5 papers · 1 filter
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…
FaaSTube: Optimizing GPU-oriented Data Transfer for Serverless Computing
Hao Wu, Junxiao Deng, Minchen Yu +5
Serverless computing has gained significant traction for machine learning inference applications, which are often deployed as serverless workflows consisting of multiple CPU and GP…