model-deploy

To deploy AI/ML models on OpenShift AI using KServe, you can use an automated approach that handles runtime selection, InferenceService CR creation, and rollout monitoring. This process supports vLLM, NVIDIA NIM, and Caikit+TGIS runtimes.

Automating model deployment in OpenShift AI is best handled by a system that automatically selects the appropriate runtime based on model and cluster capabilities. This ensures models are served efficiently while optimizing for available hardware and resource constraints.

Yes, GPU validation is a required step when deploying models on OpenShift AI. The deployment process includes GPU and resource optimization checks to validate that necessary components and resources are correctly configured before the actual rollout.

Yes, you can use vLLM and NVIDIA NIM runtimes to serve inference services on OpenShift AI. The platform supports these runtimes alongside Caikit+TGIS, allowing flexible deployment options tailored to your specific model architecture.

You can monitor rollouts and validate post-deployment status for KServe inference services by using automated deployment tools. These tools track the rollout process and perform post-deployment validation to ensure the model endpoint is functioning correctly.

You need openshift and rhoai MCP servers for AI/ML model deployment because they provide the necessary tools and dependencies to interact with the cluster. They enable core operations like resource management, pod logging, and deploying the model endpoints.