"当前灵初智能在训练中使用的是英伟达的显卡芯片，在模型端侧部署上使用的是英伟达的Orin和Thor。"钟伊凡说。 相比大模型训练相对集中的场景，机器人应用更为碎片化，既有成本压力，也需要长期场景验证。"竞争对手依然可以在低功耗芯片、细分场景或开源生态 上形成差异化优势。" 北京时间8月26日，英伟达发布机器人计算平台Jetson Thor的12个小时后，在北京海淀的灵初智能办公室里，机器人Psi V1亲自打开了自己的"新脑子"。 这是全球首批到货的Jetson Thor之一。目前，这套芯片已经以开发者套件的形式正式发售，售价为3499美元。 对于英伟达给机器人送上的"新大脑"，灵初智能的工程师钟伊凡告诉第一财经记者，Thor在算力和数据处理能力上的突破，能够让机器人直接在端侧运行大 规模、高性能的模型，从而完成更通用和复杂的任务。 然而，云端部署也带来了现实问题。"延迟很难避免，对于一些高节拍的场景，例如连续抓取、快速判断等，延迟会直接影响产品的安全性和可行性。"上述 高管直言。 钟伊凡说，Thor在数据处理能力和接口带宽上的提升，让机器人能够直接承接来自高分辨率、高频率传感器的多模态输入，并及时完成处理。因 ...

Core Viewpoint - The article discusses the evolution of the partnership between Tesla and NVIDIA in the autonomous driving sector, highlighting the challenges and innovations that have shaped their collaboration. Group 1: Tesla's Journey in Autonomous Driving - In September 2013, Tesla officially entered the autonomous driving arena, emphasizing internal development rather than relying on external technologies [5] - Initially, Tesla partnered with Mobileye due to the lack of suitable self-developed autonomous driving chips, enhancing Mobileye's technology with unique innovations like Fleet Learning [9][12] - Tensions arose between Tesla and Mobileye as Tesla sought to develop its own algorithms, leading to Mobileye's demand for Tesla to halt its internal vision efforts [12][13] Group 2: NVIDIA's Strategic Shift - In 2012, NVIDIA's CEO Jensen Huang recognized the potential of autonomous driving in electric vehicles, leading to a focus on deep learning and computer vision [15] - By November 2013, Huang highlighted the importance of digital computing in modern vehicles, indicating a shift towards automation in the automotive industry [17] - In January 2015, NVIDIA launched the DRIVE brand, introducing the DRIVE PX platform, which provided significant computational power for autonomous driving applications [18] Group 3: The Partnership Development - Following a significant accident in May 2016, Mobileye ended its partnership with Tesla, prompting Tesla to choose NVIDIA as its new technology partner [19][20] - In October 2016, Tesla announced that all its production models would feature hardware capable of full self-driving capabilities, utilizing NVIDIA's DRIVE PX 2 platform [20] - By early 2017, Tesla publicly announced its plans to develop its own chips, indicating a shift in its strategy while NVIDIA continued to expand its automotive partnerships [25][26] Group 4: Technological Advancements - In 2018, NVIDIA introduced the DRIVE Xavier platform, which improved computational performance while reducing power consumption [28] - Tesla's HW3, launched in April 2019, was described by Musk as the most advanced computer designed specifically for autonomous driving, marking the end of NVIDIA's direct involvement in Tesla's autonomous driving hardware [30][32]

Core Viewpoint - Huang Renxun, CEO of NVIDIA, is actively engaging with the Chinese market despite ongoing U.S. sanctions on semiconductor exports to China, highlighting the importance of China as a critical market for NVIDIA's growth and future opportunities [5][12][16]. Group 1: NVIDIA's Market Position and Challenges - NVIDIA has achieved a market capitalization exceeding 4 trillion yuan, driven by the global AI boom, but faces significant challenges due to U.S. export restrictions on its A100 and H100 chips to China [4][23]. - The company’s revenue from the Chinese market reached $17.1 billion in 2024, marking a 66% year-on-year increase, contributing 13% to NVIDIA's total revenue [17][18]. - The U.S. government's strict AI chip export regulations have led to a significant decline in NVIDIA's market share in Asia, dropping from 95% to 50% [20]. Group 2: Huang Renxun's Engagement with China - Huang Renxun has made multiple visits to China, emphasizing the importance of the Chinese market and expressing a desire to continue collaboration with Chinese companies [15][16]. - During his visits, he has praised China's rapid AI development and robust supply chain, indicating a strong commitment to maintaining NVIDIA's presence in the market [15][17]. - Huang's efforts include addressing employee morale in China amidst fears of layoffs due to the impact of U.S. sanctions [6][14]. Group 3: Product Adaptations and Future Prospects - In response to export restrictions, NVIDIA has developed a "special supply version" of its H100 chip, named H20, which has significantly reduced performance but is tailored for the current needs of Chinese companies [25][26]. - Huang Renxun anticipates that the H20 chip will find success in the Chinese market, despite its limitations, as companies are eager to invest in AI capabilities [26]. - The emergence of domestic competitors in China, such as Huawei, poses a potential threat to NVIDIA's market dominance, especially as these companies advance their own chip technologies [27][28].

