The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence

TL;DR

The paper introduces The Internet of LLMs, a framework designed to enable scalable, interoperable knowledge exchange and cost-effective training for large language models. It identifies key obstacles—scale, environment configuration, cross-model coordination, and compute costs—and responds with four core innovations: an LLM sharing protocol, a universal LLM environment, an agent optimal path module, and a joint mining mechanism for resource sharing. The architecture combines a Model Network Layer, an LLM Interoperability Layer, and a Decentralized GPU Layer to support cross-platform model exchange, unified development workflows, and distributed computation. By integrating these components with a joint mining incentive model and rigorous evaluation, the framework aims to democratize access to LLM development, accelerate progress toward AGI, and promote greener, more collaborative AI R&D.

Abstract

This paper explores the multi-dimensional challenges faced during the development of Large Language Models (LLMs), including the massive scale of model parameters and file sizes, the complexity of development environment configuration, the singularity of model functionality, and the high costs of computational resources. To address these challenges, this paper proposes three core technical solutions: LLM sharing protocol, LLM universal environment framework, and Agent optimal path module. To solve the computational resource constraints in the early stages of research, we further innovatively propose a joint mining mechanism, achieving bilateral value sharing between computing power providers and model designers, including breakthrough rewards for optimal model paths and long-term profit distribution, thereby providing researchers with cost-optimized computational resource support and promoting the continuous development of LLM research and applications.

Figures (4)

• Figure 1: The illustration of LLM Interoperability Layer.
• Figure 2: The illustration of how agents plan and execute tasks.
• Figure 3: The illustration of how to reflect and critique in automatically finishing tasks.
• Figure 4: The illustration of the joint mining mechanism.