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Documentation Index

Fetch the complete documentation index at: https://numpyts.dev/llms.txt

Use this file to discover all available pages before exploring further.

numpy-ts is 1.13x faster than NumPy on average across 7,159 benchmarks (small, medium, and large arrays) and runs natively in JavaScript + WASM with zero dependencies. It’s faster in 12 of 18 categories, with standout wins in gradient computation (4.3x), polynomial operations (3.2x), and boolean logic (2.3x).
All benchmarks compare computation time between JS (numpy-ts) and Python (NumPy with OpenBLAS), measured on each side respectively. See methodology for details.

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Performance by Category

numpy-ts outperforms NumPy in most categories. NumPy leads in bitwise, trig, and math — all active areas of improvement.
Performance by category: numpy-ts vs NumPy
See the full breakdown of category results on the numpy-ts vs. NumPy page.

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Performance by Data Type

Smaller data types see the biggest gains — numpy-ts’s SIMD kernels process more elements per instruction for int8, uint8, and float16. Even float64 (NumPy’s home turf) is on par.
Performance by data type: numpy-ts vs NumPy
See the full breakdown of dtype results on the numpy-ts vs. NumPy page.

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Performance by Array Size

numpy-ts is as fast or faster than NumPy at every tested array scale — from small (100-element) arrays where low overhead matters, to large (10K-element) arrays where SIMD throughput dominates.
Performance by array size: numpy-ts vs NumPy
See the full breakdown of array size results on the size scaling page.

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All Benchmarks

vs. NumPy (Native)

How does numpy-ts compare to NumPy running natively in Python with OpenBLAS?

vs. NumPy (Pyodide)

How does numpy-ts compare to NumPy running in WebAssembly via Pyodide?

Performance Scaling by Size

How does numpy-ts performance scale across small, medium, and large array sizes?

Node.js, Deno & Bun

How does numpy-ts perform across different JavaScript runtimes?