On the Expressive Power of Weight Quantization in Large Language Models
In recent years, weight quantization that encodes the learnable parameters of large language models in an $n$-bit format has garnered significant attention due to its potential for model compression and inference acceleration. Many practical techniques have been developed; however, the theoretical understanding of many aspects, especially the approximation and degradation of expressive power as the number of quantization bits decreases, remains unclear. In this paper, we provide a theoretical investigation into the expressive capability of large language models relative to the number of quantization bits. We argue that 1.58-bit is the limiting precision for weight quantization by establis...
| Authors | Shao-Qun Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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