Technical highlights
- TurboQuant provides data‑oblivious quantization with near‑optimal distortion and no training overhead.
- SIMD kernels operate on a vector‑major layout, allowing direct dot‑product computation without costly transposes.
- On ARM, kernels use NEON SDOT/SMMLA; on x86 they leverage AVX‑512 VNNI and `vpermb`.
- Benchmarks (100 K vectors, 1 K queries, k = 64) show median single‑thread speeds 3.4× faster than FAISS at 4‑bit and 20‑30 % faster at 2‑bit across both architectures.
- Insertion latency per vector is 6‑20 µs (≈8‑14× faster than FAISS), and deletions are O(1) at sub‑microsecond cost.
- Compression plots demonstrate up to 8× reduction in RAM vs raw float32.

Who should use turbovec?
- Engineers building Retrieval‑Augmented Generation (RAG) systems where memory, latency, or data‑privacy are critical.
- Teams that need a drop‑in FAISS alternative but want better speed and smaller footprints.
- Rust or Python developers who prefer a single‑library solution with native SIMD performance.
- Anyone integrating vector stores into LangChain, LlamaIndex, Haystack, or custom pipelines.

One‑liner takeaway
lets you store massive embedding collections in a few gigabytes and search them faster than FAISS – all while staying completely local.

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