New papers target MoE LLM efficiency with pruning and quantization

A batch of arxiv papers propose methods for compressing Mixture-of-Experts LLMs, including expert pruning without calibration data and joint structural pruning with mixed-precision quantization. Techniques like coalition-aware expert pruning and routing-consistent quantization aim to reduce memory footprint while preserving model quality.