2 papers
cs.DC2025
SiPipe: Bridging the CPU-GPU Utilization Gap for Efficient Pipeline-Parallel LLM Inference
Yongchao He, Bohan Zhao, Zheng Cao
As inference workloads for large language models (LLMs) scale to meet growing user demand, pipeline parallelism (PP) has become a widely adopted strategy for multi-GPU deployment,…
cs.CL2025
HeteroSpec: Leveraging Contextual Heterogeneity for Efficient Speculative Decoding
Siran Liu, Yang Ye, Qianchao Zhu +2
Autoregressive decoding inherently limits the inference throughput of Large Language Model (LLM) due to its sequential dependency. Speculative decoding mitigates this by verifying…