Installing NVIDIA GPU Drivers on SUSE Linux Micro

Publication Date: 15 Sep 2025

WHAT?: NVIDIA GPU drivers use the full potential of the GPU unit.
WHY?: To learn how to install NVIDIA GPU drivers on SUSE Linux Micro to fully use the computing power of the GPU in higher-level applications, such as AI workloads.
EFFORT: Understanding the information in this article and installing NVIDIA GPU drivers on your SUSE Linux Micro host requires less than one hour of your time and basic Linux administration skills.
GOAL: You can install NVIDIA GPU drivers on a SUSE Linux Micro host with a supported GPU card attached.

Revision History: Installing NVIDIA GPU Drivers on SUSE Linux Micro

1 Installing NVIDIA GPU drivers on SUSE Linux Micro #

1.1 Introduction #

This guide demonstrates how to implement host-level NVIDIA GPU support via the open-driver on SUSE Linux Micro 6.1. The open-driver is part of the core SUSE Linux Micro package repositories. Therefore, there is no need to compile it or download executable packages. This driver is built into the operating system rather than dynamically loaded by the NVIDIA GPU Operator. This configuration is desirable for customers that want to pre-build all artifacts required for deployment into the image, and where the dynamic selection of the driver version via Kubernetes is not a requirement.

1.2 Requirements #

If you are following this guide, it assumes that you have the following already available:

At least one host with SUSE Linux Micro 6.1 installed, physical or virtual.
Your hosts are attached to a subscription as this is required for package access.
A compatible NVIDIA GPU installed or fully passed through to the virtual machine in which SUSE Linux Micro is running.
Access to the root user—these instructions assume you are the root user, and not escalating your privileges via sudo.

1.3 Considerations before the installation #

1.3.1 Select the driver generation #

You must verify the driver generation for the NVIDIA GPU that your system has. For modern GPUs, the G06 driver is the most common choice. Find more details in the support database.

This section details the installation of the G06 generation of the driver.

1.3.2 Additional NVIDIA components #

Besides the NVIDIA open-driver provided by SUSE as part of SUSE Linux Micro, you might also need additional NVIDIA components. These could include OpenGL libraries, CUDA toolkits, command-line utilities such as nvidia-smi, and container-integration components such as nvidia-container-toolkit. Many of these components are not shipped by SUSE as they are proprietary NVIDIA software. This section describes how to configure additional repositories that give you access to these components and provides examples of using these tools to achieve a fully functional system.

1.4 The installation procedure #

On SUSE Linux Micro host, open up a transactional-update shell session to create a new read/write snapshot of the underlying operating system so that we can make changes to the immutable platform.
```
# transactional-update shell
```

When you are in the transactional-update shell session, add a package repository from NVIDIA. This allows pulling in additional utilities, for example, nvidia-smi.

transactional update # zypper ar \
  https://developer.download.nvidia.com/compute/cuda/repos/sles15/x86_64/ \
  cuda-sle15
transactional update # zypper --gpg-auto-import-keys refresh

Install the Open Kernel driver KMP and detect the driver version.

transactional update # zypper install -y --auto-agree-with-licenses \
  nvidia-open-driver-G06-signed-cuda-kmp-default
transactional update # version=$(rpm -qa --queryformat '%{VERSION}\n' \
  nvidia-open-driver-G06-signed-cuda-kmp-default \
  | cut -d "_" -f1 | sort -u | tail -n 1)

You can then install the appropriate packages for additional utilities that are useful for testing purposes.
```
transactional update # zypper install -y --auto-agree-with-licenses \
nvidia-compute-utils-G06=${version}
```
Exit the transactional-update session and reboot to the new snapshot that contains the changes you have made.
```
transactional update # exit
# reboot
```

After the system has rebooted, log back in and use the nvidia-smi tool to verify that the driver is loaded successfully and that it can both access and enumerate your GPUs.

# nvidia-smi

The output of this command should show you something similar to the following output. In the example below, the system has one GPU.

