A Local Distributed Multi-Agent LLM Ensemble System

Описание: Welcome to my first video! In this video, I explore the architecture and performance of a local distributed multi-agent LLM ensemble system.
We look at how one FastAPI orchestrator manages four diverse AI agents (Llama 3.2, Qwen 2.5, Phi 3 Mini, and Gemma 2) to improve accuracy on complex tasks.
What you’ll learn:
The architecture of a local-first AI cluster designed for privacy and cost predictability.
Five ensemble strategies: Majority Voting, Weighted Voting, ISP, Topic Routing, and Multi-Agent Debate.
Real-world performance benchmarks on MMLU, GSM8K, and TruthfulQA.
The trade-offs between model accuracy and latency—why "Weighted Voting" often beats "Majority Voting."
Resources & Links:
GitHub Repository: https://github.com/ANIS151993/Distributed-...
Interactive Dashboard: Explore the system flow and results online.
Full Research Paper: "A Local Distributed Multi-Agent LLM Ensemble System."
Chapters: 0:00 Intro: Why a Team of AIs? 1:15 System Architecture: Orchestrator & Agents 3:45 5 Strategies for AI Collaboration 6:20 Results: Accuracy vs. Latency Trade-offs 8:50 Reproducibility & Open-Source Tools 10:30 Conclusion & Future Work.
#AI #LLM #MachineLearning #DistributedSystems #OpenSource #MultiAgentSystems.