Core Viewpoint - The article emphasizes the significant progress made in the third year of the company's journey, highlighting advancements in autonomous driving technology and the expansion of services beyond online education to include hardware and offline training [1][2]. Group 1: Company Progress - The company has developed four key intellectual properties (IPs): Autonomous Driving Heart, Embodied Intelligence Heart, 3D Vision Heart, and Large Model Heart, with a focus on embodied intelligence and large models in the third year [1]. - The company has transitioned from a purely online education model to a comprehensive service platform that includes hardware teaching tools, offline training, and job recruitment [1]. - A new offline office has been established in Hangzhou, and several talented individuals have joined the team [1]. Group 2: Industry Insights - The article reflects on the challenges of maintaining long-term value in business, emphasizing that short-term economic returns are insufficient for sustainable growth [2]. - It discusses the importance of understanding market needs and business pain points through direct research, rather than merely chasing immediate profits [4]. - The company advocates for a balanced approach of focusing on long-term value while also achieving commercial success along the way [4]. Group 3: Innovation and Execution - The company stresses the necessity of innovation and execution as key factors for survival and growth in the competitive landscape of the AI education and self-media industries [7][8]. - It highlights the importance of deep thinking and continuous innovation to produce valuable content and avoid mediocrity [7]. - The company aims to transition from being a pure education provider to a technology company, with plans to stabilize operations by the second half of 2025 [9]. Group 4: Future Plans - The company is committed to making AI education accessible to all students in need, striving to make AI easier to learn and use [10]. - A significant promotional offer has been introduced to celebrate the third anniversary, providing discounts on various courses related to autonomous driving and large models [12][14].

Summary of NVIDIA Conference Call - June 12, 2023 Company and Industry Overview - **Company**: NVIDIA (NVDA) - **Industry**: Automotive technology, specifically focusing on autonomous vehicles (AV) and related software solutions Key Points and Arguments 1. **NVIDIA's Unique Automotive Strategy**: NVIDIA provides an end-to-end platform for automotive partners, which includes tools for car design, AI model training, and simulation of AV stacks, differentiating itself from competitors who only sell chips [4][5][6] 2. **Ecosystem Approach**: NVIDIA aims to support the entire automotive ecosystem, collaborating with software companies and OEMs to enhance the development of self-driving software [6][7] 3. **Increasing Compute Requirements**: The demand for higher compute capabilities in AVs is growing due to advancements in sensor technology and the complexity of networks, necessitating a flexible, programmable architecture [10][11] 4. **Modular Architecture**: NVIDIA's architecture allows for easy upgrades and compatibility across different generations of hardware, ensuring that software can run seamlessly on newer models [12] 5. **Centralization Trend**: The industry is moving towards centralizing computing power in vehicles, reducing the number of computers needed and improving efficiency and security [13][14] 6. **Partnerships and Ecosystem Expansion**: NVIDIA is expanding its ecosystem through partnerships, such as with MediaTek, to integrate its software capabilities into third-party SoCs [15][16] 7. **Revenue Model**: NVIDIA's revenue from automotive is not solely from chip sales; software and services can represent a significant portion of revenue, especially for full-stack solutions [21][22][24] 8. **Open Ecosystem**: NVIDIA is open to working with various hardware solutions, allowing OEMs to use non-NVIDIA chips while still benefiting from NVIDIA's software and cloud services [32][34] 9. **Pipeline and Future Growth**: NVIDIA has a projected pipeline of $14 billion over the next six years, with various OEMs ramping production of new models [17][19] 10. **Cost Competitiveness**: NVIDIA's cost positioning is competitive in the L2+ and higher segments, with a focus on scalable architectures that allow customers to choose their sensor sets [60][62][64] Additional Important Insights 1. **Long-Term OEM Strategy**: OEMs are expected to develop in-house competencies for software in the long term, but many currently lack the resources and expertise to do so [52][54][57] 2. **Cautious Approach to AV Rollout**: The rollout of fully autonomous vehicles is anticipated to take longer than expected, with initial trials likely focusing on commercial goods delivery rather than passenger cars [66][68][70] 3. **Simulation Importance**: Simulation is critical for validating AV technology, as it allows for testing various scenarios without real-world risks [70] This summary encapsulates the core discussions and insights from the NVIDIA conference call, highlighting the company's strategic positioning within the automotive technology sector and its approach to fostering partnerships and innovation in autonomous vehicle development.