Core Viewpoint - The semiconductor industry recognizes that while developing a chip may take three years, it often takes a decade for developers to write code for that chip. The success of NVIDIA's CUDA is fundamentally a victory of software stack and developer ecosystem. For domestic GPUs, merely matching computational power is insufficient for long-term competitiveness; the real challenge lies in establishing a deeply integrated hardware-software architecture that allows global developers to transition seamlessly [1][3]. Group 1: MUSA Ecosystem and Achievements - The MUSA developer conference showcased a strong consensus on the need for an ecosystem breakthrough, emphasizing that it was not just a technical release but a large-scale event with around 1,000 participants [1]. - Over the past five years, the company has made significant strides, including the development of five chips, an investment exceeding 4.3 billion yuan in R&D, a 77% R&D personnel ratio, and over 200,000 active developers, highlighting its unique position in the domestic GPU sector [3]. Group 2: MUSA Architecture - MUSA (Meta-computing Unified System Architecture) is not merely a software package; it encompasses a full-stack technology system that integrates chip architecture, instruction sets, programming models, and software libraries, enabling developers to efficiently write, migrate, and optimize code on the company's GPUs [6][8]. - The MUSA architecture defines unified technical standards from chip design to software ecosystem, similar to how Android and Windows function as platforms rather than just software installers [8]. Group 3: Full-Function GPU - The concept of a "full-function GPU" is rooted in its ability to handle multiple tasks, including graphics rendering, AI tensor computation, physical simulation, and ultra-high-definition video encoding, making it versatile for various applications [12][15]. - The evolution of GPU capabilities has been pivotal in the computing revolution, transitioning from graphics acceleration to general computing and now to AI-driven applications [10][14]. Group 4: New Architectures and Innovations - The latest "Huagang" architecture has been introduced, featuring a 50% increase in computational density and a tenfold improvement in computational efficiency, along with new asynchronous programming models and AI-driven rendering capabilities [19][21]. - The company has filed over 1,000 patents, with more than 500 granted, establishing a leading position in the domestic GPU industry [21]. Group 5: Key Products - The "Huashan" chip is designed for AI training and inference, featuring advanced load balancing and a new generation of Tensor Cores optimized for AI applications, significantly enhancing computational efficiency [24][25]. - The "Lushan" chip, aimed at high-performance graphics rendering, boasts a 15-fold increase in 3A game performance and a 64-fold increase in AI computing performance compared to previous models [28][30]. Group 6: AI Factory and Large-Scale Systems - The company is advancing towards building AI factories capable of supporting over 100,000 GPUs, addressing challenges such as connectivity, fault tolerance, and energy efficiency in large-scale systems [34]. - The new MTLink 4.0 technology enhances data transmission efficiency, while the ACE 2.0 engine optimizes GPU collaboration, ensuring high stability and availability in large clusters [34]. Group 7: MUSA 5.0 Software Stack - The MUSA 5.0 upgrade represents a significant milestone, providing seamless support for various applications, including AI training and scientific computing, while ensuring compatibility with both international and domestic CPU operating systems [36][37]. - The upgrade includes enhancements in performance optimization, open-source tools, and programming languages tailored for 3D graphics and AI applications, improving developer efficiency [40]. Group 8: Embodied Intelligence and AI SoC - The company is venturing into embodied intelligence with the launch of the "Changjiang" AI SoC, integrating multiple computational cores to support advanced AI applications in robotics and next-generation devices [39]. - The MT Lambda simulation platform aims to enhance the efficiency of transitioning from simulation to real-world applications, providing a comprehensive solution for embodied intelligence [42]. Group 9: Developer Ecosystem - The success of the domestic GPU ecosystem hinges on attracting developers, addressing high migration costs, and improving toolchains and documentation [46]. - The MUSA software stack is designed to enhance developer experience, facilitating a smooth transition to domestic GPUs while ensuring compatibility with mainstream ecosystems [47].

