1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
LiveServe: Interaction-Aware Serving for Real-Time Omni-Modal LLMs
Xiangyu Zhi, Peiqi Yin, Sheng Guan +3
Realtime omni-modal LMs support speech-centric conversations where users stream inputs, hear generated audio, and interrupt freely. Existing Omni-LM serving systems still rely on t…
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…
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…
SPT: Fine-Tuning Transformer-based Language Models Efficiently with Sparsification
Yuntao Gui, Xiao Yan, Peiqi Yin +2
Transformer-based large language models (e.g., BERT and GPT) achieve great success, and fine-tuning, which tunes a pre-trained model on a task-specific dataset, is the standard pra…