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gaia

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Basic Information

GAIA is an open-source solution from AMD for rapidly setting up and running generative AI applications and LLM-based agents on local Windows PC hardware. It is designed to enable local inference and agent execution, with a particular focus on Ryzen AI systems that combine an AMD Neural Processing Unit (NPU) and integrated GPU (iGPU) for hybrid acceleration. The project provides an installer that configures a CLI and GUI, an optional web UI called GAIA UI (RAUX), three installation modes (Hybrid, NPU, and Generic), and tooling to download and run models locally without cloud dependencies. The README also describes developer workflows and building from source, including integration with Onnx Runtime GenAI and the Lemonade Server, and notes system and driver requirements and limitations such as Windows 11 support.

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App URL
https://github.com/amd/gaia
Github Repository
https://github.com/amd/gaia/blob/main/README.md

App Details

Features
Local LLM processing on Windows using hybrid NPU+iGPU acceleration for Ryzen AI or a generic Ollama backend for other PCs. Multiple use cases including chat, retrieval-augmented generation (RAG), and specialized agents, with an extensible architecture that lets contributors add new agents via the agents folder. Both command-line and graphical interfaces are provided, plus an optional modern web UI (RAUX/GAIA UI) in beta. A unified installer supports Hybrid, NPU (coming soon), and Generic modes and will set up dependencies like Python 3.10, Miniconda, FFmpeg and Ollama where applicable. The project documents system requirements, driver recommendations, troubleshooting steps, model download/token handling, and developer build instructions for source builds.
Use Cases
GAIA enables users to run powerful language models locally without relying on cloud services, reducing latency and preserving data privacy while leveraging on-device acceleration on Ryzen AI hardware. For end users it offers an easy installer, desktop GUI and CLI for quick experimentation and model downloads handled automatically. For developers it provides an extensible framework to build and integrate agents, example agents to use as starting points, and build-from-source guidance including GenAI runtime integration. The project also supplies troubleshooting guidance, driver recommendations and manual uninstallation steps, making it practical for both hobbyists and developers who want to prototype or deploy local LLM applications on Windows.

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