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You can connect a local LLM to Home Assistant by running Ollama on a separate, network-reachable server and adding Home Assistant’s Ollama integration as a conversation agent. To let the model control devices, enable control and expose only the entities you intend it to use. Your GPU is part of the Ollama host—not a requirement supplied by Home Assistant—and its suitability depends on the model and configuration.
How the local LLM and Home Assistant fit together
The setup has three distinct parts: Ollama runs the model, Home Assistant provides the conversation agent and smart-home context, and your chosen interface—such as Assist—lets you interact with it. Home Assistant’s built-in Ollama integration connects to an external Ollama server; it does not host the model itself. That server must be reachable over the network. Home Assistant’s Ollama integration documentation describes the server connection and configuration.
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When device control is enabled, the model can use Home Assistant tools to interact with the entities made available to it. The Home Assistant LLM integration provides the framework for those tools and is set up automatically when needed. In the developer documentation, tools are operations the LLM can call; their arguments are checked against a parameter schema, and annotations describe behavior such as whether an operation is read-only or destructive. That boundary helps define what a tool is allowed to do, but it does not make a model’s interpretation or every device action infallible. See the LLM API developer documentation.
Connect Ollama to Home Assistant
- Run Ollama separately. Install and run Ollama on a machine that can be reached from Home Assistant. Download a model in Ollama that supports tool calling if you want device control. Home Assistant’s integration page does not prescribe a GPU, model, quantization, or minimum VRAM.
- Add the integration. In Home Assistant, open Settings > Devices & services, select Add integration, and search for Ollama. Follow the integration’s prompts to enter the Ollama server URL and select a model. The integration page also lists optional API key and conversation settings. Check the current Ollama integration instructions for the available fields.
- Choose whether to allow control. If you only want responses and information, leave Home Assistant control disabled. If you want device actions, enable control and select the intended entities for exposure. Home Assistant says: “Only models that support Tools may control Home Assistant.”
- Try a limited request. Start with a simple question about an exposed entity, then test one low-risk action. Confirm the model’s interpretation and the resulting state in Home Assistant before expanding the exposed set or relying on the setup for routine actions.
Limit which devices the model can reach
Exposure is the practical boundary between a conversational model and your smart-home controls. Make the exposed set small and deliberate: a model cannot operate an entity that Home Assistant has not made available to it through this integration. Home Assistant recommends exposing fewer than 25 entities when experimenting with local LLMs. That is official guidance, not a guarantee that any particular set of entities or model is safe.
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- Begin with low-risk entities such as a lamp or a noncritical room setting.
- Keep locks, alarms, garage doors, heaters, and other consequential controls out of scope unless you have carefully considered the risks and verified the behavior.
- Use prompt instructions to clarify names and expected behavior, but do not treat instructions as a substitute for limiting entity access.
- Check the Home Assistant state after an action; natural-language confirmation alone is not proof the device changed as intended.
Smaller models may make mistakes and may not reliably maintain a conversation when control is enabled, according to Home Assistant’s integration guidance. Tool support is necessary for control, but it does not ensure that a model will choose the correct tool or interpret an ambiguous request correctly. Review the integration’s model and control cautions.
What the GPU does—and what the documentation does not establish
The GPU accelerates inference only if the Ollama host, drivers, runtime, and model configuration actually use it. Home Assistant’s integration documentation establishes a connection to an external Ollama server; it does not specify which GPU to use, a minimum VRAM amount, or a performance target. The title’s “old GPU” is therefore a particular build detail, not a universal hardware recipe.
Whether a specific card can run a model acceptably depends on the model and quantization, its memory use, the context window, other workloads sharing the machine, and the time required for the full request. Without the GPU model, host setup, Ollama version, model details, and measured timings, there is no grounded speed or compatibility claim to make. Use the actual server’s behavior with your chosen model to decide whether the hardware is adequate.
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Configure Assist and voice separately
The Ollama integration provides a conversation agent; it does not itself supply microphones, speakers, speech recognition, or spoken output. You can interact through text or route Assist to a local or cloud conversation agent. If using voice, configure speech-to-text and text-to-speech separately in Home Assistant’s voice pipeline. The Assist guide explains how agents and voice configuration fit together.
Assist can handle recognized commands before passing less direct or open-ended requests to an AI agent, which Home Assistant says can be faster than sending every intent through an LLM. Home Assistant’s 2025 discussion also describes sharing context between Assist and an AI agent, and streaming text-to-speech to reduce perceived wait time. These behaviors depend on the Home Assistant version, selected agent, audio stack, and configuration; they should not be assumed to appear automatically in every Ollama setup. See Home Assistant’s 2025 voice chapter article.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Local Ollama or a cloud conversation agent?
A local model keeps inference on hardware you manage, but it brings setup and maintenance responsibilities and may offer different capabilities from a cloud agent. The trade-off depends on the model, connection, and service in question; there is no universal winner.
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| Consideration | Local Ollama | Cloud agent |
|---|---|---|
| Data routing | Inference runs on your Ollama host; Home Assistant still communicates with that server over your network. | Requests are handled by the chosen provider’s service; review its data handling and account settings. |
| Ongoing cost | No cloud-model subscription is inherent to the local path, but you supply and maintain the hardware. | Cost depends on the chosen service and its current terms; no universal current price applies. |
| Model capability | Depends on the locally available model and its tool-calling support. | Depends on the provider and model selected. Home Assistant’s 2024 comparison described cloud models as more capable at that time, not as a current benchmark. |
| Latency | Depends on your host, model, network, and configuration; no universal speed figure is established. | Depends on provider response time and your internet connection. |
| Setup and maintenance | You manage the Ollama host, model downloads, network access, and Home Assistant integration. | You configure the provider’s integration and account; the provider manages model hosting. |
| Internet reliance | Model inference can run locally, though remote access or other integrations may still need internet connectivity. | Cloud inference requires the service and an internet connection. |
Home Assistant’s 2024 article on LLMs provides historical architectural context. Its characterization of cloud and local models reflects that publication period, not today’s model rankings, pricing, or a measured comparison of particular systems.
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home-llm is a third-party Home Assistant project describing a custom integration and model for local smart-home control. Its repository history mentions multiple backends, tool calling, voice streaming, and fixes for Home Assistant 2026.9. It is optional community software, not a prerequisite for the built-in Ollama integration; check its current compatibility and installation guidance before choosing it.
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