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cs.CL2025
KVSink: Understanding and Enhancing the Preservation of Attention Sinks in KV Cache Quantization for LLMs
Zunhai Su, Kehong Yuan
Key-Value (KV) cache quantization has become a widely adopted optimization technique for efficient large language models (LLMs) inference by reducing KV cache memory usage and miti…
cs.CL2025
Unveiling Super Experts in Mixture-of-Experts Large Language Models
Zunhai Su, Qingyuan Li, Hao Zhang +6
In this study, we report, for the first time, the discovery and systematic investigation of a distinct subset of experts that play a pivotal role in the MoE LLMs' forward inference…
cs.CL2025
AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for Vision-Language Models
Zunhai Su, Wang Shen, Linge Li +4
Vision-language models (VLMs) show remarkable performance in multimodal tasks. However, excessively long multimodal inputs lead to oversized Key-Value (KV) caches, resulting in sig…