genai-dac-specialist

To size an OCI Dedicated AI Cluster for private LLMs, you must calculate the required unit count based on your workload demands and budget. This process enforces DAC sizing guidelines and cost-optimization practices to ensure right-sized capacity for multi-endpoint inference.

The best way to deploy private endpoints for LLM inference on OCI is by using Terraform deployment patterns provided for Dedicated AI Clusters. This approach ensures data sovereignty and controlled access while provisioning isolated endpoints for your models.

Yes, you can run LoRA-based fine-tuning on a Dedicated AI Cluster. The cluster supports fine-tuning workflows that enable you to adapt models and deploy them directly to your private DAC endpoints for inference.

OCI Dedicated AI Clusters support data sovereignty and multi-endpoint inference by providing private, isolated hosting for LLMs. This ensures controlled access and scalable operations for production deployments requiring strict data governance.

You should use a Dedicated AI Cluster instead of shared OCI GenAI endpoints when your production deployment requires strict data sovereignty, private model hosting, and custom LoRA-based fine-tuning workflows that shared infrastructure cannot securely isolate.

Limitations of OCI Dedicated AI Clusters for cost optimization involve rigid initial unit provisioning that requires careful sizing to avoid over-provisioning. You must strictly apply DAC sizing guidelines and Terraform patterns to manage varying workloads within budget constraints.