LCXX(SZ:000977) 量子位·2025-08-11 07:48Core Viewpoint - The article highlights the advancements in domestic open-source AI models, emphasizing their performance improvements and the challenges posed by the increasing demand for computational resources and low-latency communication in the era of Agentic AI [1][2][13]. Group 1: Model Performance and Infrastructure - Domestic open-source models like DeepSeek R1 and Kimi K2 are achieving significant milestones in inference capabilities and handling long texts, with parameter counts exceeding trillions [1]. - The emergence of Agentic AI necessitates multi-model collaboration and complex reasoning chains, leading to explosive growth in computational and communication demands [2][15]. - Inspur's "Yuan Nao SD200" super-node AI server is designed to support trillion-parameter models and facilitate real-time collaboration among multiple agents [3][5]. Group 2: Technical Specifications of Yuan Nao SD200 - Yuan Nao SD200 integrates 64 GPUs into a unified memory and addressing super-node, redefining the boundaries of "machine domain" beyond multiple hosts [7]. - The architecture employs a 3D Mesh design and proprietary Open Fabric Switch technology, allowing for high-speed interconnectivity among GPUs across different hosts [8][19]. - The system achieves ultra-low latency communication, with end-to-end delays outperforming mainstream solutions, crucial for inference scenarios involving small data packets [8][12]. Group 3: System Optimization and Compatibility - Yuan Nao SD200 features Smart Fabric Manager for global optimal routing based on load characteristics, minimizing communication costs [9]. - The system supports major computing frameworks like PyTorch, enabling quick migration of existing models without extensive code rewriting [11][32]. - Performance tests show that the system achieves approximately 3.7 times super-linear scaling for DeepSeek R1 and 1.7 times for Kimi K2 during full-parameter inference [11]. Group 4: Open Architecture and Industry Strategy - Yuan Nao SD200 is built on an open architecture, promoting collaboration among various hardware vendors and providing users with diverse computing options [25][30]. - The OCM and OAM standards facilitate compatibility and low-latency connections among different AI accelerators, enhancing the system's performance for large model training and inference [26][29]. - The strategic choice of an open architecture aims to lower migration costs and enable more enterprises to access advanced AI technologies, promoting "intelligent equity" [31][33].