In recent years, NVIDIA and Tesla have intensified their rivalry in the field of autonomous driving technology. Initially, Tesla relied on NVIDIA’s Drive PX series chips to power its Autopilot system. However, starting in 2019, Tesla shifted to its in-house developed Full Self-Driving (FSD) chip, emphasizing vertical integration and tight hardware-software co-optimization to boost performance and reduce costs. This move marked a strategic divergence from collaboration to competing technological philosophies.NVIDIA continues to pursue an open-platform strategy. Its DRIVE Orin and upcoming Thor chips are adopted by numerous automakers—including XPeng, NIO, and Mercedes-Benz—highlighting high compute power, scalability, and ecosystem compatibility. In contrast, Tesla sticks to a closed yet highly customized approach, leveraging massive real-world driving data to continuously train its neural networks and achieve an end-to-end autonomous driving experience.At the core of their disagreement lies a fundamental choice: NVIDIA champions a universal, flexible computing platform to empower the entire industry, while Tesla believes true autonomy can only be realized through full control over chips, algorithms, and the data feedback loop. This clash between ‘open vs. closed’ and ‘general-purpose vs. custom-built’ not only shapes their individual trajectories but also reflects broader debates about the future evolution of autonomous driving.
近年来，英伟达（NVIDIA）与特斯拉（Tesla）在自动驾驶技术领域的竞争日益激烈。起初，特斯拉依赖英伟达的Drive PX系列芯片为其Autopilot系统提供算力支持。然而，自2019年起，特斯拉开始转向自主研发的FSD（Full Self-Driving）芯片，强调垂直整合与软硬件协同优化，以提升性能并降低成本。这一转变标志着双方从合作关系走向技术路线的分道扬镳。英伟达则继续深耕开放平台战略，其DRIVE Orin和Thor芯片被广泛应用于多家车企，包括小鹏、蔚来、奔驰等，主打高算力、可扩展性和生态兼容性。相比之下，特斯拉坚持封闭但高度定制化的方案，通过海量真实驾驶数据不断训练其神经网络，追求端到端的自动驾驶体验。两者的根本分歧在于：英伟达主张通用、灵活的计算平台，赋能整个行业；而特斯拉则相信只有完全掌控芯片、算法与数据闭环，才能实现真正的自动驾驶。这场“开放 vs 封闭”、“通用 vs 定制”的技术之争，不仅影响着各自的发展路径，也折射出自动驾驶产业未来可能的演进方向。
原创文章，作者：admin，如若转载，请注明出处：https://avine.cn/12702.html