Core Insights - The core message of the news is that Moore Threads is evolving from a GPU manufacturer to a comprehensive computing infrastructure company, similar to Nvidia, but tailored to address the unique challenges of the Chinese market [2][3]. Group 1: Product Development and Strategy - Moore Threads introduced its full-featured GPU architecture "Huagang" and the MUSA (Meta-computing Unified System Architecture) at the MDC 2025, emphasizing a shift towards a more integrated approach to computing [2][5]. - The company aims to support a wide range of applications, including graphics, AI, HPC, and video processing, through its full-featured GPU, which is designed to handle mixed computing tasks rather than focusing solely on AI [2][5][6]. - The MUSA architecture encompasses a complete technology stack, from chip architecture to software frameworks, allowing for versatile applications across different industries [7][8][10]. Group 2: Technical Innovations - The full-featured GPU integrates multiple computing engines, including AI computation, 3D graphics rendering, high-performance computing, and intelligent video encoding, to meet diverse computational needs [6][7]. - The latest MUSA upgrade to version 5.0 enhances compatibility with various programming languages and significantly improves performance metrics, such as achieving over 98% efficiency in core computation libraries [10][11]. - The "Huagang" architecture boasts a 50% increase in computing density and substantial improvements in energy efficiency, supporting a wide range of precision from FP4 to FP64 [12][14]. Group 3: Market Position and Future Outlook - Moore Threads is positioning itself as a key player in the domestic GPU market, leveraging its unique methodologies to tackle challenges such as supply chain uncertainties and technological barriers [3][5]. - The company is set to release two new chips, "Huashan" for AI training and "Lushan" for high-performance graphics rendering, which are expected to significantly enhance capabilities in their respective fields [14][15]. - The launch of the "Kua'a" supercomputing cluster marks a significant milestone, achieving floating-point performance of 10 Exa-Flops and demonstrating high efficiency in AI training and inference tasks [15][16].

Core Viewpoint - The article highlights the rapid rise of China's information technology industry, particularly in the chip sector, with a focus on the unicorn company Moore Threads, which is on track to become the first domestic GPU stock [2][3]. Group 1: Company Overview - Moore Threads has made significant progress in its IPO application, receiving approval from the Shanghai Stock Exchange in just 88 days, a much shorter timeframe compared to the average of around 200 days for other companies [4]. - Founded by Zhang Jianzhong, a former NVIDIA executive, Moore Threads aims to develop a full-function GPU with its self-developed architecture, MUSA, which supports a wide range of precision calculations [7][8]. - The company has a diverse product lineup, including consumer GPUs, professional graphics acceleration cards, and AI computing products, with a total R&D investment of 3.81 billion yuan from 2022 to 2024 [8][10]. Group 2: Financial Performance - Moore Threads has accumulated losses exceeding 5 billion yuan over the past three years, despite its products showing competitive performance against NVIDIA's offerings [8][12]. - The company plans to raise 8 billion yuan through its IPO, with a focus on AI chip and graphics chip development, aiming to enhance its strategic transformation and product technology iteration [13][14]. - In 2024, Moore Threads is projected to achieve revenue of 432 million yuan, with a significant increase in revenue expected in the first half of 2025, reaching 702 million yuan [14][15]. Group 3: Market Position and Competition - Despite the rapid growth in AI computing demand, Moore Threads faces intense competition, with its market share in AI computing products currently below 1% [16]. - The article notes that other domestic GPU companies are also pursuing IPOs, indicating a growing trend in the capital market for similar firms [17].

Investment Rating - The report maintains an "In-Line" industry view for Greater China Technology Semiconductors [6]. Core Insights - China's AI GPU self-sufficiency is projected to increase from 34% in 2024 to 82% by 2027, with the total addressable market (TAM) for cloud AI expected to grow at a CAGR of 28% to reach US$239 billion by 2027, with China accounting for approximately US$48 billion [2][17]. - The overall semiconductor self-sufficiency ratio in China is currently at 24%, up from 20% in 2023, driven by government subsidies, inventory digestion, and capacity ramp-up in memory and leading node products [3][11]. - Local GPU suppliers like Huawei and Cambricon are primarily supported by SMIC, which faces capacity expansion challenges [2][18]. Summary by Sections AI GPU Self-Sufficiency - The self-sufficiency ratio for AI GPUs in China is estimated at 34% for 2024 and expected to rise to 82% by 2027, with significant growth in the cloud AI market [2][17]. - The TAM for cloud AI is projected to grow to US$48 billion in 2027, with China expected to capture 20% of global demand [2][17]. Semiconductor Market Overview - China's semiconductor market was valued at approximately US$183 billion in 2023, with local companies generating US$43 billion in revenue, marking a 36% increase from US$32 billion in 2023 [3]. - The self-sufficiency ratio for semiconductors in China is currently at 24%, reflecting a 4 percentage point increase from the previous year [3][11]. Localization Progress - Significant advancements have been made in memory, image sensors, and power semiconductors, while equipment and EDA progress has been slower than expected [8]. - Local vendors in memory and power semiconductors are benefiting from the growth in electric vehicles and gaining market share over global competitors [8]. Stock Implications - The report is equal-weight on SMIC and Empyrean Technology, noting that while SMIC is crucial for local AI chip production, potential Nvidia acquisitions could impact domestic GPU market share [4]. - Positive outlook on China wafer fab equipment makers like Naura, AMEC, and ACM Research due to local foundry and memory capacity expansion [4].