Model2vec-Rs – Fast Static Text Embeddings in Rust
Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance:…
In plain words
Model2vec-rs is a Rust library for generating static text embeddings without Python dependencies. It loads Model2Vec models from Hugging Face or local paths and runs inference at high throughput in microservices, CLI tools, and applications requiring semantic search, retrieval, or retrieval-augmented generation. The crate is lightweight at 1.7 MB with embedding models ranging from 7 to 30 MB, delivering approximately 1.7 times faster performance than Python implementations on single-threaded CPU inference.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance: We benchmarked single-threaded on a CPU: - Python: ~4650 embeddings/sec - Rust: ~8000 embeddings/sec (~1.7× speedup) First open-source project in Rust for us, so would be great to get some feedback!
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