@techwith_ram: A 10M document corpus eats 31 GB of RAM as float32 Most teams hit that wall & reach for a managed vector database. $400…
Summary
turbovec is an open-source Rust vector index using Google Research's TurboQuant algorithm, achieving 16x compression and faster search than FAISS, with integrations for RAG frameworks like LangChain, LlamaIndex, and Haystack.
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@hasantoxr: Vector databases are no longer a cloud product. They're becoming a pip install. A new open-source project called turbov…
An open-source project called turbovec has reached 10K stars on GitHub. It is a Rust-based vector index with Python bindings that uses Google Research's TurboQuant algorithm to compress embeddings to near the theoretical Shannon limit, enabling fully local RAG with 10 million documents fitting in 4 GB RAM and searching faster than FAISS.
@dr_cintas: Google's new algorithm just shrunk 31GB of memory down to 4GB TurboVec is a new open-source tool that stores the data y…
Google's TurboVec is a new open-source tool that reduces memory usage from 31GB to 4GB for AI search data, leveraging TurboQuant for faster search than FAISS, and integrates with LangChain and LlamaIndex while running fully offline.
@vintcessun: Compressing 10 million vectors from 31GB to 4GB, with search even faster than FAISS — sounds crazy, but Turbovec actually did it. The core is Google's TurboQuant data-independent quantization: no training, no parameter tuning, just add vectors and index. Handwritten NEON/AVX-512 implementations are genuinely 12-20% faster, supporting filtered search by ID, saving a ton of post-processing hassle. Rust under the hood + pip install, minimal maintenance cost.
Turbovec, based on Google's TurboQuant algorithm, compresses 10 million vectors from 31GB to 4GB, with search speed 12-20% faster than FAISS, supports filtered search, and offers a Rust implementation with a Python package.
RyanCodrai/turbovec
turbovec is a Rust vector index with Python bindings implementing Google's TurboQuant algorithm, offering efficient vector search with online ingest, faster-than-FAISS performance, and filtered search capabilities.
@techwith_ram: Your AI stack has a database problem You need a vector DB for embeddings. A graph DB for relationships. An application …
HelixDB is a new open-source database built in Rust that combines vector, graph, and other data models into a single engine, backed by Y Combinator. It aims to replace separate vector, graph, and application databases for AI stacks, offering native vector search, graph traversal, and MCP support.