[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-turbovec-rust-vector-index-4gb-10m-docs-en":3,"article-related-turbovec-rust-vector-index-4gb-10m-docs-en":31,"series-tools-0d39c74b-f225-4dab-af04-d3fafccb3221":84},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":23,"views":27,"created_at":28,"published_at":29,"topic_cluster_id":30},"0d39c74b-f225-4dab-af04-d3fafccb3221","turbovec-rust-vector-index-4gb-10m-docs-en","TurboVec: Rust vector index cuts 10M docs to 4GB","\u003Cp data-speakable=\"summary\">TurboVec is a \u003Ca href=\"\u002Ftag\u002Frust\">Rust\u003C\u002Fa> vector index with Python bindings that compresses large corpora and supports filtered search.\u003C\u002Fp>\u003Cp>10 million documents in 4 GB of RAM: that is the headline from \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FRyanCodrai\u002Fturbovec\" target=\"_blank\" rel=\"noopener\">RyanCodrai\u002Fturbovec\u003C\u002Fa>, a Rust vector index with Python bindings built on \u003Ca href=\"\u002Ftag\u002Fgoogle\">Google\u003C\u002Fa> Research’s \u003Ca href=\"\u002Ftag\u002Fturboquant\">TurboQuant\u003C\u002Fa> method. The project says the same corpus needs 31 GB as float32, and that TurboVec searches it faster than FAISS in its published benchmarks.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>數值\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Corpus size\u003C\u002Ftd>\u003Ctd>10 million documents\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>RAM with float32\u003C\u002Ftd>\u003Ctd>31 GB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>RAM with TurboVec\u003C\u002Ftd>\u003Ctd>4 GB\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Repository stars\u003C\u002Ftd>\u003Ctd>3.8k\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Forks\u003C\u002Ftd>\u003Ctd>347\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Commits\u003C\u002Ftd>\u003Ctd>144\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Benchmark speedup on ARM\u003C\u002Ftd>\u003Ctd>12–20% over FAISS FastScan\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Benchmark result on x86\u003C\u002Ftd>\u003Ctd>1–6% faster on 4-bit configs\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>What changed\u003C\u002Fh2>\u003Cp>TurboVec packages TurboQuant as a local-first index that can ingest vectors online, skip training, and avoid rebuilds as the corpus grows. It exposes both a simple \u003Ccode>TurboQuantIndex\u003C\u002Fcode> and an \u003Ccode>IdMapIndex\u003C\u002Fcode> for stable external IDs, plus write\u002Fload persistence for Python and Rust users.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780156972364-fgdi.png\" alt=\"TurboVec: Rust vector index cuts 10M docs to 4GB\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The repo also adds filtered search inside the SIMD kernel. Users can pass an allowlist or slot bitmask at query time, and the index returns up to \u003Ccode>k\u003C\u002Fcode> results from only the allowed set. The project says this avoids over-fetching and preserves recall for selective filters.\u003C\u002Fp>\u003Cul>\u003Cli>Rust core with Python bindings\u003C\u002Fli>\u003Cli>Online ingest, no separate train phase\u003C\u002Fli>\u003Cli>Filtered search with allowlists or bitmasks\u003C\u002Fli>\u003Cli>Local-only use for air-gapped or VPC deployments\u003C\u002Fli>\u003Cli>Adapters for LangChain, LlamaIndex, Haystack, and Agno\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Why it matters\u003C\u002Fh2>\u003Cp>For developers building \u003Ca href=\"\u002Ftag\u002Frag\">RAG\u003C\u002Fa> systems, the pitch is lower memory use without handing data to a managed vector service. That makes the project relevant for privacy-sensitive apps, embedded deployments, and teams that need dense retrieval on modest hardware.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780156971511-6gv8.png\" alt=\"TurboVec: Rust vector index cuts 10M docs to 4GB\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>The \u003Ca href=\"\u002Ftag\u002Fbenchmark\">benchmark\u003C\u002Fa> claims are also aimed at a familiar comparison point. TurboVec says its hand-written NEON and AVX-512BW kernels beat FAISS IndexPQFastScan on ARM and hold close on x86, which could make it an attractive drop-in for teams already using FAISS-style workflows.\u003C\u002Fp>\u003Cp>The broader question is whether TurboVec’s compression and filtered-search path hold up across real production corpora, not just the repo’s benchmark sets. If they do, the project gives teams a cheaper way to keep vector search local and memory-light.\u003C\u002Fp>","TurboVec is a Rust vector index with Python bindings that compresses 10M documents to 4GB RAM and adds filtered search, local-only RAG, and framework adapters.","github.com","https:\u002F\u002Fgithub.com\u002FRyanCodrai\u002Fturbovec",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780156972364-fgdi.png","tools","en","3aa7ba61-2181-4ca8-be11-b26bf62899d1",[17,18,19,20,21,22],"vector search","Rust","Python bindings","TurboQuant","FAISS","RAG",[24,25,26],"10M documents fit in 4 GB, versus 31 GB as float32 in the repo’s claim.","TurboVec supports online ingest and filtered search without a separate training step.","The project targets local-only RAG and ships adapters for major Python frameworks.",4,"2026-05-30T16:02:25.996487+00:00","2026-05-30T16:02:25.991+00:00","a7343b93-37cc-4634-a2bc-707f6275bdb6",{"tags":32,"relatedLang":43,"relatedPosts":47},[33,35,37,39,41],{"name":18,"slug":34},"rust",{"name":17,"slug":36},"vector-search",{"name":21,"slug":38},"faiss",{"name":19,"slug":40},"python-bindings",{"name":20,"slug":42},"turboquant",{"id":15,"slug":44,"title":45,"language":46},"turbovec-rust-vector-index-4gb-10m-docs-zh","TurboVec：Rust 向量索引把 1,000 萬文件壓到 4GB","zh",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":13},"1e0d71a2-19ae-44f4-970b-d27f77ad5a8a","nvidia-lg-ai-collaboration-playbook-en","Nvidia and LG turn AI plans into a 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