# onnx/onnx-mlir

Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure

Repository: https://github.com/onnx/onnx-mlir
Canonical: https://ross.abutalabs.com/products/onnx-mlir
Language: C++
License: Apache-2.0
License Family: permissive
Last push: 2026-08-24T20:43:07+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 60, longevity 100
- inputs: {"age_days": 2388, "days_push": 9, "days_rel": 23, "gap_med": 126.5, "n_releases_24m": 5}
- flags: prerelease_only
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1046, forks 449 (observed 2026-08-28T04:03:21.643131+00:00)

## What it is
ONNX-MLIR is a compiler infrastructure built on LLVM/MLIR that lowers ONNX neural network graphs into executable code, MLIR files, LLVM bytecode, or C/Java libraries. It includes an ONNX dialect, an onnx-mlir driver, and Python/C/C++/Java runtime support for executing compiled models.

## Use cases
- compile onnx models to native code
- run onnx models without python runtime
- lower neural network graphs to llvm bytecode
- generate c or java libraries from onnx models
- optimize deep learning model inference with mlir
- deploy onnx models on ibm telum accelerator
- integrate onnx dialect into custom compiler pipelines

## When to choose
- you need ahead-of-time compilation of ONNX models to native code
- you want minimal runtime dependencies for model inference
- you target CPUs or IBM Telum accelerators
- you are building compiler tooling around MLIR for ML workloads

## When to avoid
- you just need to run ONNX models quickly with existing runtimes like ONNX Runtime
- you need GPU training or broad GPU inference support
- you want a high-level training framework rather than a compiler

## Facets
- artifact type: library
- maturity: active
- function: compiler, machine-learning, llm-inference
- domain: machine-learning, compilers, developer-tools
- platform: windows, cpp, python, jvm
- tags: onnx, mlir, llvm, model-compilation, code-generation, inference-runtime, linux, macos

## Member repositories
- onnx/onnx-mlir (main) score 86

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:21.643131+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:01:58.669770+00:00, confidence not recorded.
  - readme: https://github.com/onnx/onnx-mlir (fetched 2026-08-28T04:03:21.643131+00:00, sha c8d1d2a36d44)
- Data as of 2026-08-30T08:39:29.467469+00:00.