SUSE AI Requirements

SUSE AI Requirements

 
Publication Date: 2026-01-29
WHAT?

Hardware, software and networking requirements for successful deployment and operation of SUSE AI.

WHY?

To ensure efficient operation of SUSE AI.

EFFORT

Less than 30 minutes of reading and a basic knowledge of hardware and SUSE Rancher Prime: RKE2 environment.

This article describes the software, hardware and networking requirements for the cluster nodes where you plan to install SUSE AI.

1 SUSE AI hardware requirements

 

For successful deployment and operation, SUSE AI has the same hardware prerequisites as an SUSE Rancher Prime: RKE2 cluster. For requirements of individual applications, refer to Section 3, “Application-specific requirements”.

1.1 Recommended hardware (basic functionality)

 
RAM

At least 32 GB of RAM per node. This is the minimum recommendation for the control plane node. Additional resources may be needed for the worker nodes based on workload.

CPU

A multicore processor with a minimum of 4 cores. 8 cores or more may be necessary depending on the cluster scale and application demands.

Disk space

  • 50 GB or more is recommended for control plane nodes.

  • Additional space for data storage, such as application data or log files, is required depending on the deployment scale and the workloads running on the cluster.

  • SSDs or high-speed storage are preferred for faster data access and efficient operation of containerized workloads.

Networking

  • A reliable and stable network connection between all nodes in the cluster.

  • Cluster nodes must have valid DNS A records following the *.apps.<CLUSTER_DOMAIN> pattern. The nodes must be able to communicate with each other and access external resources, such as container images or software updates.

  • Ensure that all nodes have public IP addresses or are accessible via VPN or other private network if deploying across multiple data centers.

1.2 Recommended hardware (for High Availability)

 
Important
Important

While 32 GB of RAM is the minimum for basic functionality, a production-grade deployment with high availability, multi-node clusters, or running resource-intensive applications like AI/ML workloads might require more.

RAM

64 GB or more per node is recommended for larger clusters or to run applications with high resource demands.

CPU

At least 8 cores, ideally 16 or more cores, depending on the expected load.

Disk space

  • For larger-scale clusters or persistent storage applications, 100 GB or more of disk space per node might be required.

  • Using high-performance SSDs is recommended, especially for workloads with high I/O requirements, such as databases or AI/ML model training.

Networking

Ensure a low-latency, high-throughput network for efficient communication between nodes, especially if deploying in multi-region or multi-cloud environments.

Tip
Tip

For more detailed hardware recommendations, refer to the official SUSE Rancher Prime: RKE2 installation requirements documentation at https://documentation.suse.com/cloudnative/rke2/latest/en/install/requirements.html.

2 GPU hardware for AI/ML workloads

 

To run AI/ML workloads, such as training machine learning models or running inference workloads, deploy cluster nodes with compatible NVIDIA GPUs to gain acceleration.

2.1 Using the NVIDIA GPU Operator

 

Configuring and managing nodes with hardware resources can require multiple configurations for software components. These include drivers, container runtimes and libraries. To use NVIDIA GPUs in a Kubernetes cluster, you need to configure the NVIDIA GPU Operator. Because GPU is a special resource in the cluster, you need to install the following components to enable deployment of workloads for processing on the GPU.

  • NVIDIA drivers (to enable CUDA)

  • Kubernetes device plug-in

  • Container runtime

  • Other tools to provide capabilities such as monitoring or automatic node labeling

To ensure that the NVIDIA GPU Operator is installed correctly, the Kubernetes cluster must meet the following prerequisites:

  • All worker nodes must run the same operating system version to use the NVIDIA GPU Driver container.

  • Nodes must be configured with a container engine, such as Docker (CE/EE), containerd or Podman.

  • Nodes should be equipped with NVIDIA GPUs.

  • Nodes should have NVIDIA drivers installed.

2.2 Supported GPUs

 

The NVIDIA GPU Operator is compatible with a range of NVIDIA GPUs. For a full list of supported GPUs, refer to the NVIDIA GPU Operator Platform Support documentation.

3 Application-specific requirements

 

The SUSE AI stack consists of multiple applications. We recommend running each application on nodes that meet or exceed the corresponding hardware requirements.

3.1 SUSE Rancher Prime requirements

 

3.1.1 Minimum hardware requirements

 
Nodes for HA setup

At least 3 nodes.

RAM

A minimum of 32 GB of RAM.

CPU

At least 8 CPU cores.

Disk space

At least 200 GB of storage, preferably SSD.

3.1.2 For more information

 

For more detailed recommendations, refer to the following official documentation:

3.2 SUSE Security requirements

 

3.2.1 Minimum hardware requirements

 
Nodes for HA setup

The following container instances run on existing cluster nodes:

  • 1 Manager instance

  • 3 Controller instances

  • 1 Enforcer instance on each cluster node

  • 2 Scanner & Updater instances

RAM

A minimum of 2 GB of RAM.

