Grafilab
  • Getting Started
  • Whitepaper
    • Introduction
    • Market Overview
    • Challenges
    • Goal
    • What Are We Building?
    • Grafi Cloud
      • Core Features
      • How It Works
      • Payment and Incentives
      • Advanced Features
    • Grafi Co-Builder
      • Main Services of Co-Builder
      • How It Works
      • Supporting Large-Language-Model
      • Supporting Framework
    • Grafi AI App Store
      • Key Features of the Grafi AI App Store
    • Grafi AI Data Layer
      • Key Components of the Data Layer
      • How the Data Layer Works
    • Ecosystem (Flywheel)
      • Revenue Streams of Grafilab
      • Grafi's Universal Payment and Burning Mechanism
    • Token Info
      • Token Distribution and Allocations
      • Token Utility
      • Token Emission
  • Roadmap
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  1. Whitepaper

What Are We Building?

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Last updated 4 months ago

Grafilab is uniquely positioned to address the growing demands of the AI market through its comprehensive suite of decentralized AI products:

  • Grafi Cloud: By integrating centralized and decentralized GPU resources, Grafi Cloud ensures cost-effective and scalable computational power to support compute-intensive tasks, from AI training to gaming.

  • Grafi Co-Builder: The Co-Builder platform fosters collaboration among developers, offering essential resources for AI apps/agents training, deployment, and project co-creation. This promotes innovation and accelerates the development cycle.

  • AI App Store: Grafilab’s AI App Store empowers developers by providing a seamless platform to deploy and monetize AI applications & agents. Users benefit from an extensive catalog of AI tools tailored to diverse industry needs.

  • AI Data Layer: The Grafi AI Data Layer collects data from sources like CeDePIN Cloud, Inference API, and AI Apps/Agents within the Grafi ecosystem. It validates, labels, and organizes data into swarms, ensuring high-quality inputs for model fine-tuning. This refined data enhances Grafilab's customized MoE LLM and smaller LLMs, catering to diverse AI needs efficiently.