rhoai-model-deployment

name	rhoai-model-deployment
metadata	{"author":"rhoai3-demo","version":"1.0.0","platform-family":"rhoai","platform-baseline":"repo","ocp-baseline":"repo","skill-group":"RHOAI Platform"}
description	Use when documenting, reviewing, deploying, or operating Red Hat OpenShift AI model deployment user workflows from the official Deploying models guide: model storage in S3, URI, PVC, or OCI containers/modelcars; building OCI model images; dashboard Deploy a model wizard; generative and predictive model deployment; automatic serving-runtime selection; hardware profile matching; manual runtime selection; administrator llm-d override behavior; Rolling update versus Recreate deployment strategy; AI asset endpoint registration; external routes; token authentication; MLServer deployment settings; CLI deployment from OCI images with ServingRuntime and InferenceService; NVIDIA NIM model deployment; endpoint token lookup; and runtime-specific inference endpoint paths for Caikit, OpenVINO, vLLM, Triton, and MLServer. Do NOT use for model-serving platform enablement, runtime template configuration, vLLM runtime arguments, or NIM platform enablement (use rhoai-model-serving-platform), deployed model metrics and day-2 operations (use rhoai-model-management-monitoring), TrustyAI bias or drift monitoring (use rhoai-monitoring-trustyai), model catalog or registry workflows before deployment handoff (use rhoai-model-catalog-workflows or rhoai-model-registry-workflows), MaaS governance (use rhoai-maas-governance), llm-d distributed inference (use rhoai-distributed-inference-llmd), or live cluster changes without the OpenShift safety guard.

RHOAI Model Deployment

Use this skill for official Red Hat OpenShift AI model deployment and inference request workflows on the active product baseline in docs/PLATFORM_BASELINE.md.

Source Grounding

Read references/source-capture.md before using product behavior details. Official Red Hat documentation is product authority. This skill adapts the official Deploying models guide to this repo's GitOps and README review model.

Scope

This skill covers:

• storing models before deployment in S3, URI sources, PVCs, or OCI images
• OCI model storage, also called modelcars in KServe
• building OCI model images with Podman and a shell-capable base image
• uploading model files to a model-storage PVC through a running workbench IDE
• Deploy a model wizard workflow for generative and predictive models
• model location, model type, deployment name, hardware profile, resources, serving runtime, model framework, replicas, and advanced settings
• automatic serving-runtime selection based on model type, model format, and hardware profile
• hardware profile matching, selection limitations, manual runtime selection, and administrator distributed-inference override behavior
• Rolling update versus Recreate deployment strategies
• AI asset endpoint registration for Gen AI studio playground testing
• external route and token authentication choices
• MLServer deployment settings and MLSERVER_MODEL_URI
• CLI deployment of OCI-stored models with ServingRuntime, InferenceService, storageUri, and imagePullSecrets
• NVIDIA NIM model deployment workflow after NIM platform enablement
• deployed model token lookup and inference endpoint lookup
• runtime-specific inference paths for Caikit TGIS, OpenVINO Model Server, vLLM, NVIDIA Triton, and MLServer
• dashboard and OpenShift metric handoff for deployed models

Use other skills for adjacent work:

• rhoai-model-serving-platform for KServe platform enablement, NIM platform enablement, preinstalled/custom runtime enablement, runtime templates, vLLM runtime arguments, and default deployment strategy configuration
• rhoai-model-management-monitoring for deployed-model metrics, KServe timeouts, multi-node vLLM operations, Kueue routing, Grafana dashboards, autoscaling, and NIM metrics operations
• rhoai-monitoring-trustyai for TrustyAI model observations, bias metrics, data-drift metrics, and OVMS support boundaries
• rhoai-gen-ai-playground for AI asset endpoint testing in Gen AI studio
• rhoai-model-catalog-workflows for catalog discovery, model card review, and catalog-to-deployment handoff
• rhoai-model-registry-workflows for registered model/version deployment handoff and OCI ModelCar transfer jobs
• rhoai-project-workflows for projects, connections, storage, and project access
• rhoai-s3-object-storage-data for S3-compatible object storage operations and credentials
• rhoai-nvidia-gpu-accelerators for NVIDIA GPU enablement and hardware profiles
• rhoai-maas-governance for MaaS subscriptions, model refs, authorization policies, API keys, observability, external OIDC, and external models
• rhoai-distributed-inference-llmd for LLMInferenceService, Gateway discovery, Connectivity Link auth, scheduler, autoscaling, and flow-control workflows

Demo Policy

For this repo:

• Prefer the standard model serving platform with vLLM NVIDIA GPU runtime for the active private model deployment path unless the project explicitly introduces another serving platform.
• Use NVIDIA GPU hardware profiles for generative model deployment unless the active platform baseline changes.
• Treat the Deploy a model wizard as the source for user workflow narratives. Convert durable deployment intent into GitOps manifests only after fields are verified against official docs or active CRD schema.
• Prefer OCI model storage/modelcars for reproducible demo model artifacts when supported by the chosen runtime and model format.
• Keep private OCI registry credentials, object storage credentials, and model endpoint tokens in Kubernetes Secrets. Never commit secret values.
• Enable external routes only when the demo needs external clients. Require token authentication for shared demo endpoints unless there is a documented reason not to.
• Use Recreate for scarce GPU demo environments unless zero-downtime rollout and 200% resource headroom are available and documented.
• Add generative model deployments as AI asset endpoints when they should be tested in Gen AI studio playground.
• Do not make NVIDIA NIM part of the demo unless the project explicitly adopts NIM and documents NGC account, GPU, storage, and credential handling.
• Treat MLServer ServingRuntime for KServe and Spyre x86 paths as Technology Preview where the official guide labels them that way.

Workflow

1. Confirm the active baseline in docs/PLATFORM_BASELINE.md.
2. Read references/source-capture.md and references/official-doc-extraction.md.
3. Decide whether the task is:
   • model storage and artifact preparation
   • dashboard deployment workflow
   • runtime auto-selection or manual selection review
   • deployment strategy choice
   • AI asset endpoint, route, or token-auth setup
   • MLServer deployment detail
   • OCI model deployment by CLI
   • NIM model deployment
   • endpoint token lookup or inference request testing
4. Use examples/model-deployment-patterns.md for compact review patterns.
5. For live cluster work, follow the OpenShift safety guard in AGENTS.md.
6. Validate with references/validation-checklist.md.

References

• references/source-capture.md
• references/official-doc-extraction.md
• references/validation-checklist.md
• examples/model-deployment-patterns.md