2 citations · 6 across the 7 of their papers we have counts for
7 papers
Learning Agent Execution for KV-Cache Management in Agentic Serving
Rui Zhang, Chaeeun Kim, Shaoting Feng +6
Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents. Across these…
VeriCache: Turning Lossy KV Cache into Lossless LLM Inference
Jiayi Yao, Samuel Shen, Kuntai Du +7
The large size of the KV cache has become a major bottleneck for serving LLMs with increasing context lengths. In response, many KV cache compression methods, such as token droppin…
EVICPRESS: Joint KV-Cache Compression and Eviction for Efficient LLM Serving
Shaoting Feng, Yuhan Liu, Hanchen Li +11
Reusing KV cache is essential for high efficiency of Large Language Model (LLM) inference systems. With more LLM users, the KV cache footprint can easily exceed GPU memory capacity…
LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference
Yuhan Liu, Yihua Cheng, Jiayi Yao +8
KV cache has traditionally been stored in GPU memory to accelerate the decoding phase of large language model (LLM) inference. However, it is increasingly necessary to move KV cach…
AdaptCache: KV Cache Native Storage Hierarchy for Low-Delay and High-Quality Language Model Serving
Shaoting Feng, Hanchen Li, Kuntai Du +8
Large language model (LLM) applications often reuse previously processed context, such as chat history and documents, which introduces significant redundant computation. Existing L…
METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation
Siddhant Ray, Rui Pan, Zhuohan Gu +5
RAG (Retrieval Augmented Generation) allows LLMs (large language models) to generate better responses with external knowledge, but using more external knowledge often improves gene…
DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving
Yuhan Liu, Yuyang Huang, Jiayi Yao +9
Compound AI systems, such as agentic systems, are an emerging trend in large-scale enterprise settings, with multiple LLMs specialized for different users, tasks, and/or roles work…