Financial Data and Key Metrics Changes - The demand for AI training chips has seen fluctuations, with a notable decrease in the urgency for GPU procurement compared to the previous year, attributed to high initial demand and tightening government budgets [16][19][20] - The price of GPUs has decreased significantly, with reductions of around 20% observed in the market [16] Business Line Data and Key Metrics Changes - Companies like Zhipu, Baichuan, and MiniMax primarily relied on third-party computing power leasing, with a gradual shift towards self-built infrastructures, although the transition is still in early stages [13][19] - The rental market remains dominated by NVIDIA's A100 and H100 models, with A800 also seeing increased usage due to better cost-performance ratios [15][16] Market Data and Key Metrics Changes - The market for AI chips is currently characterized by a cautious approach towards domestic alternatives, with companies actively testing local chips but still favoring NVIDIA due to supply stability concerns [20][25] - The overall supply of NVIDIA chips has been impacted by restrictions, leading to a heightened interest in domestic alternatives, although their availability remains inconsistent [24][25] Company Strategy and Development Direction - Companies are increasingly considering self-built computing clusters as a long-term strategy, driven by the need for greater control and customization in their AI training processes [11][19] - The competitive landscape is shifting, with major players like Alibaba and Tencent exploring both domestic chip options and self-research initiatives alongside traditional NVIDIA solutions [30][37] Management Comments on Operating Environment and Future Outlook - The management emphasizes the complexity of the current market, where rapid technological advancements necessitate flexible procurement strategies, including leasing and self-building [11][12] - There is a recognition that while domestic chips are being explored, the immediate reliance on NVIDIA remains due to performance and ecosystem advantages [20][23] Other Important Information - The performance of Huawei's 910B chip is reported to be around 80% of the A800's capabilities, but its higher cost and lower ecosystem support limit its attractiveness [30][38] - The integration of domestic chips into existing infrastructures is seen as a significant challenge, with many companies hesitant to invest heavily without guaranteed performance [31][41] Q&A Session Summary Question: What changes have been observed in the computing power foundation of AI companies? - The computing power foundation for companies like Zhipu and Baichuan has not seen a significant reduction in third-party leasing, but there is an ongoing search for new vendors [13] Question: What types of chips are being prioritized in the rental market? - The rental market is primarily focused on NVIDIA's A100 and H100, with A800 also gaining traction due to its cost-effectiveness [15] Question: How are companies approaching the integration of domestic chips? - Companies are actively testing domestic chips but remain cautious due to supply stability issues, with a preference for NVIDIA when available [20][25] Question: What is the outlook for self-built computing clusters? - There is a strong belief that companies will eventually move towards self-built clusters for better control and customization, despite the current reliance on leasing [11][19] Question: How does the performance of Huawei's chips compare to NVIDIA's? - Huawei's 910B is estimated to perform at about 80% of the A800's capabilities, but its higher cost and lack of ecosystem support hinder its adoption [30][38]