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English(EN) An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

新系统支持在单 GPU 上微调 123B+ LLM

研究人员开发了 SlideFormer,一个旨在实现大型语言模型(LLM)在单 GPU 上微调的新颖系统。该系统利用一个轻量级的异步引擎,将 GPU 视为一个滑动窗口,将计算与 CPU 更新和 I/O 重叠。它还包含一个高效的异构内存管理方案和优化的 Triton 内核,以减少峰值内存使用量。这种方法允许在单个 RTX 4090 上微调超过 1230 亿参数的模型,支持显著更大的批次大小和模型,同时提高吞吐量并减少内存消耗。 AI

影响 通过在单 GPU 硬件上实现大型模型适配,使 LLM 微调民主化。

排序理由 详细介绍 LLM 微调新系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新系统支持在单 GPU 上微调 123B+ LLM

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详细介绍 LLM 微调新系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ruijia Yang, Zeyi Wen ·

    单 GPU 微调的高效异构联合设计

    arXiv:2603.16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs. To address this challenge and democratize LLM fine-tuning, we pr…