摩尔线程S5000千卡集群支持具身大脑模型训练：精度对齐国际主流

Core Viewpoint - The article highlights the successful training of the RoboBrain 2.5 model using the MTT S5000 computing cluster, marking a significant advancement in domestic AI infrastructure for complex multimodal tasks [2][6]. Group 1: Model Training and Capabilities - The RoboBrain 2.5 model, developed by Zhiyuan, is designed for real-world physical scenarios, enhancing capabilities in perception, cognition, reasoning, and decision-making [2]. - The model has improved understanding and reasoning of action timing and three-dimensional spatial structures, significantly increasing the success rate of downstream task execution [2]. - The FlagOS-Robo framework integrates a multi-chip AI software stack, supporting efficient training and inference for embodied intelligence [3][4]. Group 2: Performance Metrics - The RoboBrain 2.5 model trained on the MTT S5000 cluster shows performance metrics comparable to international mainstream GPU models, particularly excelling in tasks such as CrossPoint, Q-Spatial, and VABench-V [4][5]. - The training results indicate a high stability of the MTT S5000 cluster, with a relative error of less than 0.62% compared to international GPU training results, demonstrating accurate training capabilities [5]. Group 3: Scalability and Efficiency - The MTT S5000 cluster exhibits high scalability, achieving over 90% linear scaling efficiency when expanding from 64 to 1024 cards, indicating its maturity in large-scale parallel computing [6]. - This collaboration between Moore Threads and Zhiyuan is expected to accelerate the transition of embodied intelligence from laboratory settings to industrial applications, providing a replicable and scalable domestic computing training paradigm [6].