Nvidia(US:NVDA) Guohai Securities·2025-09-17 11:02Investment Rating - The report maintains a "Buy" rating for the computer industry [1] Core Insights - The demand for AI computing power is expected to grow significantly, driven by advancements in large model training and the introduction of new architectures like GB300 and Vera Rubin [11] - NVIDIA's revenue for FY2026 Q2 reached $46.7 billion, a year-on-year increase of 56%, indicating strong market demand for AI computing solutions [5][59] - The report highlights the performance improvements of NVIDIA's new GPU architectures, with the GB300 achieving a 1.5x increase in FP4 computing power compared to its predecessor [30] Summary by Sections Section 1: GPU Core - The GB300 GPU, based on the Blackwell Ultra architecture, utilizes TSMC's 4NP process and features a floating-point performance of 15 PFLOPS, which is 1.5 times that of the B200 [5][26] - The Rubin Ultra NVL576 is expected to launch in 2027, offering significant performance enhancements over the GB300 NVL72 [11][31] Section 2: Server Details - The GB300 NVL72 system consists of 18 compute trays and 9 switch trays, integrating 72 Blackwell Ultra GPUs and 36 Grace CPUs, with potential performance improvements of up to 50 times compared to previous architectures [6][80] - The report discusses the transition from HGX to MGX server designs, allowing for more efficient AI and HPC applications [67] Section 3: Networking - The introduction of CPO technology is set to replace traditional pluggable optical modules, enhancing energy efficiency by 3.5 times and deployment speed by 1.3 times [7] - The Rubin architecture will utilize NVLink 6.0 technology, doubling the speed to 3.6 TB/s, facilitating high-speed interconnects for AI applications [7] Section 4: HBM - HBM4 is expected to achieve mass production in 2026, with SK Hynix leading the market, and collaborations with major clients like NVIDIA and Microsoft [8] Section 5: Liquid Cooling - The GB300 NVL72 employs a full liquid cooling solution, enhancing thermal efficiency and operational cost-effectiveness [9] Section 6: Investment Recommendations and Related Companies - The report identifies potential beneficiaries in the AI computing supply chain, including companies involved in AI chips, server systems, HBM, and cooling technologies [12]