AMQ (LLM quantization): superseded — cited as a baseline and beaten by newer methods. 1 paper(s) critique it, 1 beat it on benchmarks — #33 of 80 most-superseded. Sub-problem: cluster led by AMQ. Newer alternatives in the same sub-problem include SignRoundV2.

LLM quantization

AMQ

AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models

Superseded baseline#33 of 80 most-superseded · first seen Sep 15, 2025

Superseded — cited as a baseline and beaten by newer methods

1 papers critique it · 1 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites AMQ as a baseline.

it remains relatively slow for large-scale models

— SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

Beaten on benchmarks

Head-to-head results where a newer method reports beating AMQ. Values are copied from the source paper's tables — verify against the cited paper.

SignRoundV2 beats AMQ
68.57 vs 58.65
Avg · [Llama3.1-8B, Avg Bits 2.5]
SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

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Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.

SignRoundV2SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
Dec 4, 2025