working solutions. So, we're going to talk about um accelerating AI fabrics for storage. So, you know, AI, we we put a lot of effort into accelerating it and improving it uh GPUs, uh you know, from from the entire stack.And one of the things we find is that slow storage can slow down your AI training times. It'll slow down your uh your uh inference. Like even with things like rag uh there's a lot of work that's done to uh deal with the ingest phase where it's like it's kind of reading the material and so th ...

Financial Data and Key Metrics Changes - NVIDIA has a buy rating with a twelve-month target price of $200, driven by its leadership in AI and expansion into full rack scale deployments [2] - The company reported significant advancements in networking capabilities, particularly in AI data centers, emphasizing the importance of networking as a critical component of computing infrastructure [8][9] Business Line Data and Key Metrics Changes - NVIDIA's networking infrastructure has evolved from supporting eight GPUs last year to 72 GPUs this year, with future plans to support up to 576 GPUs [19][20] - The company is focusing on both scale-up and scale-out networking strategies to enhance performance and efficiency in AI workloads [15][16] Market Data and Key Metrics Changes - The demand for AI workloads is increasing, necessitating the design of data centers that can handle distributed computing and high throughput requirements [22][29] - NVIDIA's networking solutions, including InfiniBand and Spectrum X, are positioned as the gold standard for AI applications, with a focus on lossless data transmission and low latency [36][38] Company Strategy and Development Direction - NVIDIA is committed to co-designing networks with compute elements to optimize performance for AI workloads, moving beyond traditional networking paradigms [22][28] - The company aims to integrate Ethernet into AI applications, making it accessible for enterprises familiar with Ethernet infrastructure [40][42] Management's Comments on Operating Environment and Future Outlook - Management highlighted the critical role of infrastructure in determining the capabilities of data centers, emphasizing that the right networking solutions can transform standard compute engines into AI supercomputers [100][101] - The company anticipates continued innovation in networking technologies to support the growing demands of AI and distributed computing [100] Other Important Information - NVIDIA's acquisition of Mellanox has enhanced its capabilities in both Ethernet and InfiniBand technologies, allowing for a broader range of solutions tailored to customer needs [32][38] - The introduction of co-packaged silicon photonics is expected to improve optical network efficiency, reducing power consumption and increasing the number of GPUs that can be connected [84][85] Q&A Session Summary Question: What is the strategic importance of networking in AI data centers? - Networking is now seen as the defining element of data centers, crucial for connecting computing elements and determining efficiency and return on investment [8][9] Question: How does NVIDIA differentiate between scale-up and scale-out networking? - Scale-up networking focuses on creating larger compute engines, while scale-out networking connects multiple compute engines to support diverse workloads [15][16] Question: What are the advantages of NVLink over other networking solutions? - NVLink provides high bandwidth and low latency, essential for connecting GPUs in a dense configuration, making it superior for AI workloads [59][60] Question: How does the DPU enhance data center operations? - The DPU separates the data center operating system from application domains, improving security and efficiency in managing data center resources [54][56] Question: What is the future of optical networking in NVIDIA's infrastructure? - Co-packaged silicon photonics will enhance optical network efficiency, allowing for greater GPU connectivity while reducing power consumption [84][85]

英伟达 20250529 摘要 英伟达一季度确认 4.6 亿美元 H20 收入，但因美国出口管制，预计损失 25 亿美元收入，并计提 46.45 亿美元库存和采购承诺减值，对中国 AI 加速器市场准入受限将产生重大不利影响。 Blackwell 产品线增长迅速，贡献近 70%的数据中心计算收入， GB200 系列架构变革支持数据中心规模工作负载，实现最低每令牌推理 成本，主要云服务提供商开始采样 GB300 系统。 AI 工厂部署加速，本季度近 100 个视频驱动 AI 工厂运行，GPU 使用量 翻倍，各行业领导者战略性部署关键主权云项目，如 AT&T、比亚迪等。 游戏业务创下 38 亿美元新高，同比增长 42%，AI PC 笔记本产品线增 加，推出 G Force RTX 5,060 系列 GPU，任天堂 Switch 2 采用 NVIDIA 神经渲染及 AI 技术。 网络业务收入同比增长 64%至 50 亿美元，Spectrum X 产品线年收入 超 80 亿美元，新增 Google Cloud 和 Meta 为客户，推出 Spectrum X 和 Quantum X 硅光子交换机产品。 中国数据中心收入 ...

NVIDIA (NVDA) Q1 2026 Earnings Call May 28, 2025 05:00 PM ET Company Participants Toshiya Hari - VP of Investor Relations & Strategic FinanceColette Kress - EVP & CFOJensen Huang - Founder, President and CEOJoseph Moore - Managing DirectorVivek Arya - Managing DirectorCJ Muse - Senior Managing DirectorBen Reitzes - Managing Director – Head of Technology ResearchTimothy Arcuri - Managing DirectorJake Wilhelm - Vice President Operator Good afternoon. My name is Sarah, and I will be your conference operator to ...