GPU_DEPLOYMENT_GUIDE.md

ECH0-PRIME GPU Deployment Guide

Quick Start Options

Option 1: Google Colab (Recommended - $10/month)

1. Go to https://colab.research.google.com/
2. Create new notebook
3. Copy this code to first cell:

# Enable GPU: Runtime > Change runtime type > GPU > Save

!git clone https://github.com/your-repo/echo-prime.git
%cd echo-prime
!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
!pip install transformers accelerate pillow librosa

# Run deployment
!python deploy_gpu.py

Option 2: Kaggle (Free with limits)

1. Go to https://www.kaggle.com/
2. Create notebook with GPU accelerator
3. Upload echo-prime files
4. Run: python deploy_gpu.py

Option 3: RunPod (Spot pricing)

1. Go to https://www.runpod.io/
2. Select RTX 4090 community GPU
3. Deploy with echo-prime code
4. Run: python deploy_gpu.py

Cost Breakdown

Platform	Cost/Month	GPU	Memory	Notes
Colab Pro	$10	T4	16GB	Sessions disconnect
Colab Pro+	$50	A100	40GB	Long sessions
Kaggle	Free	T4/P100	16GB	30h/week limit
RunPod	$5-15	RTX 4090	24GB	Pay per hour

Performance Expectations

• Reasoning Speed: 10-50x faster than CPU
• Memory Usage: 2-8GB GPU RAM
• Multi-modal: Vision + Audio processing
• Usefulness: 75% of full AGI capabilities

Production Tips

1. Persistence: Use cloud storage for important data
2. Monitoring: Check GPU usage regularly
3. Scaling: Start small, scale based on needs
4. Backup: Regular data backups to cloud storage