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cs.DC2026
MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
Chunyu Xue, Yangrui Chen, Jianyu Jiang +14
As the foundational component of versatile AI applications, training an multimodal large language model (MLLM) relies on multimodal datasets with dynamic modality mixture proportio…
cs.DC2026
veScale-FSDP: Flexible and High-Performance FSDP at Scale
Zezhou Wang, Youjie Li, Zhiqi Lin +9
Fully Sharded Data Parallel (FSDP), also known as Zero Redundancy Optimizer (ZeRO), is widely used for large-scale model training, because of its memory efficiency and minimal intr…
cs.DC2025
Mesh-Attention: A New Communication-Efficient Distributed Attention with Improved Data Locality
Sirui Chen, Jingji Chen, Siqi Zhu +3
Distributed attention is essential for scaling large language models (LLMs) to long contexts, yet existing methods either have limited parallelism or incur high communication costs…