ECH0-PRIME GPU Deployment Guide
Quick Start Options
Option 1: Google Colab (Recommended - $10/month)
- Go to https://colab.research.google.com/
- Create new notebook
- 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)
- Go to https://www.kaggle.com/
- Create notebook with GPU accelerator
- Upload echo-prime files
- Run:
python deploy_gpu.py
Option 3: RunPod (Spot pricing)
- Go to https://www.runpod.io/
- Select RTX 4090 community GPU
- Deploy with echo-prime code
- 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
- Persistence: Use cloud storage for important data
- Monitoring: Check GPU usage regularly
- Scaling: Start small, scale based on needs
- Backup: Regular data backups to cloud storage