Tag
Proposes a structural pruning framework for MoE models that maximizes channel-score coverage via attribution-based approximation, achieving 50% or 25% pruning with 4-bit quantization and reducing memory footprint by 5.27x on Qwen3-30B-A3B.
A novel end-to-end framework for LLM compression that jointly optimizes structural pruning and mixed-precision quantization, achieving significant perplexity reductions and speedups over state-of-the-art methods, especially at ultra-low bit precisions.