Thousand-GPU Large-Scale Training and Optimization Recipe for AI-Native Cloud Embodied Intelligence Infrastructure
This work addresses the fragmentation in data, frameworks, infrastructure, and evaluation that hinders large-scale embodied intelligence training. We present a cloud-native, thousand-GPU training platform built upon LeRobot, unifying the full pipeline from data collection and model training to deployment and evaluation. For the first time in industry, we achieve thousand-GPU-scale embodied intelligence training, introducing novel techniques including sequence-integrated redundancy reduction, π-0.5 attention mechanism, and FP8 quantization. The system integrates variable-length FlashAttention, Data Packing, a Ray-driven elastic AI data lake, and a 3.2TB/s RDMA high-performance storage network. These innovations collectively reduce the single training epoch time of the GR00T-N1.5 model from 15 hours to 22 minutes—a 40× speedup—with individual contributions yielding efficiency gains of 188%, 165%, and 140%, respectively, all rigorously validated on a thousand-GPU cluster.