Is uniform bit-width quantization superseded?

uniform bit-width quantization (Mixture-of-experts routing): cited as a baseline — critiqued by newer work, not yet beaten on a benchmark here. 2 paper(s) critique it, 0 beat it on benchmarks — #241 of 1370 most-superseded. Sub-problem: cluster led by MoEQuant. Newer alternatives in the same sub-problem include BitsMoE, GEMQ, KBVQ-MoE, MC# (Mixture-Compressor-sharp).

Mixture-of-experts routing

uniform bit-width quantization

Superseded baseline#241 of 1,370 most-superseded

Cited as a baseline — critiqued by newer work, not yet beaten on a benchmark here

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

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.

BitsMoEBitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
May 22, 2026
GEMQGEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
May 21, 2026
KBVQ-MoEKBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Jan 30, 2026
MC# (Mixture-Compressor-sharp)MC#: Mixture Compressor for Mixture-of-Experts Large Models
Oct 13, 2025