Originally published on tamiz.pro.
The proliferation of Large Language Models (LLMs) has pushed the boundaries of what's possible in AI, yet their immense computational and data requirements often centralize their deployment. Distributed AI offers a compelling alternative, enabling models to operate across multiple nodes, leveraging collective resources and enhancing resilience. This article deep-dives into deploying and optimizing a 'Mesh LLM' architecture using iroh, a decentralized data synchronization and distribution platform, to build truly scalable and robust machine learning systems.
The Challenge of Centralized LLMs
Traditional LLM deployments often rely on centralized infrastructure, leading to several challenges:
Single Point of Failure: A central server going down can halt operations.
