cupy vs numba
GPU array computing and numerical Python compilation compared
cupy provides NumPy- and SciPy-compatible array computing on NVIDIA CUDA and AMD ROCm GPUs. numba uses LLVM to compile a supported subset of numerical Python to machine code, with options for loop parallelization and GPU code generation.
cupy: Run NumPy and SciPy Workloads on GPUs
CuPy is a Python array library that brings NumPy- and SciPy-compatible computing to NVIDIA CUDA and AMD ROCm GPUs. It suits Python users who want GPU acceleration while reusing familiar APIs, or who need lower-level GPU controls.
numba: Compile Numerical Python to Machine Code
Numba compiles numerically focused Python functions to machine code using LLVM, with support for many NumPy operations. It suits Python developers who need faster numerical workloads, loop parallelization, or GPU code generation without rewriting everything in a lower-level language.
| cupy | numba | |
|---|---|---|
| Language | Python | Python |
| License | MIT | BSD-2-Clause |
| Stars | 12.4k | 11.2k |
| Forks | 1.1k | 1.3k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- cupy centers on GPU-backed arrays and familiar NumPy and SciPy APIs; numba centers on compiling supported Python functions.
- cupy targets NVIDIA CUDA and AMD ROCm platforms; numba offers GPU code generation, while its listed information does not specify GPU configurations.
- cupy exposes GPU-specific controls such as RawKernels, streams, and CUDA Runtime API access; numba offers automatic loop parallelization and creation of ufuncs and C callbacks.
- cupy is MIT-licensed; numba is BSD-2-Clause-licensed.
- cupy lists installation options through pip, Conda, and Docker; numba's guidance emphasizes checking supported Python syntax and operations.
Choose cupy if you…
- want NumPy- and SciPy-compatible GPU arrays and operations.
- need to target supported CUDA or ROCm hardware for array-heavy workloads.
- want lower-level GPU controls such as RawKernels or CUDA streams.
Choose numba if you…
- need to compile supported numerical Python functions while continuing to write Python.
- want to explore automatic parallelization for loop-heavy numerical tasks.
- need to create ufuncs or C callbacks for numerical integrations.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.