arXiv Machine Learning

Loopy: Low-Bit Quantization Framework for Looped Language Models

arXiv AI

Oct 15

LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers

arXiv:2610.16368v1 Announce Type: cross Abstract: Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over...

By Zhixiong Zhao, Zukang Xu, Xing Hu, Lifeng Liu, Guangyu Sun, Dawei Yang

llms efficiency

arXiv AI

Oct 15

LoopQ: Quantization for Looped Language Models

arXiv:2605.16343v2 Announce Type: replace-cross Abstract: Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing t...

By Rui Fang, Hsi-Wen Chen, Chia-Ming Cheng, Ming-Syan Chen

llms efficiency safety

arXiv AI

Jun 9

Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression

arXiv:2606. 07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications.

By Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi, Phuong Hoai Ha

llms efficiency benchmarks

arXiv Machine Learning

Oct 2

QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs

arXiv:2609.39223v2 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive p...

By Weili Xu, Jisen Li, Yuqing Jian, Chenxi Li, Zhizhou Sha, Yifan Yu, Qingyang Wu, Chenfeng Xu, Zhongzhu Zhou, Tianyi Zhang, Ben Athiwaratkun

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jun 16

RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs

arXiv:2602. 05367v3 Announce Type: replace Abstract: Efficient deployment of large language models (LLMs) requires extreme quantization, forcing a critical trade-off between low-bit efficiency and performance.

By Youngcheon You, Banseok Lee, Minseop Choi, Seonyoung Kim, Hyochan Chong, Changdong Kim, Youngmin Kim, Dongkyu Kim

llms efficiency benchmarks

arXiv AI

Jul 7

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs

arXiv:2601. 07475v2 Announce Type: replace-cross Abstract: The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference.

By Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Peng Zhang, Xindian Ma

llms efficiency benchmarks

arXiv Machine Learning

Jun 8

AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization

arXiv:2605. 08692v2 Announce Type: replace Abstract: Post-training weight-only quantization to 4 bits is widely used to reduce the memory and compute costs of large language model inference.

By Beshr IslamBouli, David Jin

llms efficiency

arXiv Machine Learning

Sep 1

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

arXiv:2605.11222v2 Announce Type: replace Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (...

By Ryan Lucas, Mehdi Makni, Xiang Meng, Adam Deng, Rahul Mazumder

llms efficiency

arXiv Machine Learning

Oct 2

MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models

arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantiz...

By Maximilian Kleinegger, Elvir Crn\v{c}evi\'c, Dan Alistarh

llms efficiency benchmarks

arXiv AI

Jul 28

MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models

arXiv:2607. 23047v1 Announce Type: cross Abstract: Mixed-precision quantization improves the accuracy of post-training quantization by allocating higher bitwidths to sensitive layers, but existing methods solve the allocation for a single fixed memory budget.

By Ashitabh Misra, Madhav Agrawal, Arham Jain, Tarek Abdelzaher

llms efficiency

arXiv Machine Learning

Jul 3

QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs

arXiv:2602. 10431v4 Announce Type: replace Abstract: Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment.

By Kanghyun Noh, Jinheon Choi, Yulhwa Kim

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Oct 3

ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs

ConQuR introduces a lightweight post‑training rotation calibration for large language model activation quantization. By learning orthogonal rotations that align normalized activations with the corners of an inscribed hypercube, the method distributes activation energy evenly and can be updated online without storing activations. Experiments on Llama‑2 and Llama‑3 models (3B–70B) show competitive or improved perplexity and reasoning performance while avoiding costly training or large offline storage.

By Chayne Thrash, Ali Abbasi, Soheil Kolouri

llms efficiency benchmarks

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