Architecture Overview
Architecture Overview
The EdgeMob platform is built around the concept of turning mobile devices into first-class compute nodes for artificial intelligence. Instead of treating smartphones as endpoints that only consume AI, EdgeMob repositions them as active participants in a distributed compute fabric. The architecture integrates mobile devices, a lightweight orchestrator, and Solana-based coordination to provide a scalable and resilient network.
Core Components
Mobile Nodes Every participating smartphone runs the EdgeMob app, which acts as both a runtime for AI inference and a gateway to the broader network. Mobile nodes can load models, serve inference locally, or participate in distributed compute tasks.
Orchestrator At the heart of each mobile node is a lightweight orchestrator, implemented in Rust and C++, that manages model execution, resource allocation, and communication with the network. The orchestrator ensures workloads are balanced, memory is efficiently utilized, and inference results are reliably returned.
API Gateway The EdgeMob API Gateway provides a bridge between developers and the network. Initially centralized for reliability, it will evolve into a decentralized routing layer that leverages WebSockets and Solana smart contracts to connect applications with active mobile nodes.
Solana Backbone Solana provides the trust, coordination, and incentive layer for EdgeMob. Node operators stake and earn EGMO tokens, while governance and workload routing are managed through smart contracts. This ensures transparency, security, and decentralization.
Design Principles
Decentralization: Avoid single points of failure by distributing inference across nodes.
Scalability: Enable workloads to scale horizontally across thousands of devices.
Flexibility: Support both local inference and distributed execution for different model sizes.
Interoperability: Provide APIs compatible with Web2 (REST, GraphQL) and Web3 (wallet-based, RPC) ecosystems.
In summary, the EdgeMob architecture blends mobile-native compute with decentralized coordination, creating a new paradigm for AI delivery that is cost-efficient, private, and scalable.
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