突围2025：国产GPU集体上市，然后呢？（深度好文）

Core Insights - The year 2025 is a pivotal moment for the domestic GPU industry in China, marked by a wave of IPOs from companies like Moore Threads and Muxi Technology, which highlights the urgency for funding to sustain operations and growth [2][4] - The rapid listing of these companies is driven by a need for capital to navigate the lengthy R&D phase and to ensure survival in a competitive landscape [2][4] - The challenges facing domestic GPU manufacturers extend beyond product design to include supply chain vulnerabilities and the dominance of the CUDA ecosystem, which poses significant barriers to entry [9][10] Financial Performance - Moore Threads reported cumulative revenue of 609 million yuan over the past three years (2022-2024) with a net loss of 5.005 billion yuan, while Muxi Technology had cumulative revenue of 1.117 billion yuan and a net loss of 3.29 billion yuan during the same period [4] - The financial data reflects a common trend in the GPU industry, characterized by high R&D investments and significant operational losses [4] Market Dynamics - The investment strategy in the GPU sector is described as a "wolf pack tactic," where several leading companies are encouraged to go public to secure funding, with the expectation that only a few will emerge as winners [6][7] - The current market environment is shifting from a focus on fundraising to a competitive emphasis on engineering capabilities and market delivery, as companies strive to meet the demands of large clients [14] Supply Chain and Ecosystem Challenges - The reliance on external manufacturing capabilities is identified as a critical issue, with domestic manufacturers struggling to achieve scale in production and yield control [10] - The entrenched CUDA ecosystem, developed over nearly two decades, presents a formidable challenge for domestic GPU companies, making it difficult to attract customers who face high costs in switching ecosystems [10][11] Strategic Responses - Some domestic GPU manufacturers are adopting a pragmatic approach by promoting heterogeneous mixed training, which involves using a mix of different brands within a computing cluster to enhance compatibility and robustness [11][12] - The focus is shifting towards collaboration with existing ecosystems rather than outright replacement, which is seen as a more realistic strategy for overcoming barriers [12] Future Outlook - The industry is transitioning from a narrative-driven phase to one focused on tangible results and performance metrics, with a heightened emphasis on engineering reliability and stability [14] - The explosion of AI inference demand presents new opportunities for growth, with companies exploring supernode solutions to enhance performance [15] - The competitive landscape is expected to undergo significant changes within the next year, with only a few companies likely to survive the intense competition [15]