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:

```python
# 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