jaxlib
XLA library for JAX
Decision gist · record as of 2026-08-14
Yes, if you are using JAX. jaxlib is a required dependency for JAX to function; it cannot be avoided. It is actively maintained, has no known vulnerabilities, uses a permissive license, and offers prebuilt wheels for modern Python on common platforms. Install friction is moderate due to platform-specific compilation, but prebuilt binaries eliminate the need to compile from source on supported systems.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; compiled wheels are platform-specific (Linux x86_64/aarch64, macOS arm64, Windows x86_64).
- Medium install friction due to platform-specific compiled wheels; prebuilt binaries are available for Python 3.12, 3.13, and 3.14 across Linux x86_64, Linux aarch64, macOS arm64, and Windows x86_64.
- Active maintenance with recent releases.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2026-07-16 (29 days) · last repo commit 2026-08-14 · 36,158 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 18,816,096 downloads/mo, #1,078 on PyPI
Alternatives
Verify before relying
pip install jaxlib
import jaxlib
# jaxlib provides the compiled backend; typical usage is indirect
# through higher-level APIs that depend on scipy, numpy, ml_dtypes- Whether jaxlib can be installed from source on platforms without prebuilt wheels
- GPU/TPU driver and runtime version requirements for hardware acceleration
- Performance characteristics and memory overhead relative to alternatives
- Specific CUDA, ROCm, or other accelerator framework version compatibility
What it is and what it does
jaxlib is the low-level compiled library that powers JAX's numerical computing engine. It wraps XLA (Accelerated Linear Algebra) to compile and execute array operations on accelerators—GPUs, TPUs, and CPUs—with automatic optimization and fusion. It depends on scipy, numpy, and ml_dtypes for numerical operations and type support.
jaxlib is essential infrastructure for anyone using JAX for machine learning, scientific computing, or large-scale numerical work. It handles the translation from Python code into compiled, device-optimized kernels. The package is actively maintained, supports Python 3.12, 3.13, and 3.14, and is available as prebuilt wheels for common platforms, making installation straightforward on supported systems.
Use it for
- Running machine learning models on GPU or TPU clusters for training and inference at scale
- Accelerating numerical simulations and scientific computing workloads
- Enabling automatic differentiation and JIT compilation for high-performance gradient computation
- Deploying applications that require hardware acceleration on cloud or on-premise infrastructure
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are using JAX.
jaxlib is a required dependency for JAX to function; it cannot be avoided. It is actively maintained, has no known vulnerabilities, uses a permissive license, and offers prebuilt wheels for modern Python on common platforms. Install friction is moderate due to platform-specific compilation, but prebuilt binaries eliminate the need to compile from source on supported systems.
Install
jaxlib on PyPI
Before you install
Medium install friction due to platform-specific compiled wheels; prebuilt binaries are available for Python 3.12, 3.13, and 3.14 across Linux x86_64, Linux aarch64, macOS arm64, and Windows x86_64. Active maintenance with recent releases.
Requires Python 3.12 or later; compiled wheels are platform-specific (Linux x86_64/aarch64, macOS arm64, Windows x86_64).
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install jaxlib
import jaxlib
# jaxlib provides the compiled backend; typical usage is indirect
# through higher-level APIs that depend on scipy, numpy, ml_dtypesVerify before relying
- Whether jaxlib can be installed from source on platforms without prebuilt wheels
- GPU/TPU driver and runtime version requirements for hardware acceleration
- Performance characteristics and memory overhead relative to alternatives
- Specific CUDA, ROCm, or other accelerator framework version compatibility
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagesscipynumpyml_dtypes |
| Maintenance | Actively maintained 29 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 18,816,096 / month, #1,078 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Free Threading :: 3 - Stable |
Evidence: jaxlib-0.11.0-cp312-cp312-macosx_11_0_arm64.whl; jaxlib-0.11.0-cp312-cp312-manylinux_2_27_aarch64.whl; jaxlib-0.11.0-cp312-cp312-manylinux_2_27_x86_64.whl; jaxlib-0.11.0-cp312-cp312-win_amd64.whl; jaxlib-0.11.0-cp313-cp313-macosx_11_0_arm64.whl; jaxlib-0.11.0-cp313-cp313-manylinux_2_27_aarch64.whl; jaxlib-0.11.0-cp313-cp313-manylinux_2_27_x86_64.whl; jaxlib-0.11.0-cp313-cp313-win_amd64.whl; jaxlib-0.11.0-cp314-cp314-macosx_11_0_arm64.whl; jaxlib-0.11.0-cp314-cp314-manylinux_2_27_aarch64.whl; jaxlib-0.11.0-cp314-cp314-manylinux_2_27_x86_64.whl; jaxlib-0.11.0-cp314-cp314t-macosx_11_0_arm64.whl; jaxlib-0.11.0-cp314-cp314t-manylinux_2_27_aarch64.whl; jaxlib-0.11.0-cp314-cp314t-manylinux_2_27_x86_64.whl; jaxlib-0.11.0-cp314-cp314-win_amd64.whl
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See also jax-cuda12-plugin · jax · torchax · klujax · jax-jumpy · jax-cuda13-plugin · tokamax · jax-cuda13-pjrt · jax-cuda12-pjrt · chex