Comparison
onnx-mlir vs vllm-mlx
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.
Markdown twin · onnx-mlir alternatives · vllm-mlx alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | onnx-mlir | vllm-mlx |
|---|---|---|
| Maintenance | Very active (3d since push) As of 2w · github_public_v1 | Steady (31d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- vllm-mlx
- Server for LLMs and vision-language models compatible with Apple Silicon
Stars
- onnx-mlir
- 1.0k
- vllm-mlx
- 1.5k
Forks
- onnx-mlir
- 447
- vllm-mlx
- 205
Open issues
- onnx-mlir
- 352
- vllm-mlx
- 86
Language
- onnx-mlir
- C++
- vllm-mlx
- 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.
- vllm-mlx
- 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
- onnx-mlir
- -
- vllm-mlx
- -
Runtime
- onnx-mlir
- -
- vllm-mlx
- -
License
- onnx-mlir
- Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms.
- vllm-mlx
- Apache-2.0
Last pushed
- onnx-mlir
- Jul 31, 2026
- vllm-mlx
- Jun 28, 2026
Categories
- onnx-mlir
- Inference & Serving, Model Training
- vllm-mlx
- Inference & Serving, Model Training
Trust and health
Maintenance
- onnx-mlir
- Very active (96%)
- vllm-mlx
- Steady (60%)
Days since push
- onnx-mlir
- 3d
- vllm-mlx
- 31d
Open issues (now)
- onnx-mlir
- 352
- vllm-mlx
- 86
Owner type
- onnx-mlir
- Organization
- vllm-mlx
- User
OSV dependency advisories
- onnx-mlir
- Published findings
- vllm-mlx
- No lockfile (source not queried)
Full report
- onnx-mlir
- Trust report
- vllm-mlx
- Trust report
Shared compatibility
- Python · onnx-mlir: Python runtime · vllm-mlx: Python runtime
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
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 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 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.
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 Aug 4, 2026
- GitHub forks (onnx/onnx-mlir) · observed Aug 4, 2026
- Last push (onnx/onnx-mlir) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (waybarrios/vllm-mlx) · observed Jul 30, 2026
- GitHub forks (waybarrios/vllm-mlx) · observed Jul 30, 2026
- Last push (waybarrios/vllm-mlx) · observed Jun 28, 2026
- License file (Apache-2.0) · observed Jul 30, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: onnx-mlir 1.0k · vllm-mlx 1.5k (synced Aug 4, 2026).
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 and vllm-mlx alternatives (onnx-mlir markdown twin, vllm-mlx 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 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; vllm-mlx trust report.