RUQuant: Towards Refining Uniform Quantization for Large Language Models
The increasing size and complexity of large language models (LLMs) have raised significant challenges in deployment efficiency, particularly under resource constraints. Post-training quantization (PTQ) has emerged as a practical solution by compressing models without requiring retraining. While existing methods focus on uniform quantization schemes for both weights and activations, they often suffer from substantial accuracy degradation due to the non-uniform nature of activation distributions. In this work, we revisit the activation quantization problem from a theoretical perspective grounded in the Lloyd-Max optimality conditions. We identify the core issue as the non-uniform distributi...
| Authors | Han Liu, Haotian Gao, Changya Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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