---
title: "SCVQ — paper by Zhou et al. (2026) — summary and ideas"
description: "The authors propose SCVQ, a post-training, weight-only method built on standard K-means VQ. It works in four steps: Weights… 35 references."
authors: ["Zixuan Zhou", "Yujun Diao", "Zicheng Kong", "Dehua Ma", "Zhenbo Xu", "Pei Pei Li", "Zhaofeng He"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.403/"
doi: "10.18653/v1/2026.acl-long.403"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhou-2026-scvq-sparse-compensated-vector-quantization/"
---
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# SCVQ: Sparse-Compensated Vector Quantization for Large Language Models

**Authors:** [Zixuan Zhou](https://md.research.elamur.ai/free/en/authors/zixuan-zhou/), Yujun Diao, Zicheng Kong, Dehua Ma, [Zhenbo Xu](https://md.research.elamur.ai/free/en/authors/zhenbo-xu/), Pei Pei Li and [Zhaofeng He](https://md.research.elamur.ai/free/en/authors/zhaofeng-he/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.403/) · [PDF](https://aclanthology.org/2026.acl-long.403.pdf) · [DOI 10.18653/v1/2026.acl-long.403](https://doi.org/10.18653/v1/2026.acl-long.403) · [OpenAlex](https://openalex.org/W7166829902)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Vector quantization (VQ) compresses LLM weights well at very low bit-widths, but it is slow in practice. Large codebooks and frequent table lookups consume memory bandwidth, and implicit-codebook alternatives depend on costly transforms such as Hadamard rotations. The authors propose SCVQ, a post-training, weight-only method built on standard K-means VQ. It works in four steps:

- Weights are bi-normalized across rows and columns.
- Clustering uses K-means weighted by a combined local (activation-based) and global (gradient-based) salience score.
- Symmetry constraints shrink the codebook.
- Outliers, highly salient sub-vectors and the largest quantization residuals are stored together in a single sparse correction matrix.

The authors observe that such important weights tend to sit contiguously within rows, which matches VQ's block grouping. They exploit this with a structured sparse format (VSR) and custom CUDA kernels. Normalization scales and sparse terms are then fine-tuned. On the LLaMA-2 family at about 2 bits per weight, SCVQ outperforms AQLM, QuIP# and VPTQ on perplexity and most zero-shot tasks, and it also runs faster. This shows that algorithm–hardware co-design can make aggressive LLM compression practical.

## Key points
- LLaMA-2-7B at 2-bit (2.04 BPW):
  - WikiText-2 perplexity is 5.87, versus roughly 6.13–6.19 for AQLM, QuIP# and VPTQ (FP16: 5.12).
  - C4 perplexity is 7.71, versus about 8.07–8.16 for the baselines.
- On an NVIDIA A100, SCVQ gives a 1.4× end-to-end speedup over the baseline. LLaMA-2-7B at 2-bit reaches 235.4 tokens/s, 2.3× faster than FP16.
- Codebook compression cuts the codebook to a quarter of its size, raising perplexity by only 0.13.
- In ablations, fine-tuning, salience-weighted K-means and the outlier sparse matrix contribute most to quality.
- Limitations:
  - Experiments use only sub-vector dimension d = 8 and K-means clustering.
  - Sub-vector selection relies on heuristic ranking.
  - The hyperparameter α was tuned by linear search.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Beijing University of Posts and Telecommunications](https://md.research.elamur.ai/free/en/universities/beijing-university-of-posts-and-telecommunications/) — University, Beijing, China · 78 works · 33,365 citations

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## Cite

Cite as: Zixuan Zhou, Yujun Diao, Zicheng Kong, Dehua Ma, Zhenbo Xu, Pei Pei Li and Zhaofeng He. 2026. SCVQ: Sparse-Compensated Vector Quantization for Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhou2026scvq,
  title = {{SCVQ: Sparse-Compensated Vector Quantization for Large Language Models}},
  author = {Zixuan Zhou, Yujun Diao, Zicheng Kong, Dehua Ma, Zhenbo Xu, Pei Pei Li and Zhaofeng He},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.403},
  url = {https://doi.org/10.18653/v1/2026.acl-long.403}
}
```

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*Updated 2 November 2026*