The paper introduces the "Compression Trinity," a unified framework that jointly applies sparsity, quantization, and low‑rank approximations to compress large language models. It presents several methods—MKOR, SLoPe, OPTIMA, PATCH, and SLiM—that leverage these three pillars to accelerate training, reduce memory bandwidth, and recover accuracy, achieving significant speedups and accuracy gains over existing techniques. The results demonstrate that combining all three compression strategies is essential for efficient, scalable, high‑performance LLM deployment.
By Mohammad Mozaffari
Prohibitive computational and environmental costs impede the scalable deployment of Large Language Models (LLMs). Traditional compression techniques (sparsity, quantization, low-rank approximations) a...
arXiv:2607. 18280v1 Announce Type: cross Abstract: Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance degradation beyond an essential sparsity boundary.
By Chao Han, Haozhe Hu, Xiaoyu Shen
arXiv:2606. 00494v1 Announce Type: new Abstract: Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment.
By Wneya Yu, Chao Zhang, Li Wang, Samson Lasaulce, Merouane Debbah
arXiv:2602. 02599v4 Announce Type: replace Abstract: Long-context inference in large language models (LLMs) is bottlenecked by the memory and compute of the key-value (KV) cache.
By Jihao Xin, Tian Lyu, David Keyes, Hatem Ltaief, Marco Canini
arXiv:2606. 07098v1 Announce Type: cross Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices $S$ to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression.
By Ernests Lavrinovics, Marco Letizia, Roy Janco, Shai Segal, Johannes Bjerva, Maurizio Pierini
Residual sparsification via output importance (PARSER) is a new compression technique for mixture-of-experts large language models that shifts the compression objective from minimizing isolated matrix errors to preserving the expert output error. By introducing output importance, PARSER measures each residual’s contribution to the final expert output and compresses accordingly. Experiments show that PARSER reduces the accuracy gap to the uncompressed model by 1.41× on Qwen and 1.44× on DeepSeek while achieving the same peak memory reduction.
By Seungwoo Jung, Dohyeok Kwon, Seungmin Cha, Junseok Lee, Yeonho Yoo, Chuck Yoo, Gyeongsik Yang
arXiv:2606. 05861v1 Announce Type: cross Abstract: The rapid development of large language models(LLMs) has led to remarkable advances in natural language processing.
By Rui Wang, Yan Zhao, Li Song, Zhengxue Cheng
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
arXiv:2608.23843v1 Announce Type: new
Abstract: Long-context inference in large language models (LLMs) is increasingly limited by the memory required for the key-value (KV) cache. KV cache compressio...
By Zizhong Wang, Jieying Wang, Zhao Zhang, Jiajia Li
arXiv:2608. 11249v1 Announce Type: cross Abstract: We study the problem of lossless text compression, motivated by the rapid growth in the collection and storage of digital textual data - including plain text, source code, and structured formats such as XML - and by recent advances in neural language model-based compression.
By Angelo Nardone, Paolo Ferragina
arXiv:2607. 24192v1 Announce Type: cross Abstract: We study the problem of lossless compression of source code, motivated by the storage demands of large-scale software archives, such as Software Heritage (https://www.
By Angelo Nardone, Paolo Ferragina