A native desktop application for running large language models locally with Rust and Tauri, offering private AI chat without internet or cloud services.
Oxide Lab is a native desktop application built with Rust and Tauri v2 that allows users to run large language models locally on their computer. It provides a private AI chat interface without requiring internet connectivity, cloud services, or data sharing, solving the need for secure and independent AI interactions.
Developers and users who prioritize data privacy and want to run AI models offline, including those with NVIDIA GPUs, Apple Silicon, or standard CPU setups seeking local inference capabilities.
Developers choose Oxide Lab for its complete local processing, ensuring no data leaves the device, combined with support for multiple model architectures and hardware acceleration options for optimized performance.
Modern desktop application (Rust + Tauri v2 + Svelte 5 + Candle (HF)) for communicating with AI models that runs completely locally on your computer. No subscriptions, no data sent to the internet — just you and your personal AI assistant
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All AI inference occurs locally with no data sent to the cloud, as emphasized in the privacy section, ensuring no telemetry or external data sharing.
Supports multiple LLM architectures like Llama, Qwen, Mistral, and formats such as GGUF and SafeTensors, allowing flexibility in model selection from sources like Hugging Face.
Leverages CPU, CUDA for NVIDIA GPUs, Metal for Apple Silicon, and other optimizations to speed up inference, as detailed in the system requirements.
Features streaming text generation for immediate chat responses, enhancing interactivity without waiting for full model output.
Requires installing Node.js, Rust toolchain, and optional CUDA or Metal tools, which can be daunting for non-developers or those unfamiliar with system dependencies.
Inference speed is tied to local hardware; without a capable GPU or sufficient RAM, users may experience slow responses, limiting accessibility on lower-end machines.
Users must independently download, update, and load models without built-in model discovery or automated updates, adding overhead compared to cloud-based solutions.