X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms

• URL: http://arxiv.org/abs/2508.13337v1
• Date: Mon, 18 Aug 2025 19:49:28 GMT
• Title: X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms
• Authors: Yueming Yuan, Ahan Gupta, Jianping Li, Sajal Dash, Feiyi Wang, Minjia Zhang,
• Abstract summary: We present X-MoE, a novel MoE training system designed to deliver scalable training performance for next-generation MoE architectures.<n>X-MoE achieves this via several novel techniques, including efficient padding-free MoE training with cross-platform kernels, redundancy-bypassing dispatch, and hybrid parallelism with sequence-sharded MoE blocks.
• Score: 15.572112199097132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emerging expert-specialized Mixture-of-Experts (MoE) architectures, such as DeepSeek-MoE, deliver strong model quality through fine-grained expert segmentation and large top-k routing. However, their scalability is limited by substantial activation memory overhead and costly all-to-all communication. Furthermore, current MoE training systems - primarily optimized for NVIDIA GPUs - perform suboptimally on non-NVIDIA platforms, leaving significant computational potential untapped. In this work, we present X-MoE, a novel MoE training system designed to deliver scalable training performance for next-generation MoE architectures. X-MoE achieves this via several novel techniques, including efficient padding-free MoE training with cross-platform kernels, redundancy-bypassing dispatch, and hybrid parallelism with sequence-sharded MoE blocks. Our evaluation on the Frontier supercomputer, powered by AMD MI250X GPUs, shows that X-MoE scales DeepSeek-style MoEs up to 545 billion parameters across 1024 GPUs - 10x larger than the largest trainable model with existing methods under the same hardware budget, while maintaining high training throughput. The source code of X-MoE is available at https://github.com/Supercomputing-System-AI-Lab/X-MoE.

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