arXiv AI June 4, 2026 By Devleena Das, Rajeev Patwari, Elliott Delaye, Ashish Sirasao

Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

arXiv:2606. 04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typically incurs severe accuracy degradation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 26

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs

arXiv:2606. 26650v1 Announce Type: cross Abstract: In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs.

By Shigeng Wang, Chao Li, Yangyuxuan Kang, Jiawei Fan, Anbang Yao

llms efficiency benchmarks

arXiv AI

Jun 6

Channel-Wise Mixed-Precision Quantization for Large Language Models

arXiv:2410. 13056v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes.

By Zihan Chen, Bike Xie, Jundong Li, Cong Shen

llms efficiency

arXiv AI

Jun 12

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

arXiv:2606. 13054v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment.

By Zhixiong Zhao, Zukang Xu, Zhixuan Chen, Xing Hu, Zhe Jiang, Dawei Yang

llms efficiency

arXiv AI

Jun 2

BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization

arXiv:2606. 00079v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) large language models reduce per-token computation through sparse expert activation, but their deployment remains memory-intensive because all expert weights must be kept resident in memory.

By Jiayu Zhao, Zihan Teng, Minhao Fan, Tianrui Ma, Wentao Ren, Song Chen, Weichen Liu

llms efficiency

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

Hugging Face Trending Papers

Jun 11

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a promising compression technique, offering significant reductions in model size and inference complexity.

llms efficiency

Related stories

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs

Channel-Wise Mixed-Precision Quantization for Large Language Models

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization

Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization