Configuring artificial intelligence (AI) applications in the Kubernetes environment involves using Helm charts and custom values files. While applications ship with default configurations, you can modify application behavior by applying a custom values file with scenario-specific values. This approach ensures consistent, reproducible and easily manageable application deployment.
This guide provides a list of AI applications installable by SUSE AI Factory, along with their descriptions and configuration override examples.
Publication Date: 2026-10-12
- 1 Kubeflow
- 1.1 What components does Kubeflow include?
- 1.2 What are Kubeflow requirements?
- 1.3 How do I get the values list for Kubeflow Helm chart?
- 1.4 How do I install Kubeflow?
- 1.5 How do I install Kubeflow in air-gapped environments?
- 1.6 How do I access Kubeflow?
- 1.7 How do I configure Kubeflow for different scenarios?
- 1.8 How do I harden Kubeflow for production use?
- 1.9 How do I manage user profiles and namespaces?
- 1.10 How do I upgrade Kubeflow?
- 1.11 How do I remove Kubeflow?
- 1.12 Troubleshooting
- 2 LiteLLM
- 3 Milvus
- 4 Ollama
- 5 Open WebUI
- 6 PyTorch
- 7 Qdrant
- 8 vLLM
List of Figures
List of Examples
- 4.1 Enabling or disabling the GPU section
- 4.2 Basic custom values file with GPU and two models pulled at startup
- 4.3 Basic custom values file with Ingress and no GPU
- 5.1 Open WebUI custom values file with Ollama included
- 5.2 Open WebUI custom values file with Ollama installed separately
- 5.3 Open WebUI custom values file with pipelines enabled
- 5.4 Open WebUI custom values file with a connection to vLLM
- 5.5 Stand-alone deployment of open-webui-pipelines
- 6.1 Basic custom values file with GPU enabled
- 6.2 ConfigMap-based upload
- 6.3 Host-folder with files baked into the chart
- 6.4 Git repository clone: public with no authentication
- 6.5 Git repository clone: private with authentication
- 7.1 Basic custom values file when the cluster has no default storage class set.
- 7.2 An example where Qdrant uses GPU capabilities.
- 8.1 Minimal configuration
- 8.2 Validating the installation
- 8.3 Basic configuration
- 8.4 Loading prefetched models from persistent storage
- 8.5 Configuration with multiple models
- 8.6 CPU offloading
- 8.7 Shared remote KV cache storage with LMCache