5 papers · 1 filter
Arachne: Orchestrating Cascades for Efficient Text-to-Video Model Training
Peng Yu, Yuankai Fan, Yang Qiu +4
The rising demand for AI-generated videos is fueled by advances in large-scale Text-to-Video (T2V) models, trained on extensive datasets of video clips spanning diverse resolutions…
Mosaic: Towards Efficient Training of Multimodal Models with Spatial Resource Multiplexing
Yanbo Wang, Yuxuan Wang, Chen Chen +6
With the wide adoption of Multimodal Models (MMs) in real-world scenarios, it is significant to efficiently train emerging MMs that exhibit increasingly complex module architecture…
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…
CALVO: Improve Serving Efficiency for LLM Inferences with Intense Network Demands
Weiye Wang, Chen Chen, Junxue Zhang +7
Distributed prefix caching has become a core technique for efficient LLM serving. However, for long-context requests with high cache hit ratios, retrieving reusable KVCache blocks…
Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
Tianze Wang, Yifei Liu, Chen Chen +8
Modern AI clusters, which host diverse workloads like data pre-processing, training and inference, often store the large-volume data in cloud storage and employ caching frameworks…