Making Ahnlich Faster with SIMD: A 4.7x Speedup Story
· 13 min read
Vector databases power modern search, from finding similar images to semantic document retrieval. But there's a hidden performance bottleneck: calculating distances between millions of high-dimensional vectors requires billions of operations per query. In this post, I'll show you how we used SIMD (Single Instruction, Multiple Data) to make Ahnlich 4.7x faster at these calculations.
