--- title: "onnx-mlir vs vllm-mlx" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/onnx-onnx-mlir-vs-waybarrios-vllm-mlx" tools: ["onnx-onnx-mlir", "waybarrios-vllm-mlx"] --- # onnx-mlir vs vllm-mlx *GraphCanon updated Aug 4, 2026* ## 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 vllm-mlx if vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend. [onnx-mlir](https://github.com/onnx/onnx-mlir) reports 1.0k GitHub stars, 447 forks, and 352 open issues, last pushed Jul 31, 2026. [vllm-mlx](https://github.com/waybarrios/vllm-mlx) has 1.5k stars, 205 forks, and 86 open issues, last pushed Jun 28, 2026. Figures are from public GitHub metadata via [onnx-mlir's repository](https://github.com/onnx/onnx-mlir) and [vllm-mlx's repository](https://github.com/waybarrios/vllm-mlx). | | [onnx-mlir](/tools/onnx-onnx-mlir.md) | [vllm-mlx](/tools/waybarrios-vllm-mlx.md) | | --- | --- | --- | | Tagline | ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes | Server for LLMs and vision-language models compatible with Apple Silicon | | Stars | 1,039 | 1,472 | | Forks | 447 | 205 | | Open issues | 352 | 86 | | Language | C++ | Python | | Adopt for | ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments. | vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend. | | Persona | - | - | | Runtime | - | - | | License | Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms. | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [onnx-mlir](/tools/onnx-onnx-mlir.md) | [vllm-mlx](/tools/waybarrios-vllm-mlx.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 3d | 31d | | Open issues (now) | 352 | 86 | | Owner type | Organization | User | | Full report | [trust report](/tools/onnx-onnx-mlir/trust.md) | [trust report](/tools/waybarrios-vllm-mlx/trust.md) | ## Shared compatibility - **Python**: [onnx-mlir](/tools/onnx-onnx-mlir.md) - Python runtime; [vllm-mlx](/tools/waybarrios-vllm-mlx.md) - Python runtime ## 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. ## Decision facts: vllm-mlx - **Adopt for:** vllm-mlx is an open-source inference server that runs large language models and vision-language models on Apple Silicon devices with continuous batching and multimodal support using native MLX backend. ## Choose when ### Choose onnx-mlir if… - onnx-mlir is primarily C++; vllm-mlx 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 ### Choose vllm-mlx if… - vllm-mlx is primarily Python; onnx-mlir is C++. - Tags unique to vllm-mlx: anthropic, apple-silicon, audio-processing, claude-code. - If you need to run LLMs or vision-language models like Llama, Qwen-VL, and LLaVA efficiently on Apple Silicon devices. ## 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 ## When NOT to use vllm-mlx - If your target environment is not an Apple device equipped with the required hardware to run models via MLX backend. - When seeking a solution that offers high-speed token throughput beyond 400 tok/s as vllm-mlx may not be adequate for such performance needs. ## Common questions ### What is the difference between onnx-mlir and vllm-mlx? onnx-mlir: ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes. vllm-mlx: Server for LLMs and vision-language models compatible with Apple Silicon. See the comparison table for live GitHub stats and shared categories. ### When should I choose onnx-mlir over vllm-mlx? Choose onnx-mlir over vllm-mlx when onnx-mlir is primarily C++; vllm-mlx 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 choose vllm-mlx over onnx-mlir? Choose vllm-mlx over onnx-mlir when vllm-mlx is primarily Python; onnx-mlir is C++; Tags unique to vllm-mlx: anthropic, apple-silicon, audio-processing, claude-code; If you need to run LLMs or vision-language models like Llama, Qwen-VL, and LLaVA efficiently on Apple Silicon devices. ### 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 vllm-mlx? If your target environment is not an Apple device equipped with the required hardware to run models via MLX backend. When seeking a solution that offers high-speed token throughput beyond 400 tok/s as vllm-mlx may not be adequate for such performance needs. ### Is onnx-mlir or vllm-mlx more popular on GitHub? vllm-mlx has more GitHub stars (1,472 vs 1,039). Stars measure visibility, not whether either tool fits your constraints. ### Are onnx-mlir and vllm-mlx open source? Yes - both are open-source projects on GitHub (onnx-mlir: Apache-2.0, vllm-mlx: Apache-2.0). ### Where can I find alternatives to onnx-mlir or vllm-mlx? GraphCanon lists graph-backed alternatives at [onnx-mlir alternatives](/tools/onnx-onnx-mlir/alternatives) and [vllm-mlx alternatives](/tools/waybarrios-vllm-mlx/alternatives) ([onnx-mlir markdown twin](/tools/onnx-onnx-mlir/alternatives.md), [vllm-mlx markdown twin](/tools/waybarrios-vllm-mlx/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/onnx-onnx-mlir-vs-waybarrios-vllm-mlx.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, onnx-mlir or vllm-mlx? onnx-mlir: Very active. vllm-mlx: Steady. 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 vllm-mlx? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [onnx-mlir trust report](/tools/onnx-onnx-mlir/trust); [vllm-mlx trust report](/tools/waybarrios-vllm-mlx/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=onnx-onnx-mlir`](/api/graphcanon/graph?tool=onnx-onnx-mlir) - 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/_