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].