Decentralized AI Inference Architecture
Traditional AI inference relies on centralized cloud providers (AWS, GCP, Azure). A DePIN flips this model by aggregating millions of idle consumer GPUs worldwide into a resilient, cost-effective compute network.
This simulation focuses on inference — executing pre-trained models — which is stateless, parallelizable, and ideal for distribution across heterogeneous hardware.
Result: dramatically lower costs, censorship resistance, edge latency reduction, and passive income for node operators.
Live Network Simulation
Enable your node and observe real-time job routing.
Your Node
OFFLINEShare idle GPU memory with the network
Orchestration Log
Live eventsOperational Flow
Onboarding
Install client → hardware fingerprint → stake reputation
Routing
Request arrives → select best nodes by latency, price, cache
Settlement
Result verified → instant micro-payment via smart contract
Analysis
Advantages
- CostUp to 80% cheaper than cloud providers
- ResilienceNo single point of failure or control
- LatencyEdge compute closer to users
- ScaleMillions of nodes globally
Challenges
- VerificationProving correct inference (ZK or redundancy)
- BandwidthConsumer upload limits model distribution
- PrivacyPrompt exposure without encryption
- ReliabilityNodes go offline unpredictably