{"slug": "easybalance-cross-layer-load-balancing-in-distributed-moe-inference", "title": "EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference", "summary": "Researchers propose EasyBalance, a cross-layer load balancing strategy for distributed Mixture-of-Experts (MoE) inference that requires no modifications to expert-device mapping, reducing GPU idling by mostly over 40% across models, tasks, and configurations. The method, detailed in a new arXiv paper (2608.07964v1), greedily schedules cross-layer workloads to mitigate imbalance with essentially no additional overhead. Code is available at https://github.com/yize-wu/EasyInfra.", "body_md": "arXiv:2608.07964v1 Announce Type: new\nAbstract: Load Balancing has emerged as a critical problem in expert-parallel distributed inference of Mixture-of-Experts (MoE) models. As routing distributions are typically skewed across experts, devices hosting lighter-loaded experts must idle to wait for the heaviest during expert computing, leading to inefficiency. Existing load-balancing approaches primarily rely on expert replication or migration within each layer, which introduce additional overhead and limit their flexibility and scalability. To address this problem, we propose EasyBalance, a cross-layer load balancing strategy that requires no modifications to the expert-device mapping, enabling instant adaptability and incurring essentially no additional overhead. Our key insights are that (1) experts of other layers can be viewed as naturally redundant for the current layer, and (2) cross-layer MoE workloads can be jointly executed to mitigate their individual imbalance. Based on these observations, EasyBalance greedily schedules a subset of cross-layer workloads to run at each MoE step and defers the remaining workloads for future balancing opportunities, effectively leveraging cross-layer imbalance mitigation. Extensive experiments across models, tasks, and configurations demonstrate that EasyBalance consistently accelerates distributed MoE inference, reducing GPU idling by mostly over 40%. Code is available at https://github.com/yize-wu/EasyInfra.", "url": "https://wpnews.pro/news/easybalance-cross-layer-load-balancing-in-distributed-moe-inference", "canonical_source": "https://www.machinebrief.com/news/easybalance-cross-layer-load-balancing-in-distributed-moe-in-ybmi", "published_at": "2026-08-11 04:00:00+00:00", "updated_at": "2026-08-11 05:13:37.532743+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["EasyBalance", "arXiv", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/easybalance-cross-layer-load-balancing-in-distributed-moe-inference", "markdown": "https://wpnews.pro/news/easybalance-cross-layer-load-balancing-in-distributed-moe-inference.md", "text": "https://wpnews.pro/news/easybalance-cross-layer-load-balancing-in-distributed-moe-inference.txt", "jsonld": "https://wpnews.pro/news/easybalance-cross-layer-load-balancing-in-distributed-moe-inference.jsonld"}}