D-Quant: Driftable Entropy Coding for KV Cache Quantization
The KV cache has become a major bottleneck in deploying LLMs, as its memory footprint grows linearly with sequence length and batch size, imposing substantial pressure on both memory capacity and bandwidth. Among various KV cache compression techniques, quantization is particularly attractive due to its effectiveness and ease of deployment. However, most existing methods rely on fixed-width quantization, where a $b$ bit representation is inherently limited to $2^b$ quantization levels. As the bit width decreases, the number of available levels shrinks exponentially, leading to severe information loss and rapid performance degradation. We further observe that fixed-width quantization fails...
| Authors | Yi Su, Hong Liu, Guanghua Yu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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