simsimd

What it is and what it does

SimSIMD is a mixed-precision vector math library that implements over 350 SIMD-optimized kernels for computing distances and similarities between vectors. It supports float64, float32, float16, bfloat16, int8, int4, and binary vectors, as well as sparse and complex vectors. The library is designed to replace or supplement BLAS level-1 operations and scipy.spatial functions with significantly faster implementations that leverage modern CPU instruction sets (AVX-512, SVE, NEON) on both x86 and ARM architectures.

The package ships as pre-compiled wheels for multiple platforms and Python versions, with no runtime dependencies. It handles operations like Euclidean and cosine distances for vector search, dot-products for DSP and quantum computing, Hamming and Jaccard distances for bit-level comparisons, set intersections for sparse vectors, Mahalanobis distance for scientific computing, and divergence measures for probability distributions. The library is commonly used in AI, search, and database workloads where vector similarity computation is a performance bottleneck.

Use it for:

• Vector search and similarity retrieval in semantic search or embedding-based recommendation systems
• Batch computation of pairwise distances in clustering or nearest-neighbor algorithms
• Mixed-precision inference in AI pipelines where bfloat16 or float16 vectors need fast similarity scoring
• Sparse vector operations and set intersections for text analysis or information retrieval
• Geospatial distance calculations and scientific computing with Mahalanobis distance or quadratic forms

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

SimSIMD provides SIMD-optimized kernels for computing vector distances, dot-products, and similarity measures across multiple data types and precisions, with support for spatial, probabilistic, and bit-level operations.

Yes. SimSIMD is actively maintained, has no dependencies, and offers significant performance improvements over standard libraries like scipy and NumPy for vector operations. The permissive Apache-2.0 license and broad platform support (x86, ARM, multiple Python versions) make it a low-risk addition to performance-critical workloads. Install it if vector similarity or distance computation is a measurable bottleneck in your application.

Install

simsimd on PyPI

pip install simsimd

uv add simsimd

poetry add simsimd

Installing simsimd

Before you install

Medium install friction due to compiled wheels for multiple platforms. The package is actively maintained with a recent release and no runtime dependencies, making it straightforward to integrate once installed.

License in practice

Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects without significant legal constraints.

Quickstart

pip install simsimd
import simsimd
# Compute cosine distance between two float32 vectors
dist = simsimd.cosine(a, b)

Verify before relying

• Whether Python 3.9 is actually supported despite appearing in classifiers but not in friction_evidence wheels
• Performance gains relative to scipy.spatial.distance on typical workloads outside the benchmarked 1536d case
• Whether complex vector support is available through the Python API or only in C

Package facts

Evidence: simsimd-6.5.16-cp310-cp310-macosx_10_9_x86_64.whl; simsimd-6.5.16-cp310-cp310-macosx_11_0_arm64.whl; simsimd-6.5.16-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; simsimd-6.5.16-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; simsimd-6.5.16-cp310-cp310-musllinux_1_2_aarch64.whl; simsimd-6.5.16-cp310-cp310-musllinux_1_2_x86_64.whl; simsimd-6.5.16-cp310-cp310-win_amd64.whl; simsimd-6.5.16-cp310-cp310-win_arm64.whl; simsimd-6.5.16-cp311-cp311-macosx_10_9_x86_64.whl; simsimd-6.5.16-cp311-cp311-macosx_11_0_arm64.whl; simsimd-6.5.16-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; simsimd-6.5.16-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; simsimd-6.5.16-cp311-cp311-musllinux_1_2_aarch64.whl; simsimd-6.5.16-cp311-cp311-musllinux_1_2_x86_64.whl; simsimd-6.5.16-cp311-cp311-win_amd64.whl; simsimd-6.5.16-cp311-cp311-win_arm64.whl; simsimd-6.5.16-cp312-cp312-macosx_10_13_x86_64.whl; simsimd-6.5.16-cp312-cp312-macosx_11_0_arm64.whl; simsimd-6.5.16-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; simsimd-6.5.16-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl

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