PIN3 Document
  • Introduction
    • Introduction
    • Founders
    • Vision
    • Links
  • Architecture
    • Overview
    • GPU Provider Nodes
    • User Interface and Access
    • Smart Contracts and Blockchain Integration
    • Data Privacy and Security Measures
  • Products
    • Overview
    • Decentralized GPU Processing Model
    • Optimized Training Speed and Cost Reduction
    • Open Access to AI Training and Software Providers
  • Use Cases
    • Overview
    • Large Language Model Training
    • AI-Generated Picture and Video Training
    • AI-Generated Code Training
    • Incentivization Mechanism for GPU Providers
    • Compute Power Contribution
    • Quality of Service
    • PIN Token Rewards
    • Aligning Interests
    • Promoting Competition
  • Tokenomics
    • The PIN Token
    • Utilities
    • Distribution
  • Governance
    • Decentralized Governance Model
    • PIN3 DAO
    • Proposal
    • Voting Mechanism
  • Roadmap
    • Roadmap
    • Community Engagement and Partnerships
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  1. Use Cases

Compute Power Contribution

GPU providers are rewarded based on the compute power they contribute to the PIN3 network. The more compute power a GPU provider offers, the greater their potential rewards. This encourages GPU providers to allocate a significant portion of their idle GPU compute capability to the platform, ensuring a robust and reliable supply of GPU resources for AI training tasks.

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Last updated 1 year ago