genai-dac-specialist

name	genai-dac-specialist
description	Expert guidance for OCI Dedicated AI Clusters — private LLM hosting, fine-tuning, sizing, and cost optimisation for enterprise deployments
version	1.0.0
platform	["claude-code","cline","cursor","roocode"]
activation	{"cline":"@skills/genai-dac-specialist/SKILL.md","cursor":"@skills/genai-dac-specialist/SKILL.md"}

OCI GenAI Dedicated AI Cluster (DAC) Specialist

You are an expert in Oracle's Dedicated AI Clusters for private LLM hosting and fine-tuning.

When to Use

• Deploying private LLM infrastructure on OCI
• Sizing and costing DAC deployments
• Fine-tuning models with proprietary data
• High-throughput inference requirements

Overview

A DAC is a private, isolated GPU cluster in OCI for:

• Hosting LLMs without shared infrastructure
• Fine-tuning with LoRA adapter training on your data
• High throughput with multiple endpoints per cluster
• Data sovereignty — data never leaves your tenancy

Sizing Guide

Workload	Units	Approx Monthly	Use Case
Dev/Test	2-5	Verify pricing	Experimentation
Production	5-15	Verify pricing	Standard workloads
High Volume	15-30	Verify pricing	Enterprise scale
Enterprise	30-50	Verify pricing	Maximum throughput

IMPORTANT: Always verify current pricing at oracle.com/cloud/price-list

Model Selection for DAC

Model	Best For	Context
Cohere Command A	Complex reasoning, enterprise	256K
Cohere Command R/R+	RAG-optimized, retrieval	128K
Llama 3.3 70B	Fine-tuning, customization	128K
Llama 3.1 405B	Maximum capability	128K

Fine-Tuning

Methods

Method	Models	Use Case
LoRA	Llama 3.3 70B, Llama 3.1 405B	Domain-specific adaptation
T-few	Cohere Command	Quick enterprise customization
Vanilla	Cohere Command	Full fine-tune

Process

1. Upload training data → Object Storage (JSONL format)
2. Create fine-tuning job → DAC processes
3. Deploy custom endpoint → Use fine-tuned model
4. Monitor performance → Iterate

Also Consider: AI Blueprints LoRA Fine-Tuning

Before setting up a DAC, check if the AI Blueprints LoRA Fine-Tuning blueprint meets your needs: https://github.com/oracle-quickstart/oci-ai-blueprints

Terraform Quick Start

resource "oci_generative_ai_dedicated_ai_cluster" "main" {
  compartment_id = var.compartment_id
  display_name   = "production-dac"
  type           = "HOSTING"
  unit_count     = 10
  unit_shape     = "LARGE_COHERE"
}

resource "oci_generative_ai_endpoint" "chat" {
  compartment_id         = var.compartment_id
  dedicated_ai_cluster_id = oci_generative_ai_dedicated_ai_cluster.main.id
  model_id               = "cohere.command-a-03-2025"
  display_name           = "chat-endpoint"
}

Cost Optimization

1. Start with on-demand endpoints — validate before committing to DAC
2. Begin with 5 units, scale based on actual usage
3. Multi-model endpoints — share DAC across use cases
4. Monitor utilization — scale down if under 60%

Documentation

• DAC Overview
• Fine-Tuning Guide
• OCI Fine-tuning Repo

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Cline Activation

To use this skill in Cline, reference it at the start of your message:

@skills/genai-dac-specialist/SKILL.md

Size a Dedicated AI Cluster for a production Cohere Command A deployment with 500 concurrent users.

Or in a .clinerules workflow:

## DAC Sizing
When asked about private LLM hosting on OCI, load @skills/genai-dac-specialist/SKILL.md. Always verify pricing, recommend starting small (5 units), and check AI Blueprints before recommending a full DAC.

Triggers: Dedicated AI Cluster, DAC, private LLM hosting, OCI fine-tuning, GPU cluster sizing, model fine-tuning OCI