1 citations · 3 across the 6 of their papers we have counts for
6 papers
ClawMobile: Rethinking Smartphone-Native Agentic Systems
Hongchao Du, Shangyu Wu, Qiao Li +4
Smartphones represent a uniquely challenging environment for agentic systems. Unlike cloud or desktop settings, mobile devices combine constrained execution contexts, fragmented co…
LAPS: A Length-Aware-Prefill LLM Serving System
Jianshu She, Zonghang Li, Hongchao Du +7
LAPS identifies and disaggregates requests with different prompt lengths in LLM serving to reduce TTFT latency. While recent systems have decoupled the prefill and decode stages to…
FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference
Hongchao Du, Shangyu Wu, Arina Kharlamova +2
Large Language Models (LLMs) face challenges for on-device inference due to high memory demands. Traditional methods to reduce memory usage often compromise performance and lack ad…
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models
Weilan Wang, Yu Mao, Dongdong Tang +3
Large language models (LLMs) exhibit excellent performance in various tasks. However, the memory requirements of LLMs present a great challenge when deploying on memory-limited dev…
EvoP: Robust LLM Inference via Evolutionary Pruning
Shangyu Wu, Hongchao Du, Ying Xiong +4
Large Language Models (LLMs) have achieved remarkable success in natural language processing tasks, but their massive size and computational demands hinder their deployment in reso…
On the Compressibility of Quantized Large Language Models
Yu Mao, Weilan Wang, Hongchao Du +2
Deploying Large Language Models (LLMs) on edge or mobile devices offers significant benefits, such as enhanced data privacy and real-time processing capabilities. However, it also…