--- title: "awesome-tensor-compilers vs onnx-mlir" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/merrymercy-awesome-tensor-compilers-vs-onnx-onnx-mlir" tools: ["merrymercy-awesome-tensor-compilers", "onnx-onnx-mlir"] --- # awesome-tensor-compilers vs onnx-mlir *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-tensor-compilers if decision-critical Facts for awesome-tensor-compilers; 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. [awesome-tensor-compilers](https://github.com/merrymercy/awesome-tensor-compilers) reports 2.8k GitHub stars, 327 forks, and 4 open issues, last pushed Oct 19, 2024. [onnx-mlir](https://github.com/onnx/onnx-mlir) has 1.0k stars, 447 forks, and 352 open issues, last pushed Jul 31, 2026. Figures are from public GitHub metadata via [awesome-tensor-compilers's repository](https://github.com/merrymercy/awesome-tensor-compilers) and [onnx-mlir's repository](https://github.com/onnx/onnx-mlir). | | [awesome-tensor-compilers](/tools/merrymercy-awesome-tensor-compilers.md) | [onnx-mlir](/tools/onnx-onnx-mlir.md) | | --- | --- | --- | | Tagline | A collection of compiler projects and papers for tensor computation and deep learning. | ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes | | Stars | 2,770 | 1,039 | | Forks | 327 | 447 | | Open issues | 4 | 352 | | Language | - | C++ | | Adopt for | Decision-critical Facts for awesome-tensor-compilers | ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments. | | Persona | - | - | | Runtime | - | - | | License | - | Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms. | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-tensor-compilers](/tools/merrymercy-awesome-tensor-compilers.md) | [onnx-mlir](/tools/onnx-onnx-mlir.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 654d | 3d | | Open issues (now) | 4 | 352 | | Owner type | User | Organization | | Full report | [trust report](/tools/merrymercy-awesome-tensor-compilers/trust.md) | [trust report](/tools/onnx-onnx-mlir/trust.md) | ## Decision facts: awesome-tensor-compilers - **Adopt for:** Decision-critical Facts for awesome-tensor-compilers ## Decision facts: onnx-mlir - **Adopt for:** ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments. - **License detail:** Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms. ## Choose when ### Choose awesome-tensor-compilers if… - Tags unique to awesome-tensor-compilers: code generation, deep-learning, high-performance-computing, machine-learning. - If you need references to papers on cost models and automated optimizations for tensor computation. - More GitHub stars (2.8k vs 1.0k) - visibility, not fit. ### Choose onnx-mlir if… - Tags unique to onnx-mlir: llvm, mlir, onnx, runtime environments. - 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 - More recently updated (last pushed Jul 31, 2026). ## When NOT to use awesome-tensor-compilers - Avoid if focused solely on implementation without the need for theoretical background or detailed optimization methods. - Not suitable if your project requires immediate integration of a specific tensor compiler technology rather than review of existing research. ## 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 ## Common questions ### What is the difference between awesome-tensor-compilers and onnx-mlir? awesome-tensor-compilers: A collection of compiler projects and papers for tensor computation and deep learning.. 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 awesome-tensor-compilers over onnx-mlir? Choose awesome-tensor-compilers over onnx-mlir when Tags unique to awesome-tensor-compilers: code generation, deep-learning, high-performance-computing, machine-learning; If you need references to papers on cost models and automated optimizations for tensor computation; More GitHub stars (2.8k vs 1.0k) - visibility, not fit. ### When should I choose onnx-mlir over awesome-tensor-compilers? Choose onnx-mlir over awesome-tensor-compilers when Tags unique to onnx-mlir: llvm, mlir, onnx, runtime environments; 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; More recently updated (last pushed Jul 31, 2026). ### When should I avoid awesome-tensor-compilers? Avoid if focused solely on implementation without the need for theoretical background or detailed optimization methods. Not suitable if your project requires immediate integration of a specific tensor compiler technology rather than review of existing research. ### 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 awesome-tensor-compilers or onnx-mlir more popular on GitHub? awesome-tensor-compilers has more GitHub stars (2,770 vs 1,039). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-tensor-compilers and onnx-mlir open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-tensor-compilers or onnx-mlir? GraphCanon lists graph-backed alternatives at [awesome-tensor-compilers alternatives](/tools/merrymercy-awesome-tensor-compilers/alternatives) and [onnx-mlir alternatives](/tools/onnx-onnx-mlir/alternatives) ([awesome-tensor-compilers markdown twin](/tools/merrymercy-awesome-tensor-compilers/alternatives.md), [onnx-mlir markdown twin](/tools/onnx-onnx-mlir/alternatives.md)), 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](/compare/merrymercy-awesome-tensor-compilers-vs-onnx-onnx-mlir.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-tensor-compilers or onnx-mlir? awesome-tensor-compilers: Dormant. 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 awesome-tensor-compilers and onnx-mlir? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-tensor-compilers trust report](/tools/merrymercy-awesome-tensor-compilers/trust); [onnx-mlir trust report](/tools/onnx-onnx-mlir/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=merrymercy-awesome-tensor-compilers`](/api/graphcanon/graph?tool=merrymercy-awesome-tensor-compilers) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_