另据快科技报道，Galaxy S26 Ultra 将搭载高通骁龙 8 Elite Gen5，官方数据显示 NPU、GPU、CPU 性能分别提升 39%、24%、19%；
引用汽车媒体知瞭汽车评论里的话，“GX押注纯视觉。摄像头加图灵芯片，照明良好的高架没压力，广州测试视频里车流穿行也确实顺。但暴雨、浓雾、进隧道那一秒的白平衡切换、对面远光直射时的逆光——这些工况下，纯视觉的感知冗余天生低于激光雷达方案。“
，这一点在夫子中也有详细论述
Мир Российская Премьер-лига|19-й тур
“中国一強”「レアアース」 日本の戦略に密着取材
Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.