4. User Guides

4.1 For GPU Providers

Hardware Requirements

1. GPU Specifications

   • Minimum Requirements:

     • CUDA 7.0+ compute capability

     • 8GB VRAM

     • 256-bit bandwidth

     • 250W TDP

     • Active cooling system

   • Recommended Specifications:

     • CUDA 8.0+ compute capability

     • 16GB VRAM

     • 384-bit bandwidth

     • 350W TDP

     • Advanced cooling solution

2. Network Requirements

   • Minimum 10Mbps stable connection

   • Low latency (<100ms)

   • Stable uptime

   • Port accessibility

Setup Process

1. Initial Configuration

   Step 1: Register your GPU
   - Provide GPU specifications
   - Run benchmark tests
   - Complete verification process
   
   Step 2: Configure Settings
   - Set availability schedule
   - Define pricing strategy
   - Configure resource limits
   - Set up monitoring tools
   
   Step 3: Deploy Node
   - Enable WebGPU access
   - Configure security settings
   - Set up payment details
   - Test connection

2. Optimization Settings

   • Power management configuration

   • Thermal threshold settings

   • Performance optimization

   • Network configuration

Earnings Calculation

1. Base Rate Formula:

   Earnings = (GPU_Power × Time × Base_Rate) × 
              (Demand_Multiplier + Performance_Bonus)

2. Bonus Factors:

   • Uptime percentage

   • Performance metrics

   • User ratings

   • Long-term commitment

Performance Optimization

1. Hardware Optimization

   • Driver updates

   • Cooling optimization

   • Power management

   • Memory management

2. Network Optimization

   • Bandwidth allocation

   • Latency reduction

   • Connection stability

   • Traffic prioritization

4.2 For GPU Renters

Finding Suitable GPUs

1. Search Criteria

   • Performance requirements

   • Budget constraints

   • Availability needs

   • Location preferences

2. Comparison Tools

   • Performance benchmarks

   • Price comparison

   • Provider ratings

   • Historical data

Cost Estimation

1. Pricing Factors

   - Base GPU rental rate
   - Duration multiplier
   - Performance requirements
   - Network usage
   - Additional services

2. Cost Optimization

   • Bulk rental discounts

   • Long-term commitments

   • Off-peak usage

   • Resource optimization

Usage Guidelines

1. Best Practices

   - Monitor resource usage
   - Schedule tasks efficiently
   - Maintain stable connection
   - Regular performance checks

2. Resource Management

   • Task scheduling

   • Load balancing

   • Error handling

   • Performance monitoring

4.3 For AI Model Users

Model Selection

1. Evaluation Criteria

   - Model architecture
   - Performance metrics
   - Resource requirements
   - Cost implications

2. Use Case Matching

   • Task requirements

   • Performance needs

   • Budget constraints

   • Scaling considerations

Integration Guide

1. API Integration

   // Example API integration
   const neurolovClient = new NeurolovClient({
     apiKey: 'your-api-key',
     modelId: 'model-identifier',
     config: {
       maxRetries: 3,
       timeout: 30000
     }
   });

2. SDK Implementation

   // Example SDK usage
   import { NeurolovSDK } from '@neurolov/sdk';
   
   const sdk = new NeurolovSDK({
     credentials: 'your-credentials',
     region: 'preferred-region'
   });

Training Process

1. Data Preparation

   • Dataset requirements

   • Preprocessing steps

   • Validation methods

   • Quality checks

2. Training Configuration

   - Hyperparameter selection
   - Resource allocation
   - Monitoring setup
   - Performance metrics

Results & Analytics

1. Performance Monitoring

   • Real-time metrics

   • Cost tracking

   • Resource utilization

   • Error analysis

2. Optimization Tools

   • Performance tuning

   • Resource optimization

   • Cost efficiency

   • Scaling options

Common Issues & Solutions

1. Performance Issues

   Problem: Low inference speed
   Solution: 
   - Check GPU utilization
   - Optimize batch size
   - Verify network connection
   - Monitor resource allocation

2. Resource Management

   Problem: High resource consumption
   Solution:
   - Implement load balancing
   - Optimize model architecture
   - Use caching strategies
   - Monitor memory usage