Post-Training Quantization

Also known as PTQ

Post-training quantization (PTQ) converts a trained model to lower-precision numbers without retraining it, reducing memory use and enabling inference on constrained hardware. Methods range from rounding and calibration-based scaling to Hessian-aware reconstruction such as GPTQ, rotations and outlier handling, codebooks, and mixed-precision allocation across layers, experts, or caches. Calibration usually uses a small set of representative inputs, while evaluation spans language-model perplexity and reasoning, vision accuracy, multimodal tasks, and real-device latency and memory. The central tension is that moderate precision often retains task quality, while very low bit widths can amplify errors and alter behavior; calibration and hardware-aware kernels help, but accuracy, behavioral fidelity, and deployment speed do not always move together.

Latest papers

360 papers70 papers in the last four weeks, up 46% on the four weeks before. 0.4% of all new papers.

SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization
May 12, 2026Chengzhu Bao, Xianglong Yan, Zhiteng Li +3LLM Quantization 4-Bit Quantization
Shanghai Jiao Tong University · Tencent Hunyuan
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
May 11, 2026Ryan Lucas, Mehdi Makni, Xiang Meng +2LLM Quantization Alternating Direction Method of Multipliers
MIT Operations Research Center · MIT Sloan School of Management · MIT Center for Statistics
ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs
May 11, 2026Chayne Thrash, Ali Abbasi, Soheil KolouriRotation-Based Quantization LLM Inference Acceleration
Department of Computer Science Vanderbilt University
Theory-optimal Quantization Based on Flatness
May 11, 2026Xiusheng Huang, Zhe Li, Xuanwu Yin +5LLM Quantization Activation Quantization
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence +2
PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models
May 10, 2026Yongsen Cheng, Kai Liu, Kaiwen Tao +5Diffusion Model Quantization Post-Training Quantization
Shanghai Jiao Tong University · Huawei Noah’s Ark Lab
TileQ: Efficient Low-Rank Quantization of Mixture-of-Experts with 2D Tiling
May 10, 2026Hongyaoxing Gu, Xinzhe Chen, Lijuan Hu +1Efficient Inference Mixture-of-Experts Inference
Institute of Software Chinese Academy of Sciences · University of Chinese Academy of Sciences
Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs
May 9, 2026Yuzhuang Xu, Xu Han, Yuxuan Li +2LLM Quantization Post-Training Quantization
1Harbin Institute of Technology, Harbin, China · 2Tsinghua University, Beijing, China
AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization
May 9, 2026Beshr IslamBouli, David JinLLM Quantization 4-Bit Quantization
Massachusetts Institute of Technology, Cambridge, MA, USA.
LoopQ: Quantization for Looped Language Models
May 8, 2026Rui Fang, Hsi-Wen Chen, Chia-Ming Cheng +1Recurrent Transformers Language Modeling
National Taiwan University
Saliency-Aware Regularized Quantization Calibration for Large Language Models
May 7, 2026Yanlong Zhao, Xiaoyuan Cheng, Huihang Liu +6LLM Quantization Language Model Calibration
University of Science and Technology of China · University College London · 3Shanghai University of Finance and Economics +5
OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization
May 6, 2026Zhikai Li, Zhen Dong, Xuewen Liu +2LLM Quantization Weight-Only Quantization
Institute of Automation, Chinese Academy of Sciences · University of California, Santa Barbara.
HeadQ: Model-Visible Distortion and Score-Space Correction for KV-Cache Quantization
May 5, 2026Jorge L. Ruiz WilliamsSelf-Attention KV Caching
Independent Researcher
QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization
May 5, 2026Xiantao JiangMixed-Precision Quantization LLM Quantization
College of Information Engineering, Shanghai Maritime University Shanghai 201306, China
Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators
May 5, 2026Amitash Nanda, Javier Hernandez Nicolau, Madhusudan Gujral +3Mixed-Precision Quantization AI Accelerator Inference
University of California San Diego, San Diego, CA, USA · San Diego Supercomputer Center, San Diego, CA, USA
Statistically-Lossless Quantization of Large Language Models
May 4, 2026Michael Helcig, Eldar Kurtic, Dan AlistarhLLM Quantization LLM Inference Acceleration
ETH Z̈urich · ISTA
Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels
May 2, 2026Plawan Kumar Rath, Rahul MaliakkalLLM Quantization Social Bias in Language Models
Meta
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
May 1, 2026Zhixiong Zhao, Zukang Xu, Dawei YangLLM Quantization LLM Inference Acceleration
1Houmo AI
Technical Report: Activation Residual Hessian Quantization (ARHQ) for Low-Bit LLM Quantization
Apr 30, 2026YiFeng Wang, Zhun Sun, Keisuke SakaguchiLLM Quantization Activation Quantization
Graduate School of Information Sciences Tohoku University
MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
Apr 29, 2026Shuzhao Xie, Junchen Ge, Weixiang Zhang +103DGS Compression 3D Gaussian Splatting
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China · Harbin Institute of Technology, Shenzhen, China +6
CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs
Apr 29, 2026Zhe Ding, Su Pan, Duowei PanMixed-Precision Quantization LLM Quantization
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China · Amazon AGI, Seattle, WA, USA
Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels
Apr 27, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaLLM Quantization Activation Outliers
Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University
FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression
Apr 22, 2026Ye Qiao, Yian Wang, Zhiheng Chen +2LLM Compression GPU Acceleration
University of California, Irvine Irvine, California, USA
Variance Is Not Importance: Structural Analysis of Transformer Compressibility Across Model Scales
Apr 22, 2026Samuel SalfatiLLM Compression Post-Training Quantization
fraQtl AI Research
On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks
Apr 22, 2026Aarav Gupta, Gururaj Deshpande, Chandreyi ChakrabortyMixed-Precision Quantization Diffusion Model Quantization
College of Computing · Georgia Institute of Technology
From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
Apr 21, 2026Chenxi Zhou, Pengfei Cao, Jiang Li +4LLM Quantization Post-Training Quantization
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences · College of Computer Science, Inner Mongolia University +1
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
Apr 21, 2026Siqing Song, Chuang Wang, Yong Lang +2LLM Quantization Activation Quantization
MAIS, Institute of Automation, Chinese Academy of Sciences, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, China · Central Media Technology Institute, Huawei
GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling
Apr 20, 2026Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan +4LLM Quantization Post-Training Quantization
ISTA · ETH Zürich · ISTA & Red Hat AI +1
Depth Registers Unlock W4A4 on SwiGLU: A Reader/Generator Decomposition
Apr 20, 2026Ziyang LiuLLM Quantization 4-Bit Quantization
Independent Researcher
LoRaQ: Optimized Low Rank Approximation for 4-bit Quantization
Apr 20, 2026Yann Bouquet, Alireza Khodamoradi, Sophie Yáng Shen +2Mixed-Precision Quantization Diffusion Transformer
Research and Advancement Development Team, Advanced Micro Devices, Inc, USA · Computer Vision Laboratory, Ecole Polytechnique F´ed´erale de Lausanne, Lausanne, Switzerland.
Evolutionary fine tuning of quantized convolution-based deep learning models
Apr 19, 2026Marcin PietrońFine-Tuning Convolutional Neural Networks
AGH University Kraków, Poland

