Comparison
onnx-mlir vs pytorch
Verdict
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; pick pytorch if dynamic computation graphs with GPU acceleration.
Markdown twin · onnx-mlir alternatives · pytorch alternatives
GraphCanon updated Sep 30, 2026
12views this month
Trust & integrity
| Signal | onnx-mlir | pytorch |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 30, 2026 · github_public_v1 | Very active (0d since push) As of Sep 27, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 27, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No published findings from this source as of 2026-07-11 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
- onnx-mlir
- ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes
- pytorch
- Tensors and Dynamic neural networks in Python with strong GPU acceleration
Stars
- onnx-mlir
- 1.1k
- pytorch
- 103k
Forks
- onnx-mlir
- 451
- pytorch
- 31k
Open issues
- onnx-mlir
- 367
- pytorch
- 18k
Language
- onnx-mlir
- MLIR
- pytorch
- Python
Adopt for
- onnx-mlir
- ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
- pytorch
- Dynamic computation graphs with GPU acceleration.
Persona
- onnx-mlir
- -
- pytorch
- -
Runtime
- onnx-mlir
- -
- pytorch
- -
License
- onnx-mlir
- Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms.
- pytorch
- Other
Last pushed
- onnx-mlir
- Sep 29, 2026
- pytorch
- Sep 27, 2026
Categories
- onnx-mlir
- Inference & Serving, Model Training
- pytorch
- Inference & Serving, Model Training
Trust and health
Open issues (now)
- onnx-mlir
- 367
- pytorch
- 18k
Stars delta
- onnx-mlir
- +19 (30d)
- pytorch
- +1.2k (30d)
Open issues delta
- onnx-mlir
- +15 (30d)
- pytorch
- -803 (30d)
OSV dependency advisories
- onnx-mlir
- Published findings
- pytorch
- No published findings from this source as of 2026-07-11
Full report
- onnx-mlir
- Trust report
- pytorch
- Trust report
Shared compatibility
- Python · onnx-mlir: Python runtime · pytorch: Python runtime
Choose onnx-mlir if…
- onnx-mlir is primarily MLIR; pytorch is Python.
- License: onnx-mlir is Apache-2.0, pytorch is Other.
- 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
Choose pytorch if…
- pytorch is primarily Python; onnx-mlir is MLIR.
- License: pytorch is Other, onnx-mlir is Apache-2.0.
- Tags unique to pytorch: autograd, deep-learning, gpu, machine-learning.
- pytorch ships Docker support for self-hosted deployment.
- Required dynamic computation graph functionality for flexible model architectures
When NOT to use pytorch
- Static graph frameworks like TensorFlow are preferred for simpler, less variable models
- Environments with limited GPU support or requiring multi-language compatibility
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (pytorch/pytorch) · observed Sep 27, 2026
- GitHub forks (pytorch/pytorch) · observed Sep 27, 2026
- Last push (pytorch/pytorch) · observed Sep 27, 2026
- License file (Other) · observed Sep 27, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: onnx-mlir 1.1k · pytorch 103k (synced Sep 30, 2026).
Common questions
- What is the difference between onnx-mlir and pytorch?
- onnx-mlir: ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes. pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration. See the comparison table for live GitHub stats and shared categories.
- When should I choose onnx-mlir over pytorch?
- Choose onnx-mlir over pytorch when onnx-mlir is primarily MLIR; pytorch is Python; License: onnx-mlir is Apache-2.0, pytorch is Other; 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 choose pytorch over onnx-mlir?
- Choose pytorch over onnx-mlir when pytorch is primarily Python; onnx-mlir is MLIR; License: pytorch is Other, onnx-mlir is Apache-2.0; Tags unique to pytorch: autograd, deep-learning, gpu, machine-learning; pytorch ships Docker support for self-hosted deployment; Required dynamic computation graph functionality for flexible model architectures.
- 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
- When should I avoid pytorch?
- Static graph frameworks like TensorFlow are preferred for simpler, less variable models Environments with limited GPU support or requiring multi-language compatibility
- Is onnx-mlir or pytorch more popular on GitHub?
- pytorch has more GitHub stars (103,384 vs 1,058). Stars measure visibility, not whether either tool fits your constraints.
- Are onnx-mlir and pytorch open source?
- Yes - both are open-source projects on GitHub (onnx-mlir: Apache-2.0, pytorch: Other).
- Where can I find alternatives to onnx-mlir or pytorch?
- GraphCanon lists graph-backed alternatives at onnx-mlir alternatives and pytorch alternatives (onnx-mlir markdown twin, pytorch 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, onnx-mlir or pytorch?
- onnx-mlir: Very active. pytorch: 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 onnx-mlir and pytorch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: onnx-mlir trust report; pytorch trust report.