Method Drift›Mixture-of-experts routing
Superseded baseline#241 of 1,370 most-superseded
uniform bit-width quantization
Mixture-of-experts routing
superseded — cited as a baseline and beaten by newer methods
2 papers critique it · 0 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites uniform bit-width quantization as a baseline.
“such approaches overlook the sparsity inherent to the MoE architecture, leading to suboptimal performance”
— GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs“vanilla uniform bit-width quantization and expert pruning based solely on routing scores struggle to maintain performance at extremely high compression ratios”
— MC#: Mixture Compressor for Mixture-of-Experts Large Models
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- May 22, 2026
- May 21, 2026
- KBVQ-MoEKBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language ModelsJan 30, 2026
- Oct 13, 2025