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arXiv · 2602.18007

Joint Training on AMD and NVIDIA GPUs

Jon Hu·Thomas Jia·Jing Zhu·Zhendong Yu
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Abstract

As large language models continue to scale, training demands on compute and system capacity grow rapidly, making single-vendor homogeneous clusters insufficient. This paper presents a technical solution for heterogeneous mixed training in AMD-NVIDIA environments. We first adopt a compatibility-oriented approach based on CPU-Forwarding Communication, with differentiated communication back-end selection across parallel groups and multi-NIC parallel data transfer. To achieve higher performance, we further propose another Device-Direct Communication approach, integrating a CPU-offloading P2P mechanism to enable direct cross-vendor GPU data transfer without host-memory staging. Experiments on LLaMA-8B and Qwen2-7B demonstrate that the proposed Device-Direct Communication approach achieves up to 98% of the throughput of an NVIDIA homogeneous system, while preserving training stability and correctness.

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Jon Hu, Thomas Jia, Jing Zhu, Zhendong Yu. 2026-02-20. Joint Training on AMD and NVIDIA GPUs. https://arxiv.org/abs/2602.18007

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