Comparison
jax vs onnx-mlir
Verdict
Pick jax if jAX is a high-performance numerical computing library for Python that integrates automatic differentiation and compilation, suitable for GPU and TPU acceleration; pick onnx-mlir if oNNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Markdown twin · jax alternatives · onnx-mlir alternatives
GraphCanon updated Sep 30, 2026
12views this month
Trust & integrity
| Signal | jax | onnx-mlir |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 26, 2026 · github_public_v1 | Very active (0d since push) As of Sep 30, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 26, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- jax
- Composable transformations of Python+NumPy programs
- onnx-mlir
- ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes
Stars
- jax
- 36k
- onnx-mlir
- 1.1k
Forks
- jax
- 3.8k
- onnx-mlir
- 451
Open issues
- jax
- 2.6k
- onnx-mlir
- 367
Language
- jax
- Python
- onnx-mlir
- MLIR
Adopt for
- jax
- JAX is a high-performance numerical computing library for Python that integrates automatic differentiation and compilation, suitable for GPU and TPU acceleration.
- onnx-mlir
- ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Persona
- jax
- -
- onnx-mlir
- -
Runtime
- jax
- -
- onnx-mlir
- -
License
- jax
- Apache-2.0
- onnx-mlir
- Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms.
Last pushed
- jax
- Sep 26, 2026
- onnx-mlir
- Sep 29, 2026
Categories
- jax
- Inference & Serving, Model Training
- onnx-mlir
- Inference & Serving, Model Training
Trust and health
Open issues (now)
- jax
- 2.6k
- onnx-mlir
- 367
Stars delta
- jax
- +253 (30d)
- onnx-mlir
- +19 (30d)
Open issues delta
- jax
- +52 (30d)
- onnx-mlir
- +15 (30d)
OSV dependency advisories
- jax
- No lockfile (source not queried)
- onnx-mlir
- Published findings
Full report
- jax
- Trust report
- onnx-mlir
- Trust report
Shared compatibility
- Python · jax: Python runtime · onnx-mlir: Python runtime
Choose jax if…
- jax is primarily Python; onnx-mlir is MLIR.
- Tags unique to jax: compilation, differentiation, gpu, python.
- - When you need to perform high-performance numerical computations with support for both forward and reverse mode automatic differentiation on accelerators such as GPUs and TPUs.
When NOT to use jax
- - JAX should be avoided if your codebase heavily relies on non-JIT compatible operations or side effects within Python functions, due to JAX's limitations in those areas.
- - For applications that do not require GPU/TPU acceleration and where performance gains from automatic differentiation and compilation are not critical.
Choose onnx-mlir if…
- onnx-mlir is primarily MLIR; jax is Python.
- Tags unique to onnx-mlir: compiler, llvm, mlir, onnx.
- For users needing compile-time optimization of ONNX models to improve inference performance in a variety of language runtimes such as C++, Java, and Python
When NOT to use onnx-mlir
- When quick setup and environment management are desired without using prebuilt containers, as setting up prerequisites manually may be challenging
- For teams primarily focused on real-time inference serving with dedicated AI hardware that requires specialized frameworks not covered by ONNX-MLIR's support matrix
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (jax-ml/jax) · observed Sep 26, 2026
- GitHub forks (jax-ml/jax) · observed Sep 26, 2026
- Last push (jax-ml/jax) · observed Sep 26, 2026
- License file (Apache-2.0) · observed Sep 26, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (onnx/onnx-mlir) · observed Sep 30, 2026
- GitHub forks (onnx/onnx-mlir) · observed Sep 30, 2026
- Last push (onnx/onnx-mlir) · observed Sep 29, 2026
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: jax 36k · onnx-mlir 1.1k (synced Sep 26, 2026).
Common questions
- What is the difference between jax and onnx-mlir?
- jax: Composable transformations of Python+NumPy programs. onnx-mlir: ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes. See the comparison table for live GitHub stats and shared categories.
- When should I choose jax over onnx-mlir?
- Choose jax over onnx-mlir when jax is primarily Python; onnx-mlir is MLIR; Tags unique to jax: compilation, differentiation, gpu, python; - When you need to perform high-performance numerical computations with support for both forward and reverse mode automatic differentiation on accelerators such as GPUs and TPUs.
- When should I choose onnx-mlir over jax?
- Choose onnx-mlir over jax when onnx-mlir is primarily MLIR; jax is Python; Tags unique to onnx-mlir: compiler, llvm, mlir, onnx; For users needing compile-time optimization of ONNX models to improve inference performance in a variety of language runtimes such as C++, Java, and Python.
- When should I avoid jax?
- - JAX should be avoided if your codebase heavily relies on non-JIT compatible operations or side effects within Python functions, due to JAX's limitations in those areas. - For applications that do not require GPU/TPU acceleration and where performance gains from automatic differentiation and compilation are not critical.
- When should I avoid onnx-mlir?
- When quick setup and environment management are desired without using prebuilt containers, as setting up prerequisites manually may be challenging For teams primarily focused on real-time inference serving with dedicated AI hardware that requires specialized frameworks not covered by ONNX-MLIR's support matrix
- Is jax or onnx-mlir more popular on GitHub?
- jax has more GitHub stars (36,338 vs 1,058). Stars measure visibility, not whether either tool fits your constraints.
- Are jax and onnx-mlir open source?
- Yes - both are open-source projects on GitHub (jax: Apache-2.0, onnx-mlir: Apache-2.0).
- Where can I find alternatives to jax or onnx-mlir?
- GraphCanon lists graph-backed alternatives at jax alternatives and onnx-mlir alternatives (jax markdown twin, onnx-mlir markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, jax or onnx-mlir?
- jax: Very active. onnx-mlir: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for jax and onnx-mlir?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: jax trust report; onnx-mlir trust report.