/serving-runtime-config Skill

Configure custom ServingRuntime custom resources on Red Hat OpenShift AI. Use when built-in runtimes (vLLM, NIM, Caikit+TGIS) do not support the target model framework, or when customizing an existing runtime's parameters (env vars, model format, container image).

Prerequisites

Required MCP Server: openshift (OpenShift MCP Server)

Required MCP Tools (from openshift):

• resources_get - Inspect existing ServingRuntime CRs in detail
• resources_list - List ServingRuntime and ClusterServingRuntime CRs (OpenShift fallback)
• resources_create_or_update - Create fully custom ServingRuntime CR (when not using templates, or as fallback)

Preferred MCP Server: rhoai (RHOAI MCP Server) — used when available, automatic OpenShift fallback on failure

Preferred MCP Tools (from rhoai):

• list_serving_runtimes - List available runtimes and platform templates with supported model formats
• create_serving_runtime - Instantiate a serving runtime from a platform template (no YAML needed)
• list_data_science_projects - Validate namespace is an RHOAI project

Optional MCP Server: ai-observability (AI Observability MCP)

Optional MCP Tools (from ai-observability):

• list_models - Verify deployed models use the new runtime

Common prerequisites (KUBECONFIG, OpenShift+RHOAI cluster, KServe, verification protocol): See skill-conventions.md.

Fallback templates: See openshift-fallback-templates.md for OpenShift YAML templates used when RHOAI tools are unavailable.

When to Use This Skill

Use this skill when you need to:

• Create a custom ServingRuntime for a framework not covered by built-in runtimes
• Customize an existing runtime's parameters (env vars, container image, model format)
• Instantiate a platform template runtime into a namespace
• List and compare available serving runtimes and templates

Do NOT use this skill when:

• You want to deploy a model using an existing runtime (use /model-deploy)
• You need NIM platform setup (use /nim-setup)
• You need to troubleshoot a deployment (use /debug-inference)

Workflow

Step 1: Validate Target Namespace

Ask the user for:

• Namespace: Target namespace for the ServingRuntime

MCP Tool: list_data_science_projects (from rhoai)

Parameters: none

Verify the user-specified namespace is an RHOAI Data Science Project.

If rhoai unavailable or returns error: Use resources_list (from openshift) with apiVersion: v1, kind: Namespace, labelSelector: opendatahub.io/dashboard=true.

Error Handling:

• If namespace not found in project list -> Report: "Namespace [namespace] is not an RHOAI Data Science Project. Use /ds-project-setup to create one, or specify a different namespace." WAIT for user decision.

Step 2: Gather Requirements

Ask the user for:

• Use case: What framework/model needs serving? (e.g., "ONNX model", "custom TensorRT engine", "vLLM with custom args")
• Intent: New runtime from scratch, or customize an existing one?

Document Consultation (read before listing runtimes):

1. Action: Read supported-runtimes.md using the Read tool to understand available runtimes and their capabilities
2. Output to user: "I consulted supported-runtimes.md to understand available runtimes."

MCP Tool: list_serving_runtimes (from rhoai)

Parameters:

• namespace: validated namespace from Step 1 - REQUIRED
• include_templates: true - REQUIRED (shows both existing runtimes and platform templates)

If rhoai unavailable or returns error: Use resources_list (from openshift) with apiVersion: serving.kserve.io/v1alpha1, kind: ServingRuntime, namespace: [namespace] for namespace runtimes, and kind: ClusterServingRuntime for platform templates. Filter by label opendatahub.io/dashboard=true and check spec.supportedModelFormats for compatibility.

Present findings in a table:

The response distinguishes between:

• Existing runtimes (source: "namespace") - ready to use with /model-deploy
• Platform templates (source: "template", requires_instantiation: true) - must be instantiated first

If an existing runtime fits the user's need, recommend using it directly with /model-deploy. If a platform template fits, offer to instantiate it (Step 5 alternative). Otherwise, proceed to Step 3 for custom runtime creation.

WAIT for user to confirm whether to create a new runtime, instantiate a template, or customize an existing one.

Step 3: Determine Runtime Configuration

Based on the user's framework and model requirements, determine the ServingRuntime spec.

If customizing an existing runtime:

MCP Tool: resources_get (from openshift)

Parameters:

• apiVersion: "serving.kserve.io/v1alpha1" - REQUIRED
• kind: "ServingRuntime" - REQUIRED
• namespace: user-specified namespace - REQUIRED
• name: name of the existing runtime to customize - REQUIRED

Extract the current spec as a starting point. Present the current configuration and ask what the user wants to change.

If the user requests a runtime for an unfamiliar framework -> Trigger live doc lookup:

1. Action: Read live-doc-lookup.md using the Read tool for the lookup protocol
2. Output to user: "Framework [name] is not in my cached runtimes. I'll look up its serving requirements."
3. Use WebFetch to retrieve specs from Red Hat OpenShift AI documentation
4. Extract: container image, model format name, supported protocols, required env vars
5. Output to user: "I looked up [framework] on [source] to confirm its runtime requirements: [summary]"

Collect runtime parameters:

WAIT for user to confirm or modify parameters.

