Author

Yuquan Ding

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Open access Aug 2026

GPU and CPU Memory Co-Optimization in Heterogeneous Pipeline Parallelism for Efficient Large Language Model Fine-Tuning on Commodity Servers

Tiny-Pipe comprises a holistic layer packing method that simultaneously reduces GPU memory footprint and improves training performance, an active CPU memory management that alleviates CPU memory pressure by eliminating redundant parameters, and a layer-wise runtime swapping strategy that further enhances overall perfor...

Yu-Quan Ding, Jie Shao · 0 citations

Open access

GPU and CPU Memory Co-Optimization in Heterogeneous Pipeline Parallelism for Efficient Large Language Model Fine-Tuning on Commodity Servers

To train or fine-tune large language models with insufficient GPU memory, heterogeneous parallel training methods utilize aggregated GPU memory and offload tensors to CPU DRAM or SSD. However, these methods lack effective simultaneous management of GPU and CPU memory, creating a critical bottleneck on resource-constrai...

Yuquan Ding, Jie Shao · ACM Transactions on Architec... · 0 citations