4 citations · 5 across the 5 of their papers we have counts for
5 papers
PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response
Yueying Li, Jiayang Chen, Yuanfan Chen +5
AI inference clusters are increasingly constrained by instantaneous power, not just energy: grid operators condition new capacity on demand response, imposing time-varying power ca…
Beyond Prediction: Tail-Aware Scheduling for LLM Inference
Yueying Li, Yuanfan Chen, Jiayang Chen +6
LLM serving exhibits extreme length variability, making size-based scheduling difficult in practice. Recent LLM schedulers approximate SJF/SRPT using predicted decode lengths or ra…
FlashDLM: Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion
Zhanqiu Hu, Jian Meng, Yash Akhauri +4
Diffusion language models offer parallel token generation and inherent bidirectionality, promising more efficient and powerful sequence modeling compared to autoregressive approach…
EcoServe: Designing Carbon-Aware AI Inference Systems
Yueying Li, Zhanqiu Hu, Esha Choukse +3
The rapid increase in LLM ubiquity and scale levies unprecedented demands on computing infrastructure. These demands not only incur large compute and memory resources but also sign…
Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference
Michael Shen, Muhammad Umar, Kiwan Maeng +2
The rapid increase in the number of parameters in large language models (LLMs) has significantly increased the cost involved in fine-tuning and retraining LLMs, a necessity for kee…