A 10 million document corpus takes 31 GB of RAM as float32. turbovec fits it in 4 GB - and searches it faster than FAISS.
turbovec is a Rust vector index with Python bindings, built on Google Research's
TurboQuant
algorithm — a data-oblivious quantizer with near-optimal distortion and no separate training phase.
Online ingest.
Add vectors, they're indexed — no train step, no parameter tuning, no rebuilds as the corpus grows.
Fast SIMD search.
Hand-written NEON (ARM) and AVX-512BW (x86) kernels beat FAISS IndexPQFastScan by 10–19% on ARM; on x86 they win the 4-bit configs and trail by a few percent on 2-bit.
Filter at search time.
Pass an id allowlist (or a slot bitmask) to
search()
and the kernel honours it directly. You always get up to
k
results from the allowed set — no over-fetching, n (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://github.com/RyanCodrai/turbovec)获取最准确的信息。

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🔗 **原文链接**: [RyanCodrai/turbovec (⭐ 280 stars today)](https://github.com/RyanCodrai/turbovec)
🏷️ **转载来源**: GitHub Trending
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。

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> _转载自 GitHub Trending，内容版权归原作者所有_

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⏱️ 2026-07-17 22:01