Core Insights - The report highlights the emergence of AI infrastructure software (AI Infra) as a critical enabler for the deployment of generative AI applications, marking a golden development period for infrastructure software [1] - Unlike the model training phase dominated by tech giants, the inference and application deployment stages present new commercial opportunities for independent software vendors [1] - Key products in this space include computing scheduling software and data-related software, with computing scheduling capabilities directly impacting the profitability of model inference services [1][2] Computing Scheduling - AI Infra is designed to efficiently manage and optimize AI workloads, focusing on large-scale training and inference tasks [2] - Cost control is crucial in the context of a price war among domestic models, with Deepseek V3 pricing significantly lower than overseas counterparts [5] - Major companies like Huawei and Alibaba have developed advanced computing scheduling platforms that enhance resource utilization and reduce GPU requirements significantly [5][6] - For instance, Huawei's Flex:ai improves utilization by 30%, while Alibaba's Aegaeon reduces GPU usage by 82% through token-level dynamic scheduling [5][6] Profitability Analysis - The report indicates that optimizing computing scheduling can serve as a hidden lever for improving gross margins, with a potential increase from 52% to 80% in gross margin by enhancing single-card throughput [6] - The sensitivity analysis shows that a 10% improvement in throughput can lead to a gross margin increase of 2-7 percentage points [6] Vector Databases - The rise of RAG (Retrieval-Augmented Generation) technology has made vector databases a necessity for enterprises, with Gartner predicting a 68% adoption rate by 2025 [10] - Vector databases are essential for supporting high-speed retrieval of massive datasets, which is critical for RAG applications [10] - The demand for vector databases is expected to surge, driven by a tenfold increase in token consumption from API integrations with large models [11] Database Landscape - The data architecture is shifting from "analysis-first" to "real-time operations + analysis collaboration," emphasizing the need for low-latency processing [12][15] - MongoDB is positioned well in the market due to its low entry barriers and adaptability to unstructured data, with significant revenue growth projected [16] - Snowflake and Databricks are expanding their offerings to include full-stack tools, with both companies reporting substantial revenue growth and customer retention rates [17] Storage Architecture - The transition to real-time AI inference is reshaping storage architecture, with a focus on reducing IO latency [18] - NVIDIA's SCADA solution demonstrates significant improvements in IO scheduling efficiency, highlighting the importance of storage performance in AI applications [18][19]

Nvidia has acquired SchedMD and said it will continue to distribute that company’s open-source Slurm software.By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions .Complete the form to unlock this article and enjoy unlimited free access to all PYMNTS content — no additional logins required.Slurm, a workload management system for high-performance ...

Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [1] Core Insights - The report highlights the launch of Huawei's AI container technology Flex:ai, which addresses the low utilization efficiency of computing power in the industry, currently averaging only 30% to 40%. Flex:ai enhances utilization by 30% through precise segmentation of GPU/NPU resources [4][5] - The report emphasizes the unique advantages of Flex:ai over Nvidia's Run:ai, particularly in virtualization and intelligent scheduling, which can optimize resource allocation for AI workloads [5][6] - The development of Flex:ai is seen as a significant step in strengthening domestic computing power capabilities, promoting a complete open-source ecosystem for AI tools [6][7] Summary by Sections Industry Overview - The closing index is at 5068.36, with a 52-week high of 5841.52 and a low of 3963.29 [1] Performance Analysis - The relative performance of the computer industry compared to the CSI 300 index shows fluctuations, with a notable decline of 13% from November 2024 to November 2025 [3] Key Developments - Huawei's Flex:ai is positioned to significantly improve AI cluster computing efficiency and reduce migration barriers for AI models, reinforcing the software capabilities in the domestic computing landscape [6][7] - The report suggests monitoring companies involved in AI containers and domestic computing power, including BoRui Data, Haohan Deep, and others [7]

Core Insights - The rapid development of the AI industry is creating a massive demand for computing power, but the low utilization rate of global computing resources is becoming a significant bottleneck for industry growth [1] - Huawei's new AI container technology, Flex:ai, aims to address the issue of computing resource waste by allowing a single GPU/NPU card to be divided into multiple virtual computing units, improving resource utilization by 30% [1][2] - Flex:ai is positioned to compete with Nvidia's Run:ai, focusing on software innovation to unify management and scheduling of various computing resources without hardware limitations [2] Group 1 - Flex:ai technology can split a single GPU/NPU card into virtual computing units with a precision of 10%, enabling multiple AI workloads to run simultaneously [1] - The technology has been validated in real-world applications, such as the RuiPath model developed in collaboration with Ruijin Hospital, which improved resource utilization from 40% to 70% [3] - Gartner predicts that by 2027, over 75% of AI workloads will be deployed and run using container technology, indicating a shift towards more efficient resource management [3] Group 2 - Flex:ai will be open-sourced in the Magic Engine community, contributing to Huawei's comprehensive ModelEngine open-source ecosystem for AI training and deployment [3] - Unlike Run:ai, which primarily serves the Nvidia GPU ecosystem, Flex:ai supports a broader range of computing resources, including both Nvidia GPUs and Huawei's Ascend NPUs [2]

Core Insights - Nvidia is experiencing challenges in its enterprise software sales as it attempts to onboard large clients in regulated industries while maintaining its growth trajectory amid the AI boom [1][2] Group 1: Software Sales Challenges - Internal communications reveal that Nvidia's sales team is struggling to present a unified message regarding its software offerings alongside its AI hardware [2][7] - The company is focusing on selling Nvidia AI Enterprise (NVAIE) and other software products, but there is a need for a comprehensive narrative to effectively communicate these offerings to clients [4][6] - A July email indicated that stand-alone software sales are projected to exceed targets at 110%, while software sold with hardware is only expected to reach 39% of its goal [6] Group 2: Client Education and Legal Concerns - There is a significant disconnect between Nvidia and its clients' legal and procurement teams, particularly in understanding the software sales processes during negotiations [8][9] - The company is planning workshops to educate clients on NVAIE and other products, addressing the need for better internal and external education [7][8] - Data security and indemnity obligations are highlighted as major negotiation sticking points, with clients requesting higher damages caps than Nvidia is comfortable with [9] Group 3: Market Position and Future Outlook - Despite the challenges, Nvidia is forecasting strong software sales, with NVAIE expected to hit 186% of its sales target for the quarter [6][5] - The company’s software segment, while smaller, is crucial for generating recurring revenue and increasing customer dependence on its AI products [5]