SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs
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 pol...
| Authors | Ruijia Yang, Shiyuan Lin, Yulong Ao et al. |
|---|---|
| Primary category | cs.DC |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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