5 papers
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…
SDFP: Speculative Decoding with FIT-Pruned Models for Training-Free and Plug-and-Play LLM Acceleration
Hanyu Wei, Zunhai Su, Peng Lu +4
Large language models (LLMs) underpin interactive multimedia applications such as captioning, retrieval, recommendation, and creative content generation, yet their autoregressive d…
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…
RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations
Zunhai Su, Zhe Chen, Wang Shen +4
Key-Value (KV) cache facilitates efficient large language models (LLMs) inference by avoiding recomputation of past KVs. As the batch size and context length increase, the oversize…
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…