LENOVO GROUP(HK:00992) Huan Qiu Wang·2025-11-28 09:37Core Insights - Lenovo's RNL technology addresses long-standing challenges in RoCE network load balancing for AI training and inference scenarios, showcasing innovation in multi-dimensional perception, path load balancing optimization, and incremental flow migration [1][2]. Group 1: RNL Technology Overview - The RNL technology integrates multi-dimensional perception, path load balancing optimization, and incremental flow migration into a closed-loop system, providing both algorithmic innovation and practical value [1]. - The multi-dimensional perception mechanism allows real-time awareness of network topology, AI task network demands, and RoCE link load status, forming a data foundation for dynamic scheduling [1]. - Path load balancing optimization employs virtual-physical network mapping and path scoring algorithms to intelligently select optimal data transmission paths, maximizing bandwidth utilization [1]. Group 2: Performance and Cost Efficiency - RNL technology demonstrates high reliability and dual advantages in enhancing AI business efficiency and reducing total cost of ownership (TCO) [2]. - Performance improvements include a 50% enhancement in communication primitive performance, 85% bandwidth utilization, and a 90% reduction in load balancing discreteness [2]. - In AI inference scenarios, transactions per second (TPS) increased by 26%, time to first byte (TTFT) decreased by 30%, and time per output token (TPOT) reduced by 22%, while overall deployment costs were lowered by 60% [2]. Group 3: Strategic Implications - RNL technology is incorporated into Lenovo's heterogeneous computing platform, reinforcing its technological barriers in the AI heterogeneous computing market and enhancing its industry influence and core competitiveness [4].