Algorithm Combinations

AutoRound can be combined with several algorithms before (or during) quantization. This page summarizes each combination and rates it along two dimensions:

• Accuracy Gain — does the transform improve the accuracy of the quantized model compared with plain AutoRound?
• Deployment — can the resulting model actually be deployed/served today (kernel support, export path, real inference engine)?

Legend

Light	Meaning
🟢	Good — clear benefit / ready to deploy
🟡	Partial — conditional benefit / limited or experimental support
🔴	Poor — no measurable benefit / not deployable yet

Note: A Partial/Poor Accuracy Gain rating may stem from two factors: (1) limitations in our current implementation, and (2) our own internal, subjective evaluation. Both are subject to change as the implementation matures and more benchmarks become available.

Matrix

Combination	Accuracy Gain	Deployment	Details	CLI Usage	Comments	Reference
AutoRound + AWQ (activation-aware scaling)	🟢	🟢	awq_details	--algorithm awq,signround	Recommended when activations are quantized (e.g., W4A4).	arXiv:2306.00978
AutoRound + Hadamard rotation	🟢	🔴	rotation_details	--algorithm hadamard,signround	Especially helpful for INT4 (W4A4) and some MXFP4 scenarios. no production kernel.	arXiv:2404.00456
AutoRound + SpinQuant	🟡	🔴	rotation_details	Python API only	Learns rotation matrices; higher accuracy at extra training cost. no production kernel.	arXiv:2405.16406
AutoRound + LFQ (logit-aware final-block quantization)	🔴	🟢	lfq_acc	--enable_lfq	Refines the final block to lift low-bit generation quality	arXiv:2605.29756
AutoRound + MX Attention (mxfp4 variant)	🟡	🔴	mxnv_acc	--data_type mx_fp4_rceil_v2	Adopt 7.25 as the denominator for scale calculation	arXiv:2607.24377
AutoRound + SVDQuant (low-rank outlier absorption)	🟡	🟡	svdquant_details	--algorithm svdquant,signround	Recommended for diffusion models; currently only FLUX is supported.	arXiv:2411.05007