在传统存储产品方面，10nm以下DRAM制造工艺正成为主流，并逐步向7nm工艺突破，通过“FinFET架构+TSV技术”提升密度、降低功耗。3D NAND堆叠层数突破400层后，“垂直堆叠”难度加剧，厂商转向“水平扩展+架构优化”，比如三星V-NAND的阶梯式架构、Kioxia的BiCS架构，同时引入“HKC（高K介质+金属栅）”技术，解决高层数堆叠的漏电、散热问题，制造工艺从“层数竞赛”转向“架构+工艺”双重竞争。
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.
，这一点在新收录的资料中也有详细论述
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