Core Viewpoint - The report from Dongfang Securities indicates that Deepin Technology achieved a significant improvement in net profit for the first three quarters, with a net profit of -0.81 billion (+86.10%), and a remarkable net profit of 1.47 billion in Q3 (+1097.40%), attributed to effective cost control [1] Group 1: Financial Performance - Deepin Technology's net profit for the first three quarters was -0.81 billion, reflecting an increase of 86.10% [1] - In Q3 alone, the company reported a net profit of 1.47 billion, marking a staggering increase of 1097.40% [1] Group 2: Strategic Focus - The company's overall R&D strategy is shifting towards AI intelligence, with the integration of AI-related technologies and capabilities across various products, including security GPT, AICP, AIPaaS, SAVE3.0, and AIOps [1] - The introduction of the "HCI + AICP next-generation super copy selection second transfer data fusion" solution allows users to quickly deploy and support enterprise-level large models like DeepSeek by adding just one GPU node to their existing cluster [1] Group 3: Market Position and Valuation - The company is expected to benefit significantly from the growing demand for localized deployment of large models due to its strong product innovation capabilities and comprehensive channel advantages [1] - Based on the comparable company's 25-year PS level, the company maintains a target price of 119.00 yuan, corresponding to a 25-year PS of 6.10 times, and retains a "buy" rating [1]

Core Insights - The AI training and inference integrated machine market in China is projected to reach 16.8 billion yuan in 2024, with expectations to exceed 20 billion yuan by 2025 due to the surge in demand for AI large models, accelerated domestic substitution, and policy support [1][3] - The industry is characterized by a focus on local deployment, particularly in sectors such as government, finance, and healthcare, which is driving the growth of the market [3][16] Industry Definition and Classification - The AI training and inference integrated machine is a specialized computing device or system that integrates AI model training and inference deployment functions, aiming for efficient collaboration from data training to model implementation [1] - It can be classified based on deployment location into cloud-based, edge-based, and hybrid machines, and by vertical specialization into general-purpose and industry-specific types [1] Current Industry Development - Following the release of ChatGPT and the GPT series by OpenAI, there has been a global surge in AI large model development, with a focus on commercializing these models [3] - The market for AI training and inference integrated machines is expected to grow significantly, driven by increased penetration in various sectors [3][16] Industry Value Chain - The upstream of the industry involves components such as CPUs, GPUs, storage devices, and foundational software like operating systems and databases [5] - The midstream focuses on the research, production, and system integration of training and inference integrated machines, while the downstream applications span government, finance, healthcare, manufacturing, energy, and education [5] Competitive Landscape - The industry is currently in its early stages, characterized by a "domestic-led, concentrated head, and accelerated vertical segmentation" competitive landscape [10] - Major players include Alibaba, Inspur, Sugon, iFlytek, Huawei, ZTE, and others, with a focus on enhancing hardware products related to computing power [10][11] Key Companies - **Inspur**: A leading cloud computing and big data service provider in China, with a projected revenue of 114.77 billion yuan in 2024, marking a 74.24% increase from 2023 [12] - **Sugon**: A core information infrastructure leader in China, with a revenue of 13.15 billion yuan in 2024, showing a 4.10% increase in net profit [14] - **iFlytek**: Known for its intelligent voice and AI solutions, it has launched new integrated machines supporting rapid deployment and data security [11] Industry Development Trends - The AI training and inference integrated machine is a key infrastructure for AI industrialization, with increasing demand for localized deployment in sectors like finance, healthcare, and government [16] - The industry is undergoing transformations such as technological integration, localized substitution, and green computing, with future developments expected in high-performance domestic solutions and multi-modal integration [16]