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cs.LG2026
A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design
Linhui Xiao, Guiping Cao, Mingyue Guo +6
The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computation…
cs.LG2025
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
Jiaqi Zhao, Miao Zhang, Ming Wang +5
Large Language Models (LLMs) suffer severe performance degradation when facing extremely low-bit (sub 2-bit) quantization. Several existing sub 2-bit post-training quantization (PT…
cs.LG2025
Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
Jiaqi Zhao, Ming Wang, Miao Zhang +5
Post-training Quantization (PTQ) technique has been extensively adopted for large language models (LLMs) compression owing to its efficiency and low resource requirement. However,…