CPU

At least 2 CPU cores.

Disk space

At least 5 GB of storage, preferably SSD.

3.2.2 For more information

 

For more detailed recommendations, refer to the following official documentation:

3.3 SUSE Observability requirements

 

3.3.1 Minimum hardware requirements

 
Nodes for HA setup

At least 3 nodes.

RAM

A minimum of 32 GB of RAM.

CPU

At least 16 CPU cores.

Disk space

At least 5 GB of storage, preferably SSD.

3.3.2 For more information

 

For more detailed recommendations, refer to the following official documentation:

3.4 Milvus requirements

 

This topic describes requirements for the Milvus application.

3.4.1 Hardware requirements

 
3.4.1.1 Minimum requirements
 

The following requirements are for basic Milvus deployment on a single node or a small scale.

RAM

A minimum of 32 GB of RAM.

CPU

At least 8 CPU cores.

Disk space

At least 100 GB of storage, preferably SSD.

Networking

A stable connection with 1 Gbps network bandwidth.

3.4.1.2 Recommended hardware for large-scale workloads
 

The following requirements are for multi-node Milvus clusters or heavy workloads, such as large vector databases.

RAM

A minimum of 64 GB of RAM per node.

CPU

8–16 CPU cores per node or more.

Disk space

500 GB or more of high-speed storage, ideally SSD or NVMe SSD.

Networking

10 Gbps Ethernet or faster for high-performance clusters.

3.4.1.3 CPU instruction set requirements
 

The following CPU instruction sets are required for Milvus:

  • SSE4.2

  • AVX

  • AVX2

  • AVX-512

You can list the supported CPU sets on your host by running the following command:

> grep -m1 '^flags' /proc/cpuinfo

3.4.2 Software requirements

 

Running Milvus requires specific versions of the following software:

Kubernetes

SUSE-supported versions of SUSE Rancher Prime: RKE2 that use Kubernetes 1.18 or higher.

Helm

The recommended version is 3.5.0 or later.

3.4.3 Additional considerations

 
Disk and storage

  • Storage type: SSDs or NVMe SSDs are highly recommended for fast read/write access to large datasets and high-performance vector retrieval.

  • Metadata and data storage: For large-scale deployments, ensure that metadata and vector data are stored on fast disks (SSD or NVMe).

Network

For high-performance clusters, especially for large-scale deployments, ensure high-bandwidth network connectivity between nodes.

3.4.4 For more information

 

For more detailed hardware recommendations, refer to the official Milvus and prerequisite Docker documentation.

3.5 Ollama requirements

 

The version of Ollama provided with SUSE AI is optimized for NVIDIA GPU hardware. This section guides you through the steps for configuring Ollama on an NVIDIA-enabled system, including necessary configurations for both the hardware and software.

Tip
Tip: General recommendations

3.5.1 Hardware requirements

 
NVIDIA GPU

  • The recommended GPU models include Tesla, A100, V100, RTX 30 series, or other compatible NVIDIA GPUs.

  • Ensure that the CUDA Compute Capability of your GPU is compatible with the required version of Ollama.

RAM

At least 16 GB of RAM is recommended. However, higher amounts (32 GB or more) may be necessary for larger models or workloads.

Disk space

At least 50 GB of free disk space is recommended for storing the container images and any data files processed by Ollama.

3.5.2 Software requirements

 
NVIDIA Docker (nvidia-docker)

You must install nvidia-docker (the NVIDIA Container Toolkit) to allow Docker containers to use the GPU. Refer to https://documentation.suse.com/cloudnative/rke2/latest/en/advanced.html#_deploy_nvidia_operator for more details.

CUDA Toolkit

You must install the CUDA version supported by your GPU model. For most recent GPUs, CUDA 11.0 or later is required. Refer to CUDA Toolkit installation guide for more details.

NVIDIA driver

Install the NVIDIA driver compatible with your GPU model. Its version must be compatible with the installed CUDA toolkit.

Tip
Tip

You can check your GPU driver version by running the nvidia-smi command.

3.6 Open WebUI requirements

 

While Open WebUI has no specific hardware dependencies beyond those of the underlying platform, consider the following guidelines for optimal performance.

  • Because Open WebUI shares most dependencies with Milvus and Ollama, follow the hardware requirements mentioned in Section 3.4, “Milvus requirements” and Section 3.5, “Ollama requirements”.

  • Stable network connection is essential, particularly if Open WebUI is integrated with other services or databases. Ensure sufficient bandwidth for Web traffic and API calls.

  • To interact with the Open WebUI interface, use standard Web browsers such as Google Chrome, Mozilla Firefox or Microsoft Edge.