Simulating complex systems with LLM-driven agents: leveraging AWS ParallelCluster for scalable AI experiments

This blog post discusses using large language models (LLMs) to drive the decision-making processes of agents in an energy supply chain simulation. It presents a proof-of-concept for integrating generative AI agents into simulations to model complex systems and decision-making processes involving human factors.

Specifically, the article covers:

• The conceptual model design with LLM-driven energy producers and utilities as agents
• Integration with AWS services like ParallelCluster, Ray, and Ollama for scalable LLM inference
• Optimizing LLM selection and performance for the simulation
• Simulation results and insights from experiments, including profit maximization, regulated markets, scaling agent populations, and incorporating emotional aspects
• The AWS architecture using ParallelCluster, Ray, and GPU nodes for distributed computing
• Conclusion and invitation to explore the GitHub repository for further details