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cs.DC2025
Challenging GPU Dominance: When CPUs Outperform for On-Device LLM Inference
Haolin Zhang, Jeff Huang
The common assumption in on-device AI is that GPUs, with their superior parallel processing, always provide the best performance for large language model (LLM) inference. In this w…
cs.DC2025
DawnPiper: A Memory-scablable Pipeline Parallel Training Framework
Xuan Peng, Xuanhua Shi, Haolin Zhang +2
Pipeline parallelism is a crucial paradigm for large-scale model training. However, imbalances in memory footprint across stages can lead to significant GPU memory wastage, limitin…