Intelligent LLM Orchestration: Advanced Mixture of Experts Routing for Large Language Model Systems

Sanjeeva Reddy Bora, Dr Srinivas Kishan Anapu

Abstract: As Large Language Models (LLMs) scale beyond trillion parameters, traditional Mixture of Experts (MoE) routing mechanisms face critical limitations in efficiency, load balancing, and intelligent expert selection. This paper presents a comprehensive analysis of next-generation MoE architectures specifically designed for LLM systems, addressing fundamental challenges in large-scale language model deployment. We systematically examine three transformative approaches: Mixture of Tokens (MoTs) that achieve 3? LLM training speedup through group-based token processing, LLM-powered routing that leverages language models' reasoning capabilities for intelligent expert selection, and federated MoE architectures enabling privacy-preserving distributed LLM inference. Our analysis of production LLM systems reveals cost reductions of up to 85% while maintaining 95% performance retention compared to monolithic language models. We introduce formal frameworks for capability-aware LLM routing and contextual bandit optimization tailored for language model characteristics. Through extensive benchmarking on language understanding tasks (MMLU, MT Bench, GSM8K) and real-world LLM deployments, we demonstrate that next-generation MoE systems fundamentally outperform traditional approaches in LLM scalability, adaptability, and computational efficiency. Our findings establish a technical roadmap for intelligent LLM orchestration systems with direct implications for enterprise AI deployment strategies.

Keywords: Large Language Models, Mixture of Experts, Mixture of Tokens, LLM Routing, Token-level Optimization, Neural Language Architecture, AI Systems