Atomic Chat NEW

Atomic Chat is a local offline AI chat for macOS (Apple Silicon), Windows, Linux, iOS and Android.Runs fully on-device so user data is not sent to external servers, supporting private local AI and offline inference.

Supports 1,000+ LLMs including LLaMA, Qwen, Mistral, Gemma and DeepSeek, with GGUF, MLX and ONNX formats and Hugging Face model browsing.Includes TurboQuant optimizations to accelerate attention and reduce KV-cache memory for faster on-device inference (reported up to 8× faster attention and lower memory use).

Built-in agent support enables autonomous workflows, persistent memory across sessions, context switching and extended context windows for multi-step tasks.One-click model downloads and native desktop/mobile apps simplify setup and model management for developers, researchers and privacy-focused users.

Open-source codebase provides transparency for audits, customization and integration with local AI toolchains.

Key Features

• Fully on-device local offline AI chat for macOS (Apple Silicon), Windows, Linux, iOS and Android
• Supports a wide range of LLMs (LLaMA, Qwen, Mistral, Gemma, DeepSeek) with GGUF, MLX and ONNX formats and Hugging Face model browsing
• TurboQuant optimizations to accelerate attention and reduce KV-cache memory for faster on-device inference
• Built-in agent support enabling autonomous workflows, persistent memory, context switching and extended context windows
• One-click model downloads and native desktop and mobile apps for model management and setup

Use Cases

• Create a privacy-first personal research assistant that runs fully offline with Atomic Chat to ingest sensitive documents, use persistent memory to remember context, and leverage TurboQuant-accelerated local LLM inference for fast, on-device search and summarization without sending data to the cloud
• Build an agent-enabled field support app for sales and technical teams on macOS, Windows, Linux and mobile that uses one-click model downloads and low-memory inference to update domain-specific models, recall past customer interactions offline, and automate troubleshooting workflows
• Develop an offline code-review and development assistant for engineers that runs locally, supports 1,000+ LLMs and formats, uses agents to automate testing and refactoring tasks, and preserves code privacy through on-device inference and model management

Who is it for?

• Offline developers
• Privacy advocates
• Automation engineers
• Field workers
• Founders