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  • Nesa Docs
    • Introduction to Nesa
    • Overview of the Nesa System
      • AI Models: Repository, Standardization, Uniformity
      • Users: Why Do We Need Private Inference?
      • Node Runners: Doing Inference and Earning $NES
    • Organization of the Documentation
  • Technical Designs
    • Decentralized Inference
      • Overview
      • Model Partitioning and Deep Network Sharding
      • Dynamic Sharding of Arbitrary Neural Networks
      • Cache Optimization to Enhance Efficiency
      • BSNS with Parameter-efficient Fine-tuning via Adapters
      • Enhanced MTPP Slicing of Topological Order
      • Swarm Topology
      • Additional: Free-Riding Prevention
    • Security and Privacy
      • Overview
      • Hardware Side: Trusted Execution Environments (TEEs)
      • Software/algorithm Side: Model Verification
        • Zero-knowledge Machine Learning (ZKML)
        • Consensus-based Distribution Verification (CDV)
      • Software/algorithm Side: Data Encryption
        • Visioning: Homomorphic Encryption
        • Implementation: Split Learning (HE)
      • Additional Info
        • Additional Info: Trusted Execution Environments (TEEs)
        • Additional Info: Software-based Approaches
    • Overview of $NES
      • $NES Utility
    • The First Application on Nesa: DNA X
    • Definitions
    • Additional Information
      • Dynamic Model Versioning and Fork Management
      • Nesa's Utility Suite
      • The AI Kernel Market
      • Privacy Technology
        • Trusted Execution Environment (TEE)
        • Secure Multi-Party Computation (MPC)
        • Verifiable Random Function (VRF)
        • Zero-Knowledge Proof (ZKP)
      • The Integration of Evolutionary AI to Evolve the Nesa Ecosystem
      • Interoperability and Nesa Future Plans
  • Using Nesa
    • Getting Started
      • Wallet Setup
      • Testnet Nesa Faucet
    • Via Web
      • Your Nesa Account
      • Selecting an AI Kernel
      • Submitting a Query
    • Via SDK
    • Via IBC
    • Via NESBridge
      • On Sei
  • Run a Nesa Node
    • Prerequisites
    • Installation
    • Troubleshooting
    • FAQ
  • Links
    • nesa.ai
    • Nesa Discord
    • Nesa Twitter
    • Nesa dApp: dnax.ai
    • Nesa dApp: DNA X Docs
    • Terms of Service
    • Privacy Policy
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  1. Nesa Docs

Overview of the Nesa System

PreviousIntroduction to NesaNextAI Models: Repository, Standardization, Uniformity

Last updated 1 year ago

We introduce a pioneering new system architecture that is the new standard for blockchain-powered artificial intelligence. Around this standardization, we have three primary parties:

  • AI models that are available on the Nesa system by the model developers

  • Users of Nesa who need affordable, private inference

  • Node runners who execute the decentralized jobs on the Nesa system, including validation, inference, and more

At its foundation, the Nesa system is designed to streamline the AI computation process by providing a consistent set of rules and execution protocols, which every participating parties must follow. This not only guarantees that all nodes produce identical results given the same model parameters and input data, but also relieves node operators from the complexity involved in setting up execution environments, facilitating easier adoption and participation in our network.

In this section, we detail how can leverage Nesa to get inference results by , which are powered by our .

We will soon provide a flow chart of the system interaction here.

users who need private inference
leading AI models
node runners