# Simd

> Browse 5 items tagged with Simd.

## Items

- [rust-vector-db](https://vectordb.ever.works/de/items/rust-vector-db) — rust-vector-db is a lightweight, educational vector database implemented in Rust, leveraging memory safety, high performance, and SIMD instructions for efficient vector storage and retrieval. It supports HNSW indexing, product quantization, disk persistence, and distance metrics like cosine similarity, Euclidean, and dot product. Perfect for high-perf embedded and edge AI applications or learning purposes; more performant and safer than Python-based libraries like Chroma.
- [ruvector-core](https://vectordb.ever.works/de/items/ruvector-core) — Rust core for high-performance on-device HNSW vector search with SIMD and compression, achieving low-latency multi-threaded queries for edge AI RAG. Up to 3,597 QPS; optimized for real-time vs cloud alternatives.
- [Prime Radiant](https://vectordb.ever.works/de/items/prime-radiant) — Coherence Gate engine using sheaf Laplacian for mathematical consistency checks in AI responses. Implements compute ladder routing (Reflex to Human), LLM hallucination blocking, GPU/SIMD acceleration, and cryptographic audit trails.
- [ruvector-solver](https://vectordb.ever.works/de/items/ruvector-solver) — Library providing sublinear-time solvers for large-scale math problems like PageRank, graph Laplacians, and AI attention using 8 algorithms including Neumann Series, Conjugate Gradient, Forward/Backward Push, and more. Optimized for scale with SIMD SpMV, fused kernels, and arena allocators; supports WASM and NAPI bindings.
- [@ruvector/attention](https://vectordb.ever.works/de/items/ruvectorattention) — Library implementing 46 attention mechanisms including dot-product, multi-head, Flash, linear, hyperbolic, graph, and sheaf attention. Supports SIMD optimization, streaming, caching, hard negative mining, and hyperbolic math functions for transformers and GNNs.

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_Canonical page: https://vectordb.ever.works/de/tags/simd_