Introduction

Artificial Intelligence today is dominated by centralized GPU cloud providers, creating a landscape that is expensive, inaccessible for smaller teams, and often restrictive due to privacy and compliance concerns. While open-source models such as LLaMA, Mistral, and Qwen have democratized access to cutting-edge research, deploying and running them still requires high-cost compute infrastructure, making experimentation and integration into applications difficult outside of well-funded enterprises.

At the same time, the world’s most abundant compute resource—billions of smartphones—remains largely untapped. Modern mobile devices ship with multi-core CPUs, dedicated AI accelerators, and ample memory, yet they are used primarily for lightweight consumer apps rather than serious AI workloads. EdgeMob changes this paradigm by transforming smartphones into active participants in a distributed AI compute network.

EdgeMob introduces a new category of infrastructure: mobile-native AI compute. Instead of depending on remote datacenters, developers can load both open-source and custom models directly into the EdgeMob app and run inference locally or expose them via the EdgeMob API Gateway. This empowers developers to prototype, test, and deploy AI features without incurring cloud costs or sacrificing data privacy.

Beyond developers, Web3 applications gain a decentralized AI backend that can power use cases across DeFi, DeSci, NFTs, gaming, and social applications. By integrating with Solana, EdgeMob ensures that routing, incentives, and governance are secured on-chain, while the inference itself happens on mobile devices at the edge.

The motivation behind EdgeMob is clear: bring AI closer to users, leverage untapped global compute, and reduce the dependence on centralized GPU monopolies. With the convergence of mobile compute power, open-source models, and decentralized coordination, EdgeMob represents the next evolution of AI infrastructure—one that is cost-efficient, privacy-preserving, and accessible to all.