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AI Connect Whitepaper
  • Project Introduction
  • 1. Project Background and Vision
  • 2. Introduction to Computing Power Networks
    • 2.1 Data as a New Factor of Production, Giving Rise to Computing Power Networks
    • 2.2 The Current Distribution of Computing Power Supply and Demand Shows Decentralized and Unbalanced
    • 2.3 The Metaverse Era, Where New Technologies Like VR and AR Are Closely Related to High Bandwidth a
    • 2.4 Computing Power Networks Refer to the Integration of Cloud, Network, and Edge for Unified Comput
    • 2.5 Computing Power Networks Build the Network Foundation for the Development of the Metaverse
  • 3. Market Demand for the Development of Computing Power Networks
    • 3.1 The AI Wave Boosts Computing Power Demand, and Achieving Scalability of Intelligent Technology R
    • 3.2 Industry Applications of Large Model Training Also Require a Large Amount of Intelligent Computi
    • 3.3 The AI Application Has a Long Tail Effect, and Achieving Scalability Requires First Achieving Un
  • 4. AIConnect Computing Power Network Construction Plan
    • 4.1 Computing Power Supply Services
    • 4.2 The Infrastructure and Marketization of Computing Power Scheduling
    • 4.3 The Commercial Demand for Computing Power Scheduling
  • 5. Global Development of Computing Power Networks and DePIN
    • 5.1 Development Advantages of DePIN Combined with AI and Crypto
    • 5.2 AIConnect's Investment in AI Edge Computing Model Training and Development
  • 6. Joint Construction and Participation in AIConnect's Computing Power Network
    • 6.1 Introduction to the Role of AIC Token Assets in AIConnect
    • 6.2 Design Advantages of AIC Tokens
    • 6.3 Introduction to the Business Model of AIC Tokens
      • 6.3.1 AIC Token Production
      • 6.3.2 Node Participation
  • 7. Development Roadmap
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  1. 4. AIConnect Computing Power Network Construction Plan

4.1 Computing Power Supply Services

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Last updated 1 year ago

Computing power can be divided into three types: general computing power, supercomputing power, and intelligent computing power, each applied in different scenarios. General computing power mainly comes from general-purpose processors (CPUs) and has a wide range of applications, suitable for digital scenarios where precision requirements are not high. Supercomputing power primarily supports high-precision scientific research fields such as astrophysics, meteorological studies, and aerospace. These scenarios require a large amount of computation and high precision (double-precision computing power). Intelligent computing power is mainly used in AI scenarios. For AI model training and inference, there is a significant demand for processing text, speech, images, or video, where single-precision, half-precision, or even integer computations can meet application needs. Generally speaking, compared to model training, the precision required for model inference is lower, and in many scenarios, Int8 is sufficient.