MoEITS: A Green AI approach for simplifying MoE-LLMs

MoEITS is a nascent research project (2 days old, 0 stars) focusing on MoE (Mixture-of-Experts) simplification. While the 'Green AI' narrative is timely, the project currently lacks any measurable defensive moat or adoption. The field of MoE optimization is a 'red ocean' of research populated by well-funded frontier labs (OpenAI, Google) and high-velocity open-source collectives (e.g., Mistral, DeepSpeed-MoE, MegaBlocks). The risk of platform domination is high because the primary beneficiaries of MoE efficiency are the model providers and inference hardware vendors themselves (NVIDIA, AWS, Google Cloud), who are incentivized to bake these optimizations directly into their stacks. Without a significant technical breakthrough that is non-obvious to researchers at Google or Meta, or a rapid integration into industry-standard libraries like vLLM or bitsandbytes, this project remains a vulnerable academic implementation. The '2604' arXiv date in the metadata likely indicates a placeholder or error, further suggesting the repo is in an extremely early, unverified state.