39 citations · 49 across the 5 of their papers we have counts for
5 papers
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines
Jiaao He, Jidong Zhai
Cost of serving large language models (LLM) is high, but the expensive and scarce GPUs are poorly efficient when generating tokens sequentially, unless the batch of sequences is en…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
FastMoE: A Fast Mixture-of-Expert Training System
Jiaao He, Jiezhong Qiu, Aohan Zeng +3
Mixture-of-Expert (MoE) presents a strong potential in enlarging the size of language model to trillions of parameters. However, training trillion-scale MoE requires algorithm and…
Heterogeneity-Aware Asynchronous Decentralized Training
Qinyi Luo, Jiaao He, Youwei Zhuo +1
Distributed deep learning training usually adopts All-Reduce as the synchronization mechanism for data parallel algorithms due to its high performance in homogeneous environment. H…