Organizationjax-ml
@jax-ml • Pushing back the limits on numerical computing.. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
95%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
13 repos
Visible snapshot
9 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (8), C++ (2), SCSS (1)
Average size
3.1K
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-01-10(290d ago)GitHub API fallback13 repositories
95%
of the visible star count comes from this organization's top three repositories.
3.1K
stars per repository in this same snapshot.
Python
is the most common language here, with 9 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 90% of its visible star count.
Flagship share 90%Breakout repo: jax
Organization pages work best when you separate portfolio breadth from flagship concentration. In jax-ml's case, the visible top three repositories account for about 95% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (8), C++ (2), SCSS (1). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | jax-ml/jax Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more | Python | 36.5K |
| 2 | jax-ml/scaling-book Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs | SCSS | 1.5K |
| 3 | jax-ml/jax-triton jax-triton contains integrations between JAX and OpenAI Triton | Python | 473 |
| 4 | jax-ml/ml_dtypes A stand-alone implementation of several NumPy dtype extensions used in machine learning. | C++ | 364 |
| 5 | jax-ml/oryx Oryx is a library for probabilistic programming and deep learning built on top of Jax. | Python | 327 |
| 6 | jax-ml/jax-ai-stack | Python | 314 |
| 7 | jax-ml/jax-llm-examples Minimal yet performant LLM examples in pure JAX | Python | 286 |
| 8 | jax-ml/bayeux State of the art inference for your bayesian models. | Python | 249 |
| 9 | jax-ml/bonsai Minimal, lightweight JAX implementations of popular models. | Python | 246 |
| 10 | jax-ml/coix Inference Combinators in JAX | Jupyter Notebook | 54 |
| 11 | jax-ml/jax-tpu-embedding High-performance large-scale embedding acceleration for JAX on Google TPU SparseCores. | Python | 38 |
| 12 | jax-ml/australis | C++ | 28 |
| 13 | jax-ml/jax-blog | 6 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.