mlsanityData to 14 Oct 2026

TurboBoA: Faster and Exact Attention-aware Quantization without Backpropagation

arXiv 2602.04929 ↗First posted 4 Feb 2026cs.LGcited by 3

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Abstract (arXiv)

The rapid growth of large language models (LLMs) has heightened the importance of post-training quantization (PTQ) for reducing memory and computation costs. Among PTQ methods, GPTQ has gained significant attention for its efficiency, enabling billion-scale LLMs to be quantized within a few GPU hours. However, GPTQ's assumption of layer-wise independence leads to severe accuracy drops in low-bit regimes. Recently, BoA improved upon GPTQ by incorporating inter-layer dependencies within attention modules, but its reliance on sequential quantization across all out-channels makes it substantially less efficient. In this paper, we propose TurboBoA, a new backpropagation-free PTQ algorithm that preserves the accuracy benefits of BoA while significantly accelerating the process. The proposed TurboBoA introduces three key innovations: (i) joint quantization of multiple out-channels with a closed-form error compensation rule, which reduces sequential bottlenecks and yields more than a three-fold speedup; (ii) a correction mechanism for errors propagated from preceding quantized layers; and (iii) adaptive grid computation with coordinate descent refinement to maintain alignment during iterative updates. Extensive experiments demonstrate that TurboBoA delivers substantial acceleration over BoA while consistently improving accuracy. When combined with outlier suppression techniques, it achieves state-of-the-art results in both weight-only and weight-activation quantization. The code will be available at https://github.com/SamsungLabs/TurboBoA.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 25 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

20 of 25 references.

References · 20

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  4. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  5. 2016Pointer Sentinel Mixture Modelscited by 3,096
  6. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,883
  7. 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,183
  8. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,164
  9. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,386
  10. 2016The LAMBADA dataset: Word prediction requiring a broad discourse contextcited by 992
  11. 2024QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMscited by 634
  12. 2020Up or Down? Adaptive Rounding for Post-Training Quantizationcited by 621
8 more
  1. 2021BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstructioncited by 479
  2. 2024SpinQuant: LLM quantization with learned rotationscited by 471
  3. 2023OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Modelscited by 424
  4. 2024DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMscited by 167
  5. 2025OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fittingcited by 97
  6. 2025GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibrationcited by 60
  7. 2024BoA: Attention-aware Post-training Quantization without Backpropagationcited by 12
  8. 2024Towards Next-Level Post-Training Quantization of Hyper-Scale Transformerscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026LoopQ: Quantization for Looped Language Modelscited by 6
  2. 2026Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Modelscited by 0
  3. 2026G2PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
  2. 2024ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
  3. 2024ApiQ: Finetuning of 2-Bit Quantized Large Language Model
  4. 2025SiLQ: Simple Large Language Model Quantization-Aware Training
  5. 2026SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
  6. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
  7. 2024LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
  8. 2025PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models