23 citations · 39 across the 6 of their papers we have counts for
7 papers
PDD: Unleashing Economical and Flexible Heterogeneous LLM Inference via Cross-Datacenter Prefill-Decode Disaggregation
Yida Wang, Xiuhong Li, Jianping Ma +9
Interconnecting geographically dispersed clusters over wide-area Ethernet provides a scalable and cost-effective alternative to dedicated intra-datacenter heterogeneous clusters fo…
TASP: Topology-aware Sequence Parallelism
Yida Wang, Ke Hong, Xiuhong Li +4
Long-context large language models (LLMs) face constraints due to the quadratic complexity of the self-attention mechanism. The mainstream sequence parallelism (SP) method, Ring At…
semi-PD: Towards Efficient LLM Serving via Phase-Wise Disaggregated Computation and Unified Storage
Ke Hong, Lufang Chen, Zhong Wang +9
Existing large language model (LLM) serving systems fall into two categories: 1) a unified system where prefill phase and decode phase are co-located on the same GPU, sharing the u…
Efficient and Adaptable Overlapping for Computation and Communication via Signaling and Reordering
Ke Hong, Xiuhong Li, Minxu Liu +9
Generative models have achieved remarkable success across various applications, driving the demand for multi-GPU computing. Inter-GPU communication becomes a bottleneck in multi-GP…
MBQ: Modality-Balanced Quantization for Large Vision-Language Models
Shiyao Li, Yingchun Hu, Xuefei Ning +10
Vision-Language Models (VLMs) have enabled a variety of real-world applications. The large parameter size of VLMs brings large memory and computation overhead which poses significa…
A Survey on Efficient Inference for Large Language Models
Zixuan Zhou, Xuefei Ning, Ke Hong +12
Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirem…
FlashDecoding++: Faster Large Language Model Inference on GPUs
Ke Hong, Guohao Dai, Jiaming Xu +6
As the Large Language Model (LLM) becomes increasingly important in various domains. However, the following challenges still remain unsolved in accelerating LLM inference: (1) Sync…