5 papers
ReasonCache: Accelerating Large Reasoning Model Serving through KV Cache Sharing
Kaiwen Chen, Xin Tan, Minchen Yu +2
Large Reasoning Models (LRMs) are becoming integral to many AI inference systems, enhancing their capabilities with advanced reasoning. However, deploying these models in productio…
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
Zhexiang Zhang, Ye Wang, Yumiao Zhao +10
Serving large Mixture-of-Experts (MoE) models is challenging because of their large memory footprints, heterogeneous resource demands, and highly dynamic inference workloads. Most…
λScale: Enabling Fast Scaling for Serverless Large Language Model Inference
Minchen Yu, Rui Yang, Chaobo Jia +9
Serverless computing has emerged as a compelling solution for cloud-based model inference. However, as modern large language models (LLMs) continue to grow in size, existing server…
Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics
Minchen Yu, Yinghao Ren, Jiamu Zhao +1
Serverless computing has attracted a broad range of applications due to its ease of use and resource elasticity. However, developing serverless applications often poses a dilemma -…
Torpor: GPU-Enabled Serverless Computing for Low-Latency, Resource-Efficient Inference
Minchen Yu, Ao Wang, Dong Chen +8
Serverless computing offers a compelling cloud model for online inference services. However, existing serverless platforms lack efficient support for GPUs, hindering their ability…