3 papers
cs.CL2024
SnapKV: LLM Knows What You are Looking for Before Generation
Yuhong Li, Yingbing Huang, Bowen Yang +6
Large Language Models (LLMs) have made remarkable progress in processing extensive contexts, with the Key-Value (KV) cache playing a vital role in enhancing their performance. Howe…
cs.LG2024
New Solutions on LLM Acceleration, Optimization, and Application
Yingbing Huang, Lily Jiaxin Wan, Hanchen Ye +5
Large Language Models (LLMs) have become extremely potent instruments with exceptional capacities for comprehending and producing human-like text in a wide range of applications. H…
cs.LG2024
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
Tianle Cai, Yuhong Li, Zhengyang Geng +4
Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation, with each step reliant on the previous one's output. This creates a bottleneck as…