vMLX vs Swama
Side-by-side comparison for macOS
vMLX
7.5Run local AI models on Apple Silicon
Swama
8.0Machine-learning runtime
| Metric | vMLX | Swama |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| AI Score | 7.5 | 8.0 |
| 30-day Installs | 82 | 6 |
| 90-day Installs | 314 | 23 |
| 365-day Installs | 666 | 449 |
| Version | 1.6.80 | 2.5.1 |
| Auto-updates | Yes | No |
| Deprecated | No | No |
| GitHub Stars | 567 | 506 |
| GitHub Forks | 66 | 24 |
| Open Issues | 36 | 31 |
| License | Apache-2.0 | MIT |
| Language | Python | Swift |
| Last GitHub Commit | 4mo ago | 7mo ago |
| First Seen | Jun 1, 2026 | Jun 23, 2025 |
Reviews
vMLX
vMLX is a tool for running local AI models optimized for Apple Silicon, offering features like disk caching and scheduling. It benefits developers and data scientists working on machine learning projects.
vMLX enables running local AI models on Apple Silicon-based Macs.
Pros
- + Optimized for Apple Silicon for better performance
- + Supports local AI model execution
- + Active development with recent updates
- + Open-source under Apache-2.0 license
- + Features like disk caching enhance reliability
Cons
- - Low recent installs indicating niche adoption
- - Some functionalities are requested but not yet implemented
Swama
Swama is a high-performance machine learning runtime for macOS, offering efficient inference for large language models with a native Swift implementation. It benefits developers and ML enthusiasts by providing a fast and scalable solution for model execution.
Swama provides a runtime environment for executing machine learning models, particularly large language models, on macOS systems using Swift.
Pros
- + High-performance inference engine for macOS
- + Native Swift implementation for seamless integration
- + Active development and updates
Cons
- - No auto-update feature
- - Limited model support as per current issues
- - Some open issues indicating ongoing development needs