Fri Aug  1 14:53:26 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.133.20             Driver Version: 570.133.20     CUDA Version: 12.8     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       On  |   00000000:00:1E.0 Off |                    0 |
| N/A   34C    P8             10W /   70W |       0MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

1.5 Validation of the driver installation #

Running the nvidia-smi command has verified that, at the host level, the NVIDIA device can be accessed and that the drivers are loading successfully. To validate that it is functioning, you need to validate that the GPU can take instructions from a user-space application, ideally via a container and through the CUDA library, as that is typically what a real workload would use. For this, we can make a further modification to the host OS by installing nvidia-container-toolkit.

Open another transactional-update shell.
```
#  transactional-update shell
```
Install the nvidia-container-toolkit package from the NVIDIA Container Toolkit repository.
```
transactional update # zypper ar \
"https://nvidia.github.io/libnvidia-container/stable/rpm/"\
nvidia-container-toolkit.repo
transactional update # zypper --gpg-auto-import-keys install \
  -y nvidia-container-toolkit
```
The nvidia-container-toolkit.repo file contains a stable repository nvidia-container-toolkit and an experimental repository nvidia-container-toolkit-experimental. Use the stable repository for production use. The experimental repository is disabled by default.
Exit the transactional-update session and reboot to the new snapshot that contains the changes you have made.
```
transactional update # exit
# reboot
```
Verify that the system can successfully enumerate the devices using the NVIDIA Container Toolkit. The output should be verbose, with INFO and WARN messages, but no ERROR messages.
```
# nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml
```
This ensures that any container started on the machine can employ discovered NVIDIA GPU devices.
You can then run a Podman-based container. Doing this via podman gives you a good way of validating access to the NVIDIA device from within a container, which should give confidence for doing the same with Kubernetes at a later stage.
Give Podman access to the labeled NVIDIA devices that were taken care of by the previous command and simply run the bash command.
```
# podman run --rm --device nvidia.com/gpu=all \
  --security-opt=label=disable \
  -it registry.suse.com/bci/bci-base:latest bash
```
You can now execute commands from within a temporary Podman container. It does not have access to your underlying system and is ephemeral—whatever you change in the container does not persist. Also, you cannot break anything on the underlying host.
Inside the container, install the required CUDA libraries. Identify their version from the output of the nvidia-smi command. From the above example, we are installing CUDA version 12.8 with many examples, demos and development kits to fully validate the GPU.
```
# zypper ar \
  http://developer.download.nvidia.com/compute/cuda/repos/sles15/x86_64/ \
  cuda-sle15-sp6
# zypper --gpg-auto-import-keys refresh
# zypper install -y cuda-libraries-12-8 cuda-demo-suite-12-8
```

Inside the container, run the deviceQuery CUDA example of the same version, which comprehensively validates GPU access via CUDA and from within the container itself.

# /usr/local/cuda-12.8/extras/demo_suite/deviceQuery Starting...

 CUDA Device Query (Runtime API)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla T4"
  CUDA Driver Version / Runtime Version          12.8 / 12.8
  CUDA Capability Major/Minor version number:    7.5
  Total amount of global memory:                 14914 MBytes (15638134784 bytes)
  (40) Multiprocessors, ( 64) CUDA Cores/MP:     2560 CUDA Cores
  GPU Max Clock rate:                            1590 MHz (1.59 GHz)
  Memory Clock rate:                             5001 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 4194304 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  1024
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 3 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 0 / 30
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.8, CUDA Runtime Version = 12.8, NumDevs = 1, Device0 = Tesla T4
Result = PASS

From inside the container, you can continue to run any other CUDA workload—such as compilers—to run further tests. When finished, you can exit the container.

# exit

Important

Changes you have made in the container and packages you have installed inside will be lost and will not impact the underlying operating system.

2 Legal Notice #

Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or (at your option) version 1.3; with the Invariant Section being this copyright notice and license. A copy of the license version 1.2 is included in the section entitled “GNU Free Documentation License”.

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All information found in this book has been compiled with utmost attention to detail. However, this does not guarantee complete accuracy. Neither SUSE LLC, its affiliates, the authors, nor the translators shall be held liable for possible errors or the consequences thereof.