打破技术枷锁！HPN成AI必争地，大厂布局藏深意

Core Viewpoint - The article discusses the current state of High-Performance Networking (HPN) and emphasizes that the true value of technology lies in its practical application rather than theoretical peak performance in laboratory settings [1][4]. Industry Dynamics - The industry has established critical leverage points: large companies must achieve internal coverage and scalability for AI applications, as impressive technology without large-scale deployment is merely a niche toy [2][5]. - Current trends favor scenario-specific breakthroughs through technical optimization, as seen with Huawei's Unified Bus (UB) interconnect protocol and Alibaba's Alink protocol, focusing on specific computational and connectivity optimizations rather than pursuing an all-encompassing integration [7]. Competitive Landscape - The HPN landscape is characterized by a strategic battle among major players, with two main paths: Scale-up and Scale-out, each reflecting the strategic calculations of large companies [9][10]. - The choice between Scale-up and Scale-out is fundamentally based on a company's resources and business needs, with no absolute superiority of one over the other [11]. Technical Approaches - Scale-up follows a centralized approach, utilizing GPU interconnects for high bandwidth and low latency, significantly enhancing performance for latency-sensitive applications [13]. - Scale-out adopts a distributed architecture, which, while not as strong in single-machine performance, supports large-scale model training and addresses issues of communication overhead and low utilization in traditional networking [15][17]. Market Trends - The current market is witnessing a division where Scale-up is used for inference and Scale-out for training, indicating a complementary relationship rather than a competitive one [17]. - The rise of HPN is driven by large companies' desire to avoid dependency on InfiniBand (IB) networks, which have historically dominated the high-performance interconnect market but come with significant limitations [20][22]. Strategic Shifts - The closed nature of IB technology has prompted companies to seek more customizable and optimized solutions, as traditional fixed architectures cannot adapt to the evolving demands of AI applications [24].