The Future of Distributed AI Systems

1. Abstract

This paper outlines a decentralized architecture for coordinating large language and vision models across heterogeneous edge devices. We address the constraints of high latency and unpredictable network splits typical of West African urban nodes by formulating a localized spatial-temporal clustering algorithm.

2. Introduction

Traditional cloud-centric AI deployment struggles under fluctuating network bandwidth. Placing computational weight closer to the data collection points — on edge gateways and localized servers — decreases dependency on high-bandwidth trans-oceanic fiber cables. However, edge execution introduces constraints in memory footprint and compute cycles.

3. Methodology

We implement a dynamic task split across clusters. Sub-networks are evaluated for model layer division using a custom gradient sync protocol. Model weights are pruned and compiled using WebAssembly (Wasm) runtimes to guarantee sandboxed, cross-platform execution on regional nodes.

4. Key Findings & Performance

Our decentralized inference layout reduces overall server latency by 48% compared to standard cloud API requests. By utilizing local caching schemes and peer-to-peer weight replication, local cluster networks remain fully operational during standard internet backhaul failures.