Local AI Deployment Overview

Local AI Deployment

This section provides comprehensive Standard Operating Procedures (SOPs) for deploying local Large Language Models (LLMs) across different use cases and technical requirements.

Available Deployment Options

We support five primary deployment approaches, each optimized for specific scenarios:

1. LLM Container + Goose UI

• Containerized model with desktop interface
• Ideal for users wanting UI comfort with technical isolation
• Requires Docker but provides clean separation

2. Terminal-Only LLM Container

• Minimal, privacy-focused deployment
• Perfect for technical users and high-security environments
• CLI-driven with maximum control

3. LM Studio Local Runner

• Simplest “local ChatGPT” experience
• Single-user desktop application
• No containers required

4. Goose + n8n + LLM+Agent Containers

• Automated workflows and scheduled tasks
• Complete automation stack
• For teams needing recurring AI operations

5. Goose Standalone (Windows)

• One-app solution for non-technical users
• Windows-only, maximum simplicity
• No Docker required

Getting Started

1. Review the Decision Flowchart to identify the best approach for your needs
2. Consult the Client Comparison Sheet for plain-language options
3. Use the Technician Cheat Sheet for quick reference during deployment
4. Follow the detailed SOP for your chosen deployment method

Support Information

All SOPs are maintained at version 1.01.26 and include:

• Hardware requirements and recommendations
• Step-by-step installation procedures
• Validation and troubleshooting guidance
• Privacy and security considerations

For technical support or consultation, refer to the specific SOP relevant to your deployment choice.