4 papers
Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
Tian Wu, Liming Wang, Zijian Wen +5
The emergence of Mixture-of-Experts (MoE) has transformed the scaling of large language models by enabling vast model capacity through sparse activation. Yet, converting these perf…
RServe: Overlapping Encoding and Prefill for Efficient LMM Inference
Tianyu Guo, Tianming Xu, Xianjie Chen +3
Large multimodal models (LMMs) typically employ an encoding module to transform multimodal data inputs into embeddings, which are then fed to language models for further processing…
EFIM: Efficient Serving of LLMs for Infilling Tasks with Improved KV Cache Reuse
Tianyu Guo, Hande Dong, Yichong Leng +4
Large language models (LLMs) are often used for infilling tasks, which involve predicting or generating missing information in a given text. These tasks typically require multiple…
gLLM: Global Balanced Pipeline Parallelism System for Distributed LLM Serving with Token Throttling
Tianyu Guo, Xianwei Zhang, Jiangsu Du +3
Pipeline parallelism has emerged as a predominant approach for deploying large language models (LLMs) across distributed nodes, owing to its lower communication overhead compared t…