视频版： 压缩版： 理想VLA做L两个原因，技术原因是图语言的长推理能力，需要语言的token输入输出是次要的。非技术原因是更容易价值观对齐。 认为最后5%10%corner case很难靠数据或世界模型自己撞出来，而需要具备类似人的推理能力。 和业界一样，在思考GPGPU是不是AI时代的终极答案。从CPU到GPU到GPGPU，本质上是冯诺依曼架构，冯诺依曼架构核心本质是程序主要关注的是 计算不是数据，数据是第二等公民，计算是一等公民。 在AI时代，计算的算子没那么多，提出的问题是，能不能让程序更多关注数据，而不是关注计算。 理想自研的车端计算架构主要是NPU,不是SOC。SOC无非是前处理后处理的CPU Cluster，加一些IO在外面与内存访存控制器。NPU里面是一个重合架 构，加一个CCB(Central Control Computing Block)用来做一些前处理后处理，不适合非张量的计算，每个class是同构的，用Mesh Bus连在一起，也提供 Ring Bus(环形总线)做广播。原话"这个是我们完全是我们独创的一个AI推理架构，目前国内没有这么做的。" 比较挑战的是编译器（涉及很多编程模型和 ...

Core Viewpoint - The article discusses the advancements in Li Auto's self-developed chip architecture, particularly focusing on the VLA architecture and its implications for autonomous driving capabilities [1][2]. Group 1: Chip Development and Architecture - Li Auto's self-developed chip is designed with a data flow architecture that emphasizes hardware-software co-design, making it suitable for running large neural networks efficiently [5][9]. - The chip is expected to achieve 2x performance compared to leading chips when running large language models like GPT and 3x for vision models like CNN [5][8]. - The development timeline from project initiation to vehicle deployment is approximately three years, indicating a rapid pace compared to similar projects [5][8]. Group 2: Challenges and Innovations - Achieving real-time inference on the vehicle's chip is a significant challenge, with efforts focused on optimizing performance through various engineering techniques [3][4]. - Li Auto is implementing innovative parallel decoding methods to enhance the efficiency of action token inference, which is crucial for autonomous driving [4]. - The integration of CPU, GPU, and NPU in the Thor chip aims to improve versatility and performance in processing large amounts of data, which is essential for autonomous driving applications [3][6]. Group 3: Future Outlook - The company expresses strong confidence in its innovative architecture and full-stack development capabilities, which are expected to become key differentiators in the future [7][10]. - The relationship between increased computing power and improved performance in advanced driver-assistance systems (ADAS) is highlighted, suggesting a predictable enhancement in capabilities as technology evolves [6][9].