The Expert Interchange Standard: Enabling Dynamic Expert Management in Mixture-of-Experts Language Models

Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling neural networks efficiently by activating only a subset of parameters per input. However, current MoE systems treat experts as static components--once trained, experts cannot be updated without retraining the entire model or complex fine-tuning procedures. We present the Expert Interchange Standard (EIS), a systems framework enabling runtime replacement of individual experts in deployed MoE models. Inspired by proven software engineering patterns--microservices, database transactions, and package management--EIS introduces: (1) a standardized Expert Package Format (EPF) for portable expert representation, (2) an ACID-compliant hot-swap protocol ensuring safe runtime updates, (3) compatibility validation mechanisms for safe expert interchange, and (4) memory-constrained expert caching for resource-limited deployment. This work is a proof-of-concept demonstrating feasibility before scaling. We validate EIS on OLMoE-1B-7B, a production MoE with 1024 experts, showing that a 13GB model can operate within a 2GB memory budget. Key results on Apple M4 CPU: EIS expert swap is 1015x faster than full model reload (0.31ms vs 317ms), cached expert access is 281x faster than disk loading, and quality benchmarks confirm model correctness is preserved. While current throughput (0.5 tokens/sec under memory constraint) is not production-ready, EIS establishes the foundational infrastructure for continuous model improvement, A/B testing, and edge deployment.