AI / ML ML Framework stable
JAX
Composable transformations of NumPy for high-performance ML research
32.0K stars
700 contributors
Since 2018
Google's library for high-performance numerical computing with auto-differentiation, JIT compilation, and native GPU/TPU support built on XLA compiler.
License
Apache-2.0
Min RAM
2 GB
Min CPUs
2 cores
Scaling
distributed
Complexity
expert
Performance
enterprise grade
Self-hostable
✓
K8s native
✕
Offline
✓
Pricing
fully free
Docs quality
good
Vendor lock-in
none
Use cases
- ✓ Cutting-edge ML research requiring custom gradient computation
- ✓ Large-scale scientific simulation on TPU pods
- ✓ Bayesian inference with MCMC methods
- ✓ Physics-informed neural networks
Anti-patterns / when NOT to use
- ✕ Steep learning curve for production engineers
- ✕ Ecosystem smaller than PyTorch/TensorFlow
- ✕ Debugging JIT-compiled code is difficult
- ✕ Not recommended for beginners
Integrates with
Compare with alternatives
Replaces / alternatives to
Technical specs
Language
PythonC++
API type
SDK
Protocols
HTTP
Deployment
pip
SDKs
python
Community
GitHub stars 32.0K
Contributors 700
Commit frequency daily
Plugin ecosystem none
Backing Google
Funding corporate
Release
Latest version
— Last release —
Since 2018
Best fit
Team size
solosmall
Industries
researchscientific-computing