Step 4: Generate ServingRuntime YAML

Generate the ServingRuntime manifest using values from Steps 2-3.

apiVersion: serving.kserve.io/v1alpha1
kind: ServingRuntime
metadata:
  name: [runtime-name]
  namespace: [namespace]
  labels:
    opendatahub.io/dashboard: "true"
  annotations:
    openshift.io/display-name: "[Display Name]"
spec:
  supportedModelFormats:
    - name: [model-format-name]
      version: "[version]"
      autoSelect: true
  multiModel: false
  containers:
    - name: kserve-container
      image: [container-image:tag]
      ports:
        - containerPort: 8080
          protocol: TCP
      env:
        - name: [ENV_VAR_NON_SECRET]
          value: "[non-sensitive-value]"
        - name: [SECRET_ENV_VAR]
          valueFrom:
            secretKeyRef:
              name: [k8s-secret-name]
              key: [secret-key-name]
      resources:
        limits:
          nvidia.com/gpu: "[gpu-count]"
        requests:
          cpu: "[cpu]"
          memory: "[memory]"

Display the ServingRuntime YAML to the user, redacting any sensitive values.

Ask: "Proceed with creating this ServingRuntime? (yes/no/modify)"

WAIT for explicit confirmation.

• If yes -> Proceed to Step 5
• If no -> Abort
• If modify -> Ask what to change, regenerate YAML, return to this step

Step 5: Create ServingRuntime

If instantiating from a platform template (user chose a template from Step 2):

MCP Tool: create_serving_runtime (from rhoai)

Parameters:

• namespace: target namespace - REQUIRED
• template_name: name of the template to instantiate (e.g., "vllm-cuda-runtime-template") - REQUIRED

The response includes the created runtime name, display name, and supported model formats.

If rhoai unavailable or returns error: Use resources_get (from openshift) to fetch the ClusterServingRuntime template, copy its spec to a namespace-scoped ServingRuntime, and create via resources_create_or_update (from openshift). See openshift-fallback-templates.md for the pattern.

If creating a fully custom runtime (custom container image, non-template configuration):

MCP Tool: resources_create_or_update (from openshift)

Parameters:

• manifest: full ServingRuntime manifest as JSON string - REQUIRED
• namespace: user-specified namespace - REQUIRED

Error Handling:

• If namespace not found -> Report error, suggest creating namespace or using /ds-project-setup
• If runtime name already exists -> Ask user: "ServingRuntime [name] already exists. Update it? (yes/no)"
• If CRD not found -> Report: "ServingRuntime CRD not available. Ensure Red Hat OpenShift AI operator is installed."
• If RBAC error -> Report insufficient permissions

Step 6: Validate Runtime

MCP Tool: list_serving_runtimes (from rhoai)

Parameters:

• namespace: user-specified namespace - REQUIRED
• include_templates: false

Verify the runtime appears in the namespace runtime list.

If rhoai unavailable or returns error: Use resources_list (from openshift) with apiVersion: serving.kserve.io/v1alpha1, kind: ServingRuntime, namespace: [namespace] for namespace runtimes, and kind: ClusterServingRuntime for platform templates. Filter by label opendatahub.io/dashboard=true and check spec.supportedModelFormats for compatibility.

For detailed inspection:

MCP Tool: resources_get (from openshift)

Parameters:

• apiVersion: "serving.kserve.io/v1alpha1" - REQUIRED
• kind: "ServingRuntime" - REQUIRED
• namespace: user-specified namespace - REQUIRED
• name: the created runtime name - REQUIRED

Report results showing: runtime name, namespace, model format, container image, and next steps (/model-deploy to deploy a model using this runtime).

Common Issues

For common issues (GPU scheduling, OOMKilled, image pull errors, RBAC), see common-issues.md.

Issue 1: InferenceService Cannot Find Runtime

Error: InferenceService status shows "Unknown" or runtime not matched

Cause: The modelFormat.name in the InferenceService does not match any supportedModelFormats[].name in available ServingRuntimes.

Solution:

1. Verify the model format name matches exactly (case-sensitive)
2. Check the runtime is in the same namespace as the InferenceService
3. Ensure the runtime has opendatahub.io/dashboard: "true" label

Issue 2: Runtime Port Mismatch

Error: InferenceService created but health checks fail, endpoint returns connection refused

Cause: The containerPort in the ServingRuntime does not match the port the serving framework actually listens on.

Solution:

1. Check the framework's documentation for its default serving port
2. Update the containerPort in the ServingRuntime spec
3. Or set an environment variable to configure the framework's listen port to match

Dependencies

MCP Tools

See Prerequisites for the complete list of required and optional MCP tools.

Related Skills

• /model-deploy - Deploy a model using the configured runtime
• /nim-setup - NIM platform setup (if NIM runtime is needed instead)
• /debug-inference - Troubleshoot InferenceService failures after deployment

Reference Documentation

• supported-runtimes.md - Runtime capabilities and model format names
• live-doc-lookup.md - Protocol for fetching specs for unknown frameworks

Critical: Human-in-the-Loop Requirements

See skill-conventions.md for general HITL and security conventions.

Skill-specific checkpoints:

• After namespace validation (Step 1): confirm namespace or redirect to /ds-project-setup
• After listing existing runtimes (Step 2): confirm whether to create new or customize existing
• After collecting parameters (Step 3): confirm runtime configuration
• Before creating ServingRuntime (Step 4): display full YAML, confirm
• NEVER overwrite an existing ServingRuntime without user confirmation