2 citations · 3 across the 6 of their papers we have counts for
11 papers
TridentServe: A Stage-level Serving System for Diffusion Pipelines
Yifei Xia, Fangcheng Fu, Hao Yuan +6
Diffusion pipelines, renowned for their powerful visual generation capabilities, have seen widespread adoption in generative vision tasks (e.g., text-to-image/video). These pipelin…
SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling
Xiaodong Ji, Hailin Zhang, Fangcheng Fu +1
Many advanced Large Language Model (LLM) applications require long-context processing, but the self-attention module becomes a bottleneck during the prefilling stage of inference d…
Thinking Short and Right Over Thinking Long: Serving LLM Reasoning Efficiently and Accurately
Yuhang Wang, Youhe Jiang, Bin Cui +1
Recent advances in test-time scaling suggest that Large Language Models (LLMs) can gain better capabilities by generating Chain-of-Thought reasoning (analogous to human thinking) t…
Galvatron: An Automatic Distributed System for Efficient Foundation Model Training
Xinyi Liu, Yujie Wang, Shenhan Zhu +4
Galvatron is a distributed system for efficiently training large-scale Foundation Models. It overcomes the complexities of selecting optimal parallelism strategies by automatically…
ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Hao Ge, Junda Feng, Qi Huang +6
Scaling long-context ability is essential for Large Language Models (LLMs). To amortize the memory consumption across multiple devices in long-context training, inter-data partitio…
Training-free and Adaptive Sparse Attention for Efficient Long Video Generation
Yifei Xia, Suhan Ling, Fangcheng Fu +4
Generating high-fidelity long videos with Diffusion Transformers (DiTs) is often hindered by significant latency, primarily due to the computational demands of attention mechanisms…