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    • Introduction to Nesa
    • Why Nesa Represents the Future of the AI Economy
    • Why Centralized AI Infrastructure Is Broken
    • Why Decentralized AI Has Not Worked (Yet)
    • Organization of the Documentation
  • Background and Exploratory Notes
    • Private Inference for AI
    • Decentralized Execution for AI
      • Why Decentralized AI?
      • DAIs: Building Decentralized AI Applications on Nesa
      • Models on Nesa: Playground and Custom Uploads
      • Trust and Incentives: Miner Reputation
      • Efficient Inference: MetaInf and Execution Optimization
      • Background and Exploratory Notes
        • Blockchain-based Sequential Neural Sharding (BSNS)
        • Model Partitioning and Deep Network Sharding
        • Dynamic Sharding of Arbitrary Neural Networks
        • Cache Optimization to Enhance Efficiency
        • Enhanced MTPP Slicing of Topological Order
        • BSNS with Parameter-efficient Fine-tuning via Adapters
        • Swarm Topology
        • Free-Riding Prevention
        • Validation, Reputation, and Miner Lifecycle
    • $NES Utility
    • Background and Exploratory Notes
    • Getting Started
    • Via Web
    • Via SDK
    • Via IBC
    • Prerequisites
    • Installation
    • Troubleshooting
    • FAQ
  • Additional Read
    • nesa.ai
    • Nesa Playground
    • Nesa Chain Explorer
    • Nesa EVM Explorer
    • Nesa Discord
    • Nesa Twitter
    • Terms of Service
    • Privacy Policy
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For the complete documentation index, see llms.txt. This page is also available as Markdown.
  1. Major Innovations
  2. Decentralized Execution for AI

Background and Exploratory Notes

Blockchain-based Sequential Neural Sharding (BSNS)Model Partitioning and Deep Network ShardingDynamic Sharding of Arbitrary Neural NetworksCache Optimization to Enhance EfficiencyEnhanced MTPP Slicing of Topological OrderBSNS with Parameter-efficient Fine-tuning via AdaptersSwarm TopologyFree-Riding PreventionValidation, Reputation, and Miner Lifecycle
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