May 12, 2026J/K move · Enter open · S save

SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization

Chengzhu Bao, Xianglong Yan, Zhiteng Li, Guangshuo Qin+2

Shanghai Jiao Tong University · Tencent Hunyuan

LLM Quantization 4-Bit Quantization Post-Training Quantization Neural Network Quantization

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NVFP4 has recently emerged as an efficient 4-bit microscaling format for large language models (LLMs), offering superior numerical fidelity with native hardware support. However, existing methods often yield suboptimal performance due to inflexible scale selection and the coupled treatment of quantization and dequantization scales. To address these issues, we propose Scale Optimization for Accurate Reconstruction (SOAR), a novel post-training quantization framework that improves the accuracy of NVFP4 quantization. At its core, SOAR features Closed-form Joint Scale Optimization (CJSO), which jointly optimizes global and block-wise scales via analytical solutions derived from reconstruction error minimization. Furthermore, it incorporates Decoupled Scale Search (DSS). DSS decouples the high-precision quantization scale from its constrained dequantization counterpart, and performs discrete search to mitigate precision loss from scale quantization. Extensive experiments across multiple LLMs show that our method consistently outperforms existing NVFP4 quantization baselines, achieving superior accuracy under the same memory footprint with no additional hardware overhead. The code and models will be available at https://github.com/steven-bao1/SOAR.