1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation
Huichao Chai, Zhixin Wu, Xuemiao Li +8
Generative recommendation (GR) has emerged as a promising paradigm that replaces fragmented, scenario-specific architectures with unified Transformer-based models, exhibiting scali…
RelayGR: Scaling Long-Sequence Generative Recommendation via Cross-Stage Relay-Race Inference
Jiarui Wang, Huichao Chai, Yuanhang Zhang +38
Real-time recommender systems execute multi-stage cascades (retrieval, pre-processing, fine-grained ranking) under strict tail-latency SLOs, leaving only tens of milliseconds for r…
P/D-Device: Disaggregated Large Language Model between Cloud and Devices
Yibo Jin, Yixu Xu, Yue Chen +27
Serving disaggregated large language models has been widely adopted in industrial practice for enhanced performance. However, too many tokens generated in decoding phase, i.e., occ…
P/D-Serve: Serving Disaggregated Large Language Model at Scale
Yibo Jin, Tao Wang, Huimin Lin +27
Serving disaggregated large language models (LLMs) over tens of thousands of xPU devices (GPUs or NPUs) with reliable performance faces multiple challenges. 1) Ignoring the diversi…