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cs.AI2026
SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability
Xianglong Yan, Zenan Wu, Kewei Zhang +2
Extreme low-bit weight-only quantization is an effective approach to reducing the deployment costs of large language models. Below 3 bits, scalar quantization often suffers severe…
cs.AI2026
JustQuant: You Don't Need Smoothing, SVD, or Rotation for 4-Bit Activation Quantization
Kaicheng Yang, Kaisen Yang, Chunyu Liu +8
Recent generative models have become increasingly powerful, but their inference cost continues to grow. Model quantization offers a promising way to compress these models and accel…
cs.AI2026
FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling
Xianglong Yan, Hong Liu, Chengzhu Bao +4
Large language models (LLMs) achieve remarkable performance but are expensive to deploy due to their enormous size. FP4 quantization, with formats such as MXFP4 and NVFP4, offers a…