Nesa stands as the first network to execute end-to-end decentralized model querying, revolutionizing the AI ecosystem by combining on-chain innovation with decentralized execution. Here’s how Nesa reshapes the landscape of artificial intelligence technology.
Containerized AI Models and Query Templates
Nesa introduces a groundbreaking framework by containerizing AI models and query templates on-chain. This system seamlessly integrates:
- Ecosystem of Augmentative Services: From vector storage to Retrieval-Augmented Generation (RAG), Nesa supports a rich array of off-chain services.
- Decentralized AI Inference Execution: NES miners pull containers, execute them locally on Nesa’s decentralized Trusted Execution Environment (TEE) compute, and instantly reach validation consensus.
- Privacy-Preserving Transactions: Using zero-knowledge proofs (ZK proofs), query responses are reported on-chain in a fully privacy-preserving manner.
Model Config Specificity
Ensuring model consistency and inference reliability is paramount. Nesa’s AI Infrastructure Template (AIT) rigorously defines every aspect of the computational environment:
- OS and Hardware Specs: Standardized configurations eliminate variability across AI stacks.
- Precise Compilation Options: By specifying compilation flags and versions, uniformity is achieved.
This meticulous approach guarantees uniform model execution and predictable outcomes, forming a robust foundation for decentralized AI.
Decentralized Inference Protocol
Trust and reproducibility lie at the core of Nesa’s decentralized inference protocol. The network ensures this by:
- Standardized Execution Protocols: Nodes follow a strict series of steps, including initialization, data input, model execution, and output handling.
- Reliable Model Behavior: Predictable and real-time inference is achieved through uniform execution flow.
This protocol empowers the network to maintain consistency and transparency across all nodes.
Hybrid Enhanced ZK Privacy
Nesa’s hybrid enhanced privacy system employs:
- Commit-Reveal Paradigm: A two-phase transaction structure to deter dishonest behavior and free-riding.
- Trustworthy Inference Results: Nodes are incentivized to compute honestly, ensuring users receive reliable and verifiable outputs.
By leveraging these privacy-preserving mechanisms, Nesa sets a new standard for decentralized AI networks.
VRF and Pseudo-Random Seed for Deterministic Results
Many AI models rely on randomness during inference, presenting challenges for reproducibility. Nesa addresses this by:
- Fixed Random Seed: Ensures deterministic pseudo-random number generation during inference.
- Verifiable Random Functions (VRFs): Provides unpredictable and unbiased public randomness when necessary.
This ensures that all executions yield consistent results, bolstering trust in the network’s reliability.
Kernel Validation Testing
Before storing an AIT kernel on the blockchain, it undergoes comprehensive validation:
- Compliance Testing: Ensures adherence to the specified configuration template.
- Neural Arbiter Network (NAN): Simulates multi-node scenarios to verify deterministic execution and system immunity to variances.
This rigorous process guarantees that every kernel operates seamlessly, providing a secure and reliable foundation for AI workloads.
The Future of Decentralized AI
By addressing key challenges in AI inference — from reproducibility to privacy — Nesa redefines what’s possible in decentralized AI networks. With its robust infrastructure, standardized execution, and privacy-first approach, Nesa empowers developers, miners, and users to build and utilize AI models with unparalleled trust and efficiency.
