onnx-mlir
ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes
GraphCanon updated Sep 30, 2026 · GitHub synced Sep 30, 2026
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Decision brief
ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Good fit when
- 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
- If the target system is Linux, macOS, or Windows and benefits from an LLVM-based backend for deployment flexibility
Avoid when
- 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
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 30, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 30, 2026
- Security (OSV)
- 3 low (3 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/onnx/onnx-mlirSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides ONNX Dialect integration, compilers for transforming ONNX models into code using the underlying LLVM/MLIR infrastructure, with support for C++, Java runtime environments, cross-platform compatibility
Capability facts
- Languages
- mlir, python
Source: github.language+pyproject.toml · Sep 30, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 30, 2026)
* and a python/C/C++/Java runtime environment.Source link
Tags
README
<p align="center" <img width="50%" src="docs/logo/onnx mlir 1280x640.png" / </p ONNX MLIR This project (https://onnx.ai/onnx mlir/) provides compiler technology to transform a valid Open Neural Network Exchange (ONNX) graph into code that implements the graph with minimum runtime...
For agents
This page has a .md twin and JSON over the API.