AI Infrastructure Mesh
A decentralized AI compute network connecting global data centers with quantum-resistant encryption and adaptive load balancing.
Decentralized AI Infrastructure
The AI Infrastructure Mesh is a decentralized computing network that connects global AI training nodes through quantum-resistant encryption. This system enables real-time load balancing, dynamic resource allocation, and secure federated learning across 772+ data centers worldwide.
Performance
27 PFlops continuous processing
Nodes
+12,000 active GPUs
Encryption
Post-quantum Lattice
Latency
< 1.2ms regional hops
Architecture Highlights
Dynamic Resource Allocation
Intelligently distributes training workloads across the network based on real-time node availability and geographic proximity to training datasets.
Quantum-Secure Networking
Uses lattice-based cryptography to protect model weights and training data across its global network of 772+ secure nodes.
Federated Learning
Enables collaborative AI development without sharing raw training data through secure multi-party computation protocols.
Technical Foundation
Node Requirements
- Compute: 4+ RTx6000 or equivalent
- Memory: 256GB RAM min
- Storage: NVMe SSD x4
- Network: 10Gbps dual redundancy
Latency Optimization
Research Ecosystem
Distributed Model Training
Explores decentralized model training using secure aggregation protocols across 12+ global edge locations.
📄 Research PaperOn-Demand Compute Pools
Dynamic allocation of GPU resources to prevent bottlenecks during large-scale inference workloads.
📄 Research PaperQuantum Threat Modeling
Analysis of post-quantum security requirements for decentralized machine learning infrastructure.
📄 Research PaperJoin the Network
Help build the decentralized AI future. Our network is actively seeking new node operators.