download the log are displayed. ![gpu-server-createvpc_guide37_ko](https://cdn.document360.io/6998976f-9d95-4df8-b847-d375892b92c2/Images/Documentation/gpu-server-createvpc_guide37_en.png?sv=2026-02-06&spr=https&st=2026-11-14T05%3A05%3A04Z&se=2026-11-14T06%3A05%3A04Z&sr=c&sp=r&sig=r154vrENOvMgUKgL7E2kuP5Hin%2FIs1qv7oEMNgieuVQ%3D) ### GPU debug log file types The following are the types of GPU log files created through NTK: | Log file name | Commands used | Role | | --- | --- | --- | | date.log | date | Outputs the log creation date and time | | dmesg-xid.log | dmesg grep -i xid | Outputs the kernel message including xid | | dmesg.log | dmesg | Outputs the kernel message | | free.log | free -m | Outputs the memory usage in MB | | last.log | last | Outputs the login and reboot logs | | ps.log | ps auxf | Checks the process status | | top.log | top -b -n 1 | Outputs top (once in batch mode) and system information | | uptime.log | uptime | Outputs the uptime result | | nvidia-bug-report.log.gz | cell | Runs the nvidia-bug-report.sh script | ## Monitor GPU resources You can use Cloud Insight to monitor the GPU resources. For more information on Cloud Insight, see the [Cloud Insight user guide](/docs/en/cloudinsight-cloudinsightoverview). ### View dashboard In **Menu** > **All Services** > **Management & Governance** > **Cloud Insight (Monitoring)** > **Dashboard** of the NAVER Cloud Platform console, select the **Service Dashboard/Server** dashboard to view the basic server metrics at a glance. - Click **[Change widget data]** to filter the data to be displayed in the widget. - The metrics you can check in relation to GPU servers are as follows: - Current GPU MEM Activity (GPU/vmem_usage (%)): GPU memory controller usage rate = GPU/vmem_usage (%) - Current GPU MEM Usage (GPU/vmem_usage (MiB)): GPU memory usage = GPU/vmem_usage (MiB) - Current GPU Usage: GPU usage = GPU/usage (%) For more information on how to view the dashboard, see [View Cloud Insight dashboard](/docs/en/cloudinsight-cloudinsightconsole#%EB%8C%80%EC%8B%9C%EB%B3%B4%EB%93%9C%EC%A1%B0%ED%9A%8C). ### Add user dashboard You can add user dashboards to monitor only the metrics you want. Click **[Create dashboard]** to create a new dashboard, and then click **[Add widget]** to set the types of widgets and metrics information to be displayed. - To create a widget related to the GPU Server, you must select **Server** as **Product Type** when setting the data. - If you are using a GPU-related metric as setting data, you must add as many dimensions (gpu_idx) as the number of GPUs. For more information on how to create the dashboard additionally, see [Create Cloud Insight dashboard](/docs/en/cloudinsight-cloudinsightconsole#%EB%8C%80%EC%8B%9C%EB%B3%B4%EB%93%9C%EC%83%9D%EC%84%B1).