CloudAI white paper
  • 1.CloudAI Project Overview
    • 1.1 Background and Overview
    • 1.2 Technical Advantages of CloudAI
    • 1.3 Strategic Position of Singapore
  • 2. Market Background
    • 2.1 The Rise of AI and AI Agents and the Huge Market Demand for AI Resources
    • 2.2 The Lack of Credibility in the AI Market
    • 2.3 The Rise of Blockchain, Web3, and the Adoption of Cryptography
  • 3. Solutions Realized By Technological Innovation
    • 3.1 Web3-Based Cloud Service Market
      • 3.1.1 Decentralized Cloud Service Platform
      • 3.1.2 Supporting Diverse Computational Needs
    • 3.2 Intelligent Resource Matching Transactions Under AI Algorithms
      • 3.2.1 AI-Driven Intelligent Matching Engine
      • 3.2.2 Dynamic Adjustment and Forecasting Capabilities
    • 3.3 Economic Distribution Based on Computing Power Contribution
      • 3.3.1 Contribution Incentive Mechanism
      • 3.3.2 Tokenized Computing Power Units
    • 3.4 Tokenization Solution for Computing Power Units
      • 3.4.1 Issuance and Circulation of CLAI Tokens
      • 3.4.2 Token Destruction Mechanism
  • 4.CloudAI Project Introduction
    • CloudAI Project Introduction
    • 4.1 Project Architecture Display and Introduction
    • 4.2 Brief Introduction to Some Project Technologies
    • 4.3 Technology Selection and Implementation
      • 4.3.1 Blockchain Technology
      • 4.3.2 AI Technology
      • 4.3.3 Distributed Storage
    • 4.4 System Interaction and Data Flow
      • 4.4.1 Task Submission Process
      • 4.4.2 Resource Scheduling Process
      • 4.4.3 Reward Allocation Process
    • 4.5 Revenue Sources of the Project Platform
      • 4.5.1 Transaction Fees
      • 4.5.2 Node Participation Rewards
      • 4.5.3 Premium Service Fees
    • 4.6 Technical Advantages of the Platform
    • 4.7 System Scalability and Future Development
    • 4.8 Platform Vision and Objectives
  • 5. CloudAI Project Ecosystem Application Scenarios
    • CloudAI Project Ecosystem Application Scenarios
    • 5.1 Real-time Data Processing and Intelligent Analysis
    • 5.2 Artificial Intelligence Model Training and Deployment
    • 5.3 Precision Medicine and Personalized Treatment
    • 5.4 Autonomous Driving and Intelligent Transportation
    • 5.5 Augmented Reality (AR) and Industrial Simulation
    • 5.6 Supply Chain Management and Internet of Things (IoT)
  • 6. Tokenomics
    • 6.1.Node Assets Introduction
      • 6.1.1 Node Introduction
      • 6.1.2 Node Classification
    • 6.2. Token Assets Introduction
      • 6.2.1 Token Introduction
      • 6.2.2 CLAI Issuance Mechanism and Destruction Strategy
    • 6.3. Token Application and Deflationary Scenarios
      • 6.3.1 CLAI Application Scenarios
      • 6.3.2 CLAI’s Deflation Scenario
  • 7.Project Development Plan
    • 7.1. Initial Deployment Phase (January 2025 - December 2025)
    • 7.2. Development Phase (January 2026 - June 2027)
    • 7.3. Maturity Phase (July 2027 - December 2028)
  • 8.Team Introduction
    • Team Introduction
  • 9. Investment Risk Disclosure
    • Investment Risk Disclosure
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  1. 5. CloudAI Project Ecosystem Application Scenarios

5.2 Artificial Intelligence Model Training and Deployment

  • Application Fields: Language Model Development, Computer Vision Training, Automated Customer Service Systems

  • Problems Solved:

    • Efficient Computing Power Support: CloudAI integrates distributed computing resources, significantly accelerating the training process of natural language processing (NLP) and computer vision models, shortening the R&D cycle.

    • Dynamic Computing Power Allocation: Based on the scale and complexity of the training tasks, the platform provides elastic computing power support, optimizes resource utilization, and reduces computing costs.

    • Seamless Model Deployment: CloudAI provides a one-stop model training and deployment service, helping developers quickly launch models.

Actual Value: CloudAI supports the entire AI development process, from training to deployment, enabling R&D teams to focus on innovation, lower the barriers to computing power needs, and accelerate the implementation of AI applications.

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