AMD has released GAIA, an open-source project allowing developers to run large language models (LLMs) locally on Windows machines with AMD hardware acceleration.
The framework supports retrieval-augmented generation (RAG) and includes tools for indexing local data sources. GAIA is designed to offer an alternative to LLMs hosted on a cloud service provider (CSP).
Because GAIA runs entirely on-device, it is especially appealing in latency-sensitive or disconnected environments such as developer workflows, privacy-focused applications and field-deployed devices.
GAIA’s improved data-sovereignty protections keeps sensitive or proprietary data on the user’s machine, avoiding transmission over external networks. Inference occurs locally, reducing latency compared to round-trips to remote APIs.
GAIA is designed to be accessible for developers with minimal setup, offering a local Open-AI compatible API that can run entirely on consumer-grade hardware. It includes a simple prompt interface, a general purpose chat (“Chaty”), a video search assistant that can parse YouTube transcripts, and a generative personality agent called “Joker.” The backend that serves these agents is powered by the Lemonade SDK, which leverages the ONNX runtime and AMD’s TurnkeyML infrastructure. Agents interact with a local vector store populated through a document ingestion and embedding pipeline. External data is parsed, vectorized into dense embeddings, and made searchable via a similarity query engine.
AMD GAIA Overview Diagram – Source: https://www.amd.com/en/developer/resources/technical-articles/gaia-an-open-source-project-from-amd-for-running-local-llms-on-ryzen-ai.html
The core architectural approach revolves around RAG, a pattern that enhances model responses by incorporating externally indexed documents into the prompt. GAIA provides tooling to index a variety of content sources (markdown files, transcripts, GitHub repositories) and vectorizes them using a local embedding model. These embeddings are stored and queried at runtime to provide contextually relevant completions.
GAIA is offered in two variants: a standard Windows installer and a hybrid, hardware-accelerated version optimized for AMD Ryzen systems equipped with integrated GPUs and neural processing units (NPUs). While the toolset is platform-agnostic at the source level, AMD states that the hybrid path is where future optimization efforts will be focused, particularly for devices with Ryzen AI support. AMD wants to push model execution onto its dedicated neural hardware to reduce CPU load and power consumption.
By positioning GAIA as a thick-client alternative to cloud-based LLMs, AMD competes with other local-first tooling aimed at developers, hobbyists and edge-computing scenarios. Similar efforts such as ChatRTX, LM Studio and Ollama are part of a broader architectural trend of moving inference closer to model owners, reducing risks such as privacy, API rate limiting and vendor lock-in often associated with the use of cloud-managed services – a direction AMD explicitly acknowledges in its GAIA announcement.
The source code is available on GitHub under the MIT license, and includes Docker-based deployment options, preset model configurations and support for running on CPUs, GPUs and NPUs. Although the project is in its initial releases, it reflects AMD’s growing ambition to the AI developer ecosystem not only through their silicon, but also via open tooling that supports real-world application workflows.
