mlsanityData to 14 Oct 2026

RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm

arXiv 2504.03717 ↗First posted 29 Mar 2025cs.LG cs.AIcited by 5

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Abstract (arXiv)

Post-training Quantization (PTQ) has become a widely used technique for improving inference efficiency of large language models (LLMs). However, existing PTQ methods generally suffer from crucial limitations such as heavy calibration data requirements and inflexible choice of target number of bits. In this paper, we propose RaanA, a unified PTQ framework that overcomes these challenges by introducing two novel components: 1) RaBitQ-H, a variant of a randomized vector quantization method RaBitQ, designed for fast, accurate, and highly efficient quantization; and 2) AllocateBits, an algorithm that optimally allocates bit-widths across layers based on their quantization sensitivity. RaanA achieves competitive performance with state-of-the-art quantization methods while being extremely fast, requiring minimal calibration data, and enabling flexible bit allocation. Extensive experiments demonstrate RaanA's efficacy in balancing efficiency and accuracy. The code is publicly available at https://github.com/FFTYYY/RaanA .

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 27 of its 37 references that are in the map. First posted on arXiv 29 Mar 2025.

Builds on

27 of 37 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2024The Llama 3 Herd of Modelscited by 14,715
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  6. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  7. 2020Scaling Laws for Neural Language Modelscited by 6,586
  8. 2016Pointer Sentinel Mixture Modelscited by 3,096
  9. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,919
  10. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,298
  11. 2024QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMscited by 634
  12. 2024SpinQuant: LLM quantization with learned rotationscited by 471
15 more
  1. 2023OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Modelscited by 424
  2. 2023QuIP: 2-Bit Quantization of Large Language Models With Guaranteescited by 414
  3. 2024QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebookscited by 378
  4. 2022Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruningcited by 322
  5. 2023OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Modelscited by 114
  6. 2024RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Searchcited by 48
  7. 2024APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Modelscited by 34
  8. 2023Norm Tweaking: High-performance Low-bit Quantization of Large Language Modelscited by 33
  9. 2023Some Fundamental Aspects about Lipschitz Continuity of Neural Networkscited by 26
  10. 2023A Practical Mixed Precision Algorithm for Post-Training Quantizationcited by 24
  11. 2023Similarity search in the blink of an eye with compressed indicescited by 21
  12. 2024Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Searchcited by 21
  13. 2024EasyQuant: An Efficient Data-free Quantization Algorithm for LLMscited by 17
  14. 2024HadaCore: Tensor Core Accelerated Hadamard Transform Kernelcited by 12
  15. 2025Universality of Layer-Level Entropy-Weighted Quantization Beyond Model Architecture and Sizecited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunitiescited by 11
  2. 2026Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Provencited by 6
  3. 2026SNIP: An Adaptive Mixed Precision Framework for Subbyte Large Language Model Trainingcited by 2
  4. 2026Block-Sphere Vector Quantizationcited by 0
  5. 2026TORQUE: Optimizing What (not) to Quantize Before and After Rotationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
  2. 2026Recurrent Residual Quantization: A Progressive Multi-Precision Representation for LLMs
  3. 2026SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
  4. 2025PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
  5. 2025LO-BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference
  6. 2024ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
  7. 2024Post Training Quantization of Large Language Models with Microscaling Formats
  8. 2024VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models