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

Quality of Service

In addition to compute power contribution, the incentivization mechanism also takes into account the quality of service delivered by GPU providers. The platform may evaluate factors such as uptime, responsiveness, and overall performance to assess the quality of service provided by each GPU provider. Providers that consistently deliver high-quality service are likely to receive higher rewards, fostering a competitive environment that encourages providers to maintain reliable performance.

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Last updated 1 year ago