Aethyr — Decentralized AI Orchestration Platform
Edge Deployment

Deploy AI at the Edge

Run Aethyr AI agents on your own devices for maximum privacy, speed, and control. From Raspberry Pi to industrial edge computers.

Why Deploy at the Edge?

Offline Operation

AI works without internet connectivity

Instant Response

Sub-100ms latency for real-time AI

Complete Privacy

Your data never leaves your device

Full Control

Customize everything for your needs

Choose Your Edge Device

Raspberry Pi 4/5 Specifications

Hardware Specs:

CPU:4-core ARM Cortex-A76
Memory:4-8GB RAM
Storage:32GB+ microSD
Connectivity:WiFi 6, Ethernet, Bluetooth

AI Capabilities:

Personal AI assistant deployment
Smart home device control
Local voice processing
Offline AI inference
Basic computer vision tasks

Step-by-Step Deployment

Hardware Preparation

Set up your edge device and verify requirements

Tasks:

Install operating system (Ubuntu 20.04+ recommended)
Configure network connectivity
Update system packages
Install Docker and Docker Compose
Verify hardware acceleration (if applicable)

Commands:

sudo apt update && sudo apt upgrade -y
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker $USER

Real-World Use Cases

Home Automation Hub

Control smart home devices with local AI processing

Recommended Device:

Raspberry Pi 4

Key Benefits:

Works without internet connectivity
Instant response times (<100ms)
Complete privacy - no data leaves your home
Integrates with existing smart home systems

Example:

Deploy a personal assistant that controls lights, thermostat, and security system using natural language commands, all processed locally.

Business Operations Assistant

Automate routine business tasks at the edge

Recommended Device:

Industrial Edge PC

Key Benefits:

Handles sensitive business data locally
Reduces cloud computing costs
Ensures business continuity during outages
Customizable for specific business processes

Example:

Deploy agents that process invoices, manage inventory, and handle customer inquiries without sending data to external servers.

Remote Site Monitoring

AI-powered monitoring in remote locations

Recommended Device:

NVIDIA Jetson Xavier

Key Benefits:

Operates in locations with limited connectivity
Real-time analysis and decision making
Reduces bandwidth requirements
Autonomous operation with periodic sync

Example:

Monitor industrial equipment, analyze sensor data, and trigger maintenance alerts using computer vision and predictive analytics.

Common Issues & Solutions

Agent fails to start

Symptoms:

• Agent status shows "Failed"
• Error messages in logs
• High memory usage

Solutions:

Check available memory and CPU resources
Verify Docker is running and accessible
Review agent configuration for errors
Check network connectivity to Aethyr Cloud

Poor inference performance

Symptoms:

• Slow response times
• High CPU usage
• Timeout errors

Solutions:

Enable hardware acceleration if available
Adjust agent memory allocation
Consider using a more powerful edge device
Optimize model selection for edge deployment

Connectivity issues

Symptoms:

• Intermittent disconnections
• Sync failures
• Cloud communication errors

Solutions:

Configure edge device for offline operation
Set up local caching and buffering
Check firewall and network settings
Implement retry logic for cloud connections

Additional Resources

Support & Community

💬 Community Forum

Get help from other edge deployment enthusiasts and share your experiences.

🛠️ Professional Services

Need help with custom edge deployments? Our experts can assist you.

Ready to Deploy at the Edge?

Start with a simple Raspberry Pi deployment and scale up to industrial edge computing as your needs grow.

Quick Start

• 30-minute setup
• Beginner-friendly guides
• Community support

Advanced Features

• GPU acceleration
• Multi-agent orchestration
• Enterprise integration

Professional Support

• Custom deployments
• 24/7 technical support
• Training and consulting