Locally AI
FreeDiscover LocallyAI, a platform that lets you run AI models on your device with full privacy, offline access, and no cloud dependency.
About Locally AI
Locally AI is a platform designed for Apple devices—iPhones, iPads, and Macs—that enables users to run AI models directly on their device without any cloud dependency. By keeping all processing local, it offers complete privacy, offline functionality, and eliminates the need for an internet connection or login. The platform is optimized for Apple Silicon, leveraging the capabilities of M-series chips for efficient performance. It supports a variety of open-source models including Llama, Gemma, Qwen, DeepSeek, SmolLM, Granite, Cogito, and LFM, all running completely offline. In addition to text-based language models, Locally AI also offers local voice mode for real-time, on-device voice conversations and vision models for image analysis. The app is built natively for Apple platforms, integrating with the latest OS features like iOS 26 Liquid Glass and Apple Foundation model support. As of the latest announcement, Locally AI is joining LM Studio, which may influence future development and updates. The platform is currently in public beta, with new models like Qwen 3 4B and Qwen 3 4B Thinking regularly added.
Key Features
Pros & Cons
- Complete privacy; data never leaves the device
- Fully functional offline without internet or login
- Optimized for Apple Silicon, offering good performance on modern Apple devices
- Supports a wide range of open-source models with regular updates
- Native Apple app designed for seamless user experience
- Currently limited to Apple devices (iPhone, iPad, Mac) with Apple Silicon; older Intel Macs may not be supported
- Free tier appears to be in public beta, with features and availability subject to change; exact pricing after beta should be verified
- Model size and performance are constrained by device memory and storage
- The platform is joining LM Studio, which may affect future support and standalone functionality
- Some advanced models may require considerable device resources, potentially limiting use on older devices
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