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Ollama vs Osaurus
Side-by-side comparison for macOS
Ollama
8.0Get up and running with large language models locally
Osaurus
7.0LLM server built on MLX
| Metric | Ollama | Osaurus |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| AI Score | 8.0 | 7.0 |
| 30-day Installs | 11.9K | 616 |
| 90-day Installs | 27.3K | 2.3K |
| 365-day Installs | 55.7K | 4.5K |
| Version | 0.23.1 | 0.18.9 |
| Auto-updates | Yes | No |
| Deprecated | No | No |
| GitHub Stars | 164.8K | 4.0K |
| GitHub Forks | 14.9K | 163 |
| Open Issues | 2.6K | 30 |
| License | MIT | MIT |
| Language | Go | Swift |
| Last GitHub Commit | 1mo ago | 1mo ago |
| First Seen | Dec 18, 2023 | Sep 19, 2025 |
Reviews
Ollama
Ollama enables users to run large language models locally, offering a powerful tool for developers and data scientists. It supports various models and hardware, including AMD GPUs, making it versatile for different computing needs.
Runs large language models locally on your machine.
Pros
- + Enables local running of large language models for privacy and bandwidth efficiency.
- + Supports multiple models and hardware, including AMD GPUs, broadening its accessibility.
- + Active development and strong community support enhance reliability and future potential.
Cons
- - Niche appeal, primarily targeting developers and data scientists familiar with local AI setups.
- - Setup and management of models may be complex for less technical users.
Osaurus
Osaurus is a local LLM server for macOS, enabling users to run and manage AI models locally or in the cloud. It supports Apple's foundation models and integrates with tools like Ollama, making it ideal for developers and AI enthusiasts.
Osaurus provides a runtime environment for AI models, allowing users to run and share models locally or via the cloud.
Pros
- + Compatible with Apple's foundation models and Ollama
- + Supports both local and cloud-based model execution
- + Built using Swift for native macOS integration
Cons
- - Lacks auto-update functionality
- - Past issues with performance and memory usage