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cs.DC2026
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…
cs.DC2025
Computation-Bandwidth-Memory Trade-offs: A Unified Paradigm for AI Infrastructure
Yuankai Fan, Qizhen Weng, Xuelong Li
Large-scale artificial intelligence models are transforming industries and redefining human machine collaboration. However, continued scaling exposes critical limitations in hardwa…
cs.DC2025
Janus: Disaggregating Attention and Experts for Scalable MoE Inference
Zhexiang Zhang, Ye Wang, Yumiao Zhao +9
Serving large Mixture-of-Experts (MoE) models is challenging because of their large memory footprints, heterogeneous resource demands, and highly dynamic inference workloads. Most…