Efficient Training on Multiple Consumer GPUs with RoundPipe
From Daily Paper Cast by Jingwen Liang, Gengyu Wang
May 2, 2026 · 23 min · Episode 1825
About this episode
This episode discusses the RoundPipe method for efficient training of large language models on consumer GPUs.
🤗 Upvotes: 24 | cs.DC, cs.AI, cs.LG Authors: Yibin Luo, Shiwei Gao, Huichuan Zheng, Youyou Lu, Jiwu Shu Title: Efficient Training on Multiple Consumer GPUs with RoundPipe Arxiv: http://arxiv.org/abs/2604.27085v1 Abstract: Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism combined with CPU offloading mitigates these hardware bottlenecks by reducing communication overhead. However, existing PP schedules suffer from an inherent limitation termed the weight binding issue. Binding uneven model stages (e.g., the LM head is large) to GPUs limits the pipeline's throughput to that of the GPU with the heaviest load, leading to severe pipeline bubbles. In this paper, we propose RoundPipe, a novel pipeline schedule that breaks the weight binding constraint on consumer GPU servers. RoundPipe treats GPUs as a pool of stateless execution workers and dynamically dispatches computation stages across devices in a round-robin manner, achieving a near-zero-bubble pipeline. To ensure training correctness and system efficiency, RoundPipe integrates a priority-aware transfer…
People in this episode
Hosts: Jingwen Liang, Gengyu Wang
Topics covered
- GPU training
- pipeline parallelism
- large language models
- computational efficiency
- machine learning
Keywords
- RoundPipe
- GPU
- pipeline parallelism
- large language models
- training efficiency
- speedup
- synchronization
Mentioned in this episode
Organizations: RoundPipe, RTX 4090
Books & works: Efficient Training on Multiple Consumer GPUs with RoundPipe
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