5 papers
xGR: Efficient Generative Recommendation Serving at Scale
Qingxiao Sun, Tongxuan Liu, Shen Zhang +13
Recommendation system delivers substantial economic benefits by providing personalized predictions. Generative recommendation (GR) integrates LLMs to enhance the understanding of l…
xLLM Technical Report
Tongxuan Liu, Tao Peng, Peijun Yang +50
We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimi…
OOCO: Latency-disaggregated Architecture for Online-Offline Co-locate LLM Serving
Siyu Wu, Zihan Tang, Yuting Zeng +5
Large Language Models (LLMs) are increasingly deployed in both latency-sensitive online services and cost-sensitive offline workloads. Co-locating these workloads on shared serving…
HydraInfer: Hybrid Disaggregated Scheduling for Multimodal Large Language Model Serving
Xianzhe Dong, Tongxuan Liu, Yuting Zeng +7
Multimodal Large Language Models (MLLMs) have been rapidly advancing, enabling cross-modal understanding and generation, and propelling artificial intelligence towards artificial g…
Arrow: Adaptive Scheduling Mechanisms for Disaggregated LLM Inference Architecture
Yu Wu, Tongxuan Liu, Yuting Zeng +6
Existing large language model (LLM) serving systems typically employ Prefill-Decode disaggregated architecture to prevent computational interference between the prefill and decode…