英伟达财报未超预期，最强AI芯片要推中国特供版？

Core Insights - The article highlights the rapid rise of Cambrian Technology, surpassing Kweichow Moutai to become the highest-priced stock in A-shares, driven by the booming AI market [1] - NVIDIA's stock price fell despite impressive Q2 2026 financial results, with revenue reaching $46.7 billion, a 6% increase from Q1 and a 56% year-over-year growth [2][4] - NVIDIA's CEO Jensen Huang emphasizes the company's transformation into an AI infrastructure provider, with expectations of AI infrastructure investments reaching $3 to $4 trillion by the end of the decade [18][19] Financial Performance - NVIDIA's data center revenue was $41.1 billion, a 5% increase from Q1 and a 56% year-over-year growth [8] - The company has consistently exceeded revenue expectations, leading to heightened market expectations for future performance [4][5] - NVIDIA's revenue from the Chinese market decreased to $2.769 billion, down nearly $900 million from the previous year, with its contribution to total data center revenue dropping to a "low single-digit percentage" [24][25] Product Development - NVIDIA has developed the Blackwell NVLink 72 system, which significantly enhances performance and energy efficiency [10][11] - The new Blackwell architecture's B100/B200 series offers a 2.5x performance improvement over the H100 [11] - NVIDIA is transitioning to producing compliant chips for the Chinese market, including a reduced-performance version of the Blackwell architecture [26][27] Market Trends - The demand for AI computing power is expected to grow exponentially, driven by the proliferation of inference and intelligent AI applications [21] - NVIDIA's CUDA platform and AI model frameworks have become essential tools for AI developers, creating a strong ecosystem that is difficult for customers to replace [22][23] - The Chinese market presents a significant opportunity for NVIDIA, estimated at around $50 billion this year, with a projected annual growth rate of 50% [29] Competitive Landscape - Domestic competitors are emerging, with companies like DeepSeek developing models tailored to local chip architectures [32][33] - The introduction of new parameter formats, such as UE8M0 FP8 by DeepSeek and NVFP4 by NVIDIA, indicates a competitive push in the AI training space [36][38] - As local chip manufacturers collaborate to create compatible software stacks, confidence in domestic solutions is expected to rise [43]