Thousand-GPU Large-Scale Training and Optimization Recipe for AI-Native Cloud Embodied Intelligence Infrastructure

2026-03-11

AI Summary

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.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Embodied intelligence is a key step towards Artificial General Intelligence (AGI), yet its development faces multiple challenges including data, frameworks, infrastructure, and evaluation systems. To address these issues, we have, for the first time in the industry, launched a cloud-based, thousand-GPU distributed training platform for embodied intelligence, built upon the widely adopted LeRobot framework, and have systematically overcome bottlenecks across the entire pipeline. At the data layer, we have restructured the data pipeline to optimize the flow of embodied training data. In terms of training, for the GR00T-N1.5 model, utilizing thousand-GPU clusters and data at the scale of hundreds of millions, the single-round training time has been reduced from 15 hours to just 22 minutes, achieving a 40-fold speedup. At the model layer, by combining variable-length FlashAttention and Data Packing, we have moved from sample redundancy to sequence integration, resulting in a 188% speed increase; π-0.5 attention optimization has accelerated training by 165%; and FP8 quantization has delivered a 140% speedup. On the infrastructure side, relying on high-performance storage, a 3.2T RDMA network, and a Ray-driven elastic AI data lake, we have achieved deep synergy among data, storage, communication, and computation. We have also built an end-to-end evaluation system, creating a closed loop from training to simulation to assessment. This framework has already been fully validated on thousand-GPU clusters, laying a crucial technical foundation for the development and application of next-generation autonomous intelligent robots, and is expected to accelerate the arrival of the era of human-machine integration.

Problem

• embodied intelligence
• large-scale training
• AI infrastructure
• distributed training
• cloud computing

Innovation

• Thousand-GPU Training
• Embodied Intelligence
• FlashAttention
• FP8 Quantization
• Ray-driven Data Lake