1 citations · 2 across the 4 of their papers we have counts for
6 papers
vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models
Peiqi Yin, Jiangyun Zhu, Han Gao +13
Any-to-any multimodal models that jointly handle text, images, video, and audio represent a significant advance in multimodal AI. However, their complex architectures (typically co…
SparseServe: Unlocking Parallelism for Dynamic Sparse Attention in Long-Context LLM Serving
Qihui Zhou, Peiqi Yin, Pengfei Zuo +1
Serving long-context LLMs is costly because attention computation grows linearly with context length. Dynamic sparse attention algorithms (DSAs) mitigate this by attending only to…
Gorgeous: Revisiting the Data Layout for Disk-Resident High-Dimensional Vector Search
Peiqi Yin, Xiao Yan, Qihui Zhou +6
Similarity-based vector search underpins many important applications, but a key challenge is processing massive vector datasets (e.g., in TBs). To reduce costs, some systems utiliz…
PilotANN: Memory-Bounded GPU Acceleration for Vector Search
Yuntao Gui, Peiqi Yin, Xiao Yan +3
Approximate Nearest Neighbor Search (ANNS) has become fundamental to modern deep learning applications, having gained particular prominence through its integration into recent gene…
Progressive Sparse Attention: Algorithm and System Co-design for Efficient Attention in LLM Serving
Qihui Zhou, Peiqi Yin, Pengfei Zuo +1
Processing long contexts has become a critical capability for modern large language models (LLMs). However, serving long-context LLMs comes with significant inference costs due to…
DGI: Easy and Efficient Inference for GNNs
Peiqi Yin, Xiao Yan, Jinjing Zhou +5
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopte…