Core Insights - The core challenge of AI applications going global is the timeliness, scalability, and stability of model inference services [2][18]. Group 1: Event Overview - The 36Kr WISE2025 Business King Conference, recognized as an annual technology and business trendsetter, took place in Beijing on November 27-28 [3]. - This year's WISE is not a traditional industry summit but an immersive experience using "tech short dramas" to convey insights [4]. Group 2: AI Application Trends - GMI Cloud's VP of Engineering, Qian Yujing, presented on the efficiency upgrade of AI applications going global, focusing on breaking through computing power and evolving inference architecture [6][11]. - The company, a North American AI Native Cloud service provider and one of NVIDIA's first six Reference Cloud Partners, emphasizes the importance of globalizing computing power and demand for AI applications [7][8]. Group 3: Market Dynamics - The AI application market is experiencing exponential growth, with a significant increase in monthly active users for Chinese AI applications overseas [15]. - Over 90% of knowledge workers in the U.S. are now proficient in using AI tools, indicating a strong adoption of AI [15]. - The demand for AI services in regions like the Middle East and Latin America has reached a high level, suggesting that user education for overseas markets is largely complete [16]. Group 4: Challenges in AI Globalization - Key challenges in AI globalization include the timely delivery of services, scalability, and stability, particularly due to the rapid technological iterations in AI [18][20]. - The need for companies to keep pace with technological advancements poses a significant challenge for enterprises [21]. Group 5: GMI Cloud's Solutions - GMI Cloud is investing $500 million to build a 3 million card AI factory in Asia in collaboration with NVIDIA [14]. - The company has developed three product lines: computing hardware, cluster management, and inference services, catering to various AI enterprise needs [14]. - The Cluster Engine and Inference Engine are designed to address different customer segments, with the former focusing on complex applications and the latter on lightweight, end-user applications [25][29]. Group 6: Inference Engine Features - The Inference Engine supports global deployment and automatic scaling across clusters and regions, addressing the challenges faced by companies when their traffic peaks [30][31]. - It features a three-layer architecture for resource scheduling, with two main scheduling methods: queue-based and load balancing-based [31]. - The core features of the Inference Engine include global deployment, elastic scaling, high availability, and unified workload management [33][35][36]. Group 7: Future Outlook - By 2026, the paradigm of AI globalization is expected to shift from a one-way technology output to a global value resonance, emphasizing a two-way empowering ecosystem [43]. - The transformation will involve a new cycle of value creation, where computing power, technology, demand, and applications interact globally [43].

Core Insights - The WISE 2025 conference in Beijing emphasized the transformation of AI applications and the globalization of technology, highlighting the shift from traditional industry practices to immersive experiences in business [1][3]. Company Overview - GMI Cloud is a North American AI Native Cloud service provider and one of the first six Reference Cloud Partners of NVIDIA [2][6]. - The company focuses on AI infrastructure for overseas markets, offering three main product lines: computing hardware, cluster management, and inference services [7]. AI Application Trends - The current state of AI application development is described as "armed to the teeth," with a significant increase in active users, particularly in North America, where over 90% of knowledge workers are proficient in using AI tools [8][10]. - The demand for AI services in overseas markets has surged, driven by completed user education and a growing need for AI inference capabilities [10]. Challenges in AI Deployment - Key challenges in AI deployment include service timeliness, scalability, and stability, particularly as traditional software expansion methods are inadequate for AI applications [11]. - Rapid technological iteration in AI has led to fluctuating token prices, complicating the operational landscape for companies [12]. GMI Cloud's Strategic Initiatives - GMI Cloud is investing in building AI factories in collaboration with NVIDIA, aiming to enhance cluster throughput and support AI application efficiency [12]. - The company is iterating its cluster and inference engines to cater to different customer needs, with the cluster engine designed for technically capable clients and the inference engine for lightweight applications [12][14]. Inference Engine Features - The GMI Cloud Inference Engine supports global deployment and automatic scaling across clusters and regions, addressing the challenges faced by companies during peak traffic [16]. - It features a three-layer architecture for resource scheduling, ensuring efficient workload management based on sensitivity to latency and cost [16]. Future Outlook - By 2026, the paradigm of AI globalization is expected to evolve from a one-way technology output to a model of global value resonance, emphasizing a dual empowerment ecosystem for resources, technology, and demand [23].