cupy vs jax
GPU array computing and numerical program transformations compared
cupy provides NumPy- and SciPy-compatible GPU array computing for NVIDIA CUDA and AMD ROCm. jax transforms Python numerical programs through automatic differentiation, compilation, vectorization, and sharding, and supports CPUs and accelerators including GPUs and TPUs.
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.
jax: Transform and Accelerate Python Numerical Programs
JAX is a Python library for transforming numerical programs with automatic differentiation, compilation, and vectorization. Use it for high-performance scientific computing and machine learning, especially when workloads need to scale across accelerators.
| cupy | jax | |
|---|---|---|
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Stars | 12.4k | 36.4k |
| Forks | 1.1k | 3.8k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- cupy focuses on GPU-backed array operations with familiar NumPy and SciPy APIs; jax focuses on transforming numerical functions with differentiation, compilation, and vectorization.
- cupy exposes GPU-specific controls such as RawKernels, streams, and CUDA Runtime API access; jax offers function transformations and automatic, explicit, or manual sharding.
- cupy targets NVIDIA CUDA and AMD ROCm GPUs; jax supports CPUs and accelerators, including GPUs and TPUs, subject to platform and installation support.
- cupy is MIT-licensed; jax is Apache-2.0-licensed.
- cupy notes that some NumPy or SciPy APIs may need changes when porting; jax cautions that its programming model has sharp edges and that jax.jit constrains Python control flow.
- cupy lists pip, Conda, and Docker installation options; jax describes itself as a research project.
Choose cupy if you…
- need GPU-backed arrays with NumPy- and SciPy-compatible APIs on supported CUDA or ROCm hardware.
- want lower-level GPU controls, including RawKernels or CUDA Runtime API access.
- prefer to move suitable NumPy-based numerical workflows to a GPU with fewer changes than a full rewrite.
Choose jax if you…
- need automatic differentiation, including higher-order derivatives, for numerical programs.
- want to compile or vectorize functions using jax.jit or jax.vmap.
- need to scale computations across devices using JAX sharding approaches.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.