Nesa AI
  • 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
  • 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
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