Investment Rating - The report does not explicitly state an investment rating for the AI industry or Huawei's AI initiatives. Core Insights - Huawei is exploring a full-stack AI competitive strategy through the integration of software and hardware, transitioning from merely catching up with state-of-the-art (SOTA) models to customizing model architectures to better leverage its self-developed Ascend hardware [6][20]. - The evolution of the Pangu model series reflects a shift from dense models to sparse architectures, addressing systemic issues in large-scale distributed systems and enhancing efficiency [6][22]. - The introduction of the CloudMatrix infrastructure supports the optimization of AI inference, enabling high throughput and low latency through a unified bus network and various operator-level optimizations [6][20]. Summary by Sections 1. Evolution of Pangu Models - The Pangu model series began with PanGu-α, a 200 billion parameter autoregressive Chinese language model, which established a technical route based on Ascend hardware [6][8]. - PanGu-Σ, launched in 2023, marked an exploration into trillion-parameter models, introducing a sparse architecture to reduce computational costs [8][10]. - Pangu 3.0 introduced a "5+N+X" architecture, focusing on industry-specific applications and enabling rapid deployment of AI capabilities across various sectors [15][16]. 2. Maximizing Ascend Hardware Efficiency - Pangu Pro MoE and Pangu Ultra MoE are designed to maximize the efficiency of Ascend hardware, with Pangu Pro MoE addressing load imbalance through a grouped expert mixture architecture [25][26]. - Pangu Ultra MoE employs a system-level optimization strategy, utilizing simulation-driven design to enhance performance on Ascend hardware [46][47]. 3. CloudMatrix Infrastructure - CloudMatrix serves as the physical foundation for AI inference, addressing new challenges posed by large language models and enabling high-performance computing through a distributed memory pool [6][20]. - The infrastructure supports various software innovations, allowing for efficient communication and optimization of AI models [6][20]. 4. Full-Stack Collaboration Strategy - Huawei's strategy emphasizes open-source models to build an ecosystem around Ascend hardware, integrating architecture, systems, and operators for comprehensive collaboration [6][20].

Core Viewpoint - NVIDIA is seeking to resume the sale of its H20 GPU in China, having received assurances from the U.S. government regarding the granting of necessary licenses, which is crucial for initiating commercial activities in the Chinese market [2][10]. Group 1: H20 GPU Development and Compliance - The H20 GPU was developed in response to U.S. export control regulations that were updated in October 2023, which set specific performance thresholds for chips sold to China [5]. - The H20's design adheres to the U.S. Bureau of Industry and Security (BIS) regulations, ensuring its total processing performance (TPP) is below the 4800 TPP limit, despite its performance being significantly lower than the banned H100 chip [6][8]. - The H20 features a floating-point performance of 148 TFLOPS, compared to the H100's 1979 TFLOPS, and has increased HBM3 memory capacity from 80GB to 96GB [6][7]. Group 2: Market Dynamics and Competition - The announcement of H20's potential return comes amid a rapidly evolving competitive landscape in China, where local AI chip manufacturers are gaining market share due to U.S. export restrictions [12][13]. - By 2025, the share of AI servers in China sourced from local suppliers is expected to rise to 40%, nearly equal to that of foreign suppliers, indicating a significant shift in the market [13]. - Local companies have made substantial investments in AI infrastructure, with a notable example being China Mobile's procurement of 7994 AI servers, predominantly from Huawei's ecosystem [14]. Group 3: Strategic Shifts and Future Outlook - NVIDIA is also introducing the NVIDIA RTX PRO GPU, aimed at industrial AI applications, which aligns with China's push for manufacturing upgrades and presents lower export control risks [10]. - The competitive landscape for NVIDIA's H20 is challenging, as local firms have established strong footholds in the AI infrastructure market, necessitating NVIDIA to justify its product's performance reductions and adapt to local market conditions [11][16]. - The decision for Chinese customers to return to NVIDIA's technology ecosystem will be complex, given their investments in local alternatives [17].