SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective technique for compressing models to fit resource-limited devices while preserving generative quality, encompasses two primary methods: quantization aware training (QAT) and post-training quantization (PTQ). QAT involves additional retraining or fine-tuning, thus inevitably resulting in high training cost and making it unsuitable for LLMs. Consequently, PTQ has become the research hotspot in recent quantization methods. However, existing PTQ methods usually rely on various complex computation procedures and suffer...
| Authors | Han Liu, Haotian Gao, Xiaotong Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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