SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs

Ruijia Yang; Shiyuan Lin; Yulong Ao; Zhiyu Li; Yingli Zhao; Xianduo Li; Yonghua Lin; Zeyi Wen

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arXiv · 2609.34162

SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs

Ruijia Yang·Shiyuan Lin·Yulong Ao·Zhiyu Li·Yingli Zhao·Xianduo Li·Yonghua Lin·Zeyi Wen

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Abstract

Host-resident layer streaming enables full-parameter LLM fine-tuning beyond GPU memory, but data-parallel ranks compete for shared host resources. Replicated transfers amplify traffic, while strong scaling can expose host work as computation windows shrink. We present SlideDP, a synchronous data-parallel runtime for shared-host multi-GPU systems. It maintains one authoritative host state, decouples communication routes from state layout, and pipelines parameter delivery, gradient aggregation, and CPU updates across ranks and chunks. An analytical step-time model characterizes resource bottlenecks and pipeline exposure; runtime measurements guide communication, chunking, and activation policies under a GPU memory budget. In matched-batch sweeps, SlideDP achieves geometric-mean throughput ratios of 1.46-2.64$\times$ over SlideFormer, MegaTrain, and ZeRO-Offload. On four H100s, SlideDP approaches GPU-resident FSDP2 throughput for Qwen3-14B at a smaller batch size. With a larger batch, it processes over 1M tokens per step and exceeds FSDP2's measured peak throughput by 11.2%. Separately, it supports 256K-token sequences for the same model and fine-tunes Qwen2.5-72B on four RTX 4090 GPUs. Project page: https://github.com/RegiaYoung/SlideDP.

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Ruijia Yang, Shiyuan Lin, Yulong Ao, Zhiyu Li, Yingli Zhao, Xianduo Li, Yonghua Lin, Zeyi Wen. 2026-09-28. SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs. https://arxiv.org/abs/2609.34162

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