AI Infrastructure
Building the backbone for decentralized AI computation and secure model deployment in the Web3 ecosystem.
Key Features
- Decentralized AI training and inference networks
- Blockchain-based model versioning and governance
- Privacy-preserving federated learning systems
- Scalable and efficient distributed computing for AI workloads
- Secure enclaves for confidential AI computations
Use Cases
Decentralized Model Marketplaces
Create a trustless ecosystem for AI model sharing, where developers can monetize their models and users can access a wide range of AI capabilities.
Privacy-Preserving AI
Enable AI computations on sensitive data without compromising user privacy, ideal for healthcare and financial applications.
Edge AI Deployment
Efficiently deploy and update AI models across a network of edge devices, ensuring low-latency and offline capabilities.
Distributed AI Research
Facilitate collaborative AI research by providing a decentralized infrastructure for sharing datasets, models, and compute resources.
Technical Details
Core Technologies
- Ethereum-compatible smart contracts for model governance
- IPFS (InterPlanetary File System) for distributed storage
- Libp2p for peer-to-peer networking and discovery
- Trusted Execution Environments (TEEs) for secure computations
Advanced Techniques
- Distributed ledger for immutable record-keeping of model provenance
- Decentralized compute marketplace for matching AI tasks with resources
- Federated learning protocols for collaborative model training
- Homomorphic encryption for computations on encrypted data
Performance Optimizations
- Sharding techniques for improved scalability of blockchain operations
- Layer 2 solutions for faster and cheaper transactions
- Optimistic rollups for bundling multiple operations
- Efficient consensus mechanisms for reduced energy consumption
Get Involved with AI Infrastructure
Join our community of researchers and developers working on cutting-edge AI Infrastructure projects.