ashvardanian/NumKong

Health v2 · maintenance only

Adoption not part of the score

1877 stars · 128 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NumKong is a mixed-precision numeric and linear-algebra library with ~2,000 SIMD-accelerated kernels for dot products, distances, matrix multiplication, geospatial, and geometric computations across 15+ numeric types (from 4-bit integers and 6-bit floats to 128-bit complex). It targets x86, Arm, RISC-V, LoongArch, Power, and WebAssembly, with bindings for C, C++, Python, Rust, JavaScript, Go, and Swift.

Use cases

• compute dot products and distances faster than numpy or scipy
• run mixed-precision matrix multiplication on cpu
• accelerate vector search similarity metrics with simd
• calculate geodesic distances faster than geopy
• score colbert maxsim late interaction without a gpu
• use low-precision int4 and float6 kernels in python
• run blask-like math in webassembly in the browser

When to choose

• you need fast, numerically stable low-precision math (int8, fp16, bf16, fp6) on cpu
• you want a tiny, dependency-free BLAS-like library with multi-language bindings
• you need SIMD portability across x86, Arm, RISC-V, and WASM
• you need single-call low latency rather than graph-based throughput frameworks

When to avoid

• you need GPU or distributed training workloads where PyTorch/JAX excel
• you need the full BLAS/LAPACK API surface (eigen decomposition, solvers)
• you rely on a mature ecosystem with long-term community support

Facets

library · maturity active

math benchmarking machine-learning geospatial search-engine performance machine-learning data-science developer-tools python rust cpp go wasm cross-platform simd blas linear-algebra mixed-precision dot-product matrix-multiplication vector-search arm-neon amx sme risc-v numpy scipy tensors distance-metrics algorithms javascript swift gpu

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