Numba

Health v2 · maintenance only

Adoption not part of the score

11129 stars · 1315 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Numba is an open-source NumPy-aware JIT compiler that translates a subset of Python and NumPy code into fast machine code using LLVM. It supports automatic parallelization, GPU (CUDA) acceleration, ufunc creation, and C callbacks with minimal code changes.

Use cases

• speed up numerical Python functions to near C/Fortran performance
• parallelize loops across CPU cores with minimal code changes
• compile Python code to run on NVIDIA GPUs via CUDA
• accelerate NumPy array computations and create custom ufuncs
• optimize Monte Carlo simulations and scientific algorithms
• use fast Python kernels inside Jupyter notebooks or with Dask/Spark

When to choose

• your workload is numerical/array-based Python or NumPy code that is too slow
• you want C-like speed without rewriting in C/C++ or leaving the interpreter
• you need easy CPU parallelization or GPU offloading of numeric loops

When to avoid

• your code is heavily object-oriented, uses arbitrary Python objects, or relies on unsupported libraries
• you need to compile non-numeric general-purpose Python
• you depend on features outside Numba's supported Python/NumPy subset

Facets

library · maturity stable

compiler machine-learning gpu-computing concurrency math machine-learning data-science gpu-computing performance python cross-platform jit-compiler numpy llvm cuda scientific-computing parallelization high-performance-computing algorithms gpu

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markdown · JSON · MCP: product_card(name="numba/numba")