seohee0925/COMPEL

COMPEL addresses a critical bottleneck in modern LLM architecture: the massive memory footprint of Mixture-of-Experts (MoE) models like Mixtral or DeepSeek. While dense pruning is well-studied, MoE pruning requires managing the routing logic and the varying importance of experts across different layers. The defensibility is low (3) because the project is currently a fresh academic code drop (1 day old, 0 stars) with no community or ecosystem. Its value lies entirely in the algorithmic approach documented in its associated research paper. Frontier labs (OpenAI, Anthropic, Meta) are high-risk competitors as they are internally developing proprietary MoE compression techniques to reduce serving costs. If COMPEL's technique proves superior, it will likely be absorbed into major inference frameworks like vLLM or TensorRT-LLM, rendering the original repo obsolete. The displacement horizon is short (6 months) because the field of LLM compression moves at extreme velocity, with new pruning and quantization methods appearing weekly.