6 citations · 14 across the 13 of their papers we have counts for
5 papers · 1 filter
EPIC: Efficient Position-Independent Caching for Serving Large Language Models
Junhao Hu, Wenrui Huang, Weidong Wang +7
Large Language Models (LLMs) show great capabilities in a wide range of applications, but serving them efficiently becomes increasingly challenging as requests (prompts) become mor…
MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool
Cunchen Hu, Heyang Huang, Junhao Hu +8
Large language model (LLM) serving has transformed from stateless to stateful systems, utilizing techniques like context caching and disaggregated inference. These optimizations ex…
The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving
Pai Zeng, Zhenyu Ning, Jieru Zhao +5
We survey the large language model (LLM) serving area to understand the intricate dynamics between cost-efficiency and accuracy, which is magnified by the growing need for longer c…
CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference
Suyi Li, Hanfeng Lu, Tianyuan Wu +6
Pre-trained large language models (LLMs) often need specialization for domain-specific tasks. Low-Rank Adaptation (LoRA) is a popular approach that adapts a base model to multiple…
Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads
Cunchen Hu, Heyang Huang, Liangliang Xu +9
Transformer-based large language model (LLM) inference serving is now the backbone of many cloud services. LLM inference consists of a prefill phase and a decode phase. However, ex…