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For most Home Assistant automations, a cloud LLM is currently the easier and more accurate choice; a local Ollama LLM is the better fit when keeping inference on your own network and avoiding provider dependence matters more than maximum capability. Home Assistant’s 2024 benchmark scored cloud models at 98% and tuned local models at 84%, but those results belong to the models, prompts and release tested at the time. Local deployment also needs a reachable Ollama server, model management and careful limits on exposed entities. Cloud deployment trades those hardware tasks for API credentials, recurring usage charges and sending the conversation context needed by the provider. The practical decision is therefore a balance of tool-call accuracy, latency, privacy, outage behavior, cost, hardware and maintenance—not a universal winner.
Which is better for Home Assistant: a local LLM or a cloud LLM?
Choose a cloud conversation agent if you want the strongest documented command accuracy and the shortest path to a working Assist setup. Choose a local Ollama agent if data locality, operation during an internet outage and control over the inference machine are your priorities, and you are willing to run and maintain another service.
Both approaches use the same Home Assistant Assist framework. The LLM receives only the tools and entities made available through the selected API, so changing providers does not automatically grant broader control. A carefully restricted cloud agent is safer than a local agent with excessive permissions, and a well-tuned local model can be perfectly adequate for a small, predictable set of devices.
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Home Assistant treats the LLM as a conversation agent inside the Assist pipeline. The pipeline has four independently selectable stages, so you can mix deployment types rather than making every voice component local or every component cloud-based.
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The four Assist stages
- Wake word: detects the activation phrase. It may run locally, through an integration or in the cloud.
- Speech-to-text: converts spoken audio into text. Its deployment can be selected independently.
- Conversation agent: interprets the text and decides whether to answer or call Home Assistant tools. This is where the local-versus-cloud LLM choice applies.
- Text-to-speech: turns the response back into audio and can also be local, integration-provided or cloud-based.
This separation lets you, for example, keep wake-word detection and speech processing local while using a cloud conversation agent, or use a local Ollama agent with a cloud speech service. Test each stage separately when diagnosing delays or failures.
What the LLM can and cannot control
The Assist API exposes intents for reading and controlling the entities you deliberately expose. That covers normal device actions and information requests such as checking a light’s state or changing a thermostat setting. It does not provide administrative access.
The separate Home Assistant API is intended for authorized administrators and can manage registries, system logs, automations and configuration entries. Calling this broad administrative interface “LLM control” would overstate what a normal Assist agent can do. Keep administrative API access out of an everyday conversation agent unless you have a narrowly designed, audited reason to use it.
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What does a local Ollama setup require?
The documented local path is the Ollama integration. Ollama runs the selected model on an external server, which can be another computer on your home network. Home Assistant itself remains local, but the server must be reachable whenever the conversation agent is used.
Local setup steps
- Run Ollama on a suitable host. Install the Ollama server on hardware that can hold the model and its context in memory. The exact host, model size, quantization and context length determine whether responses are interactive; the official guidance does not prescribe one universal computer specification.
- Make the server reachable. Confirm that Home Assistant can connect to the Ollama server URL over your network. If you use an API key, keep it in the integration’s credential fields rather than in an automation message.
- Add the Ollama conversation integration. Enter the server URL, optional API key and model name. During setup, Home Assistant downloads the selected model through Ollama.
- Set the model behavior. Configure the instruction text, context-window size, conversation-history limit and model keep-alive behavior. A larger context can preserve more history but consumes more memory and may increase response time.
- Decide whether control is enabled. The integration lets you allow the model to control Home Assistant. Start with control disabled while validating ordinary conversation, then enable it only after restricting the exposed entities.
- Expose a small test set. Home Assistant recommends fewer than 25 entities for local experimentation. Begin with devices that have clear names and low consequences, such as a lamp, a fan or a read-only sensor.
- Select the agent in Assist. In the voice assistant configuration, choose the Ollama conversation agent for the pipeline you want to test, then issue both read and control requests.
Why model capability matters locally
Only models that support tool calling can operate exposed Home Assistant entities. Smaller models are more likely to misunderstand a request, select the wrong entity or fail to complete a conversation reliably. A model that produces fluent prose but lacks dependable tool support is not a suitable device-control agent.
A practical pattern is to create two Ollama configurations: one for general conversation with no device control, and a second with a tightly limited entity list for actions. This limits the impact of a mistaken tool call while preserving a useful local assistant for questions.
How do I set up a cloud LLM for Home Assistant?
Home Assistant’s conversation-agent framework supports cloud providers including OpenAI and Google Generative AI. The provider handles inference on its service, while Home Assistant supplies the conversation context and the tools associated with the entities you expose.
Cloud setup steps
- Create the provider account and credentials. Obtain the API credentials required by the chosen integration. Keep account keys private and apply any spending or usage limits offered by the provider.
- Install the provider integration. Add the OpenAI or Google Generative AI integration from Home Assistant’s integrations interface and enter the requested credentials.
- Select a model and conversation agent. Model capability, context handling and provider pricing vary, so record the model name and date when evaluating results.
- Limit exposed entities. Cloud inference does not remove least-privilege requirements. Expose only the devices and sensors that the agent needs for the intended tasks.
- Assign the agent to an Assist voice assistant. Choose the cloud conversation agent in the Assist pipeline, leaving other stages local or integration-based if desired.
- Test representative commands. Include ambiguous names, multi-step requests, status questions and a refusal case for an entity that is not exposed. Confirm that the agent declines the last request instead of trying an administrative workaround.
Cloud setup is usually quicker because you do not provision inference hardware or download models. It does, however, depend on valid credentials, a working internet path and the provider’s availability.
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Are cloud LLMs more accurate for Home Assistant commands?
Home Assistant’s published 2024.8 release notes reported a 98% benchmark score for cloud-based models and 84% for tuned local models. The test used particular models, prompts and a particular release, so the figures are historical indicators rather than permanent rankings for every current model.
| Evidence or factor | Cloud LLM | Local Ollama LLM |
|---|---|---|
| Home Assistant benchmark reported in 2024.8 release notes | 98% | 84% after prompt tuning |
| Project’s 2024 overall assessment | Described as more powerful and easier to run at that time | Open-source options required more setup and were less capable in that comparison |
| Tool support | Must support the Home Assistant tools exposed by the integration | Must support tools; smaller models are more error-prone |
| Entity guidance for experimentation | Still requires least-privilege exposure | Home Assistant recommends fewer than 25 exposed entities |
The project also recommended Llama 3.1 8B for local experimentation in that release. Treat that recommendation as a dated starting point, not a guarantee that the same model remains the best choice after model and integration updates.
Accuracy depends on more than the provider label. Entity names, room assignments, prompt instructions, model quantization, context length and the complexity of the requested action can all change the result. Measure the exact model and prompt you intend to run instead of assuming that a new model inherits the benchmark score.
Is a local LLM faster or more reliable during an outage?
There is no universal latency number in the published Home Assistant material. Local response time depends on the host’s processor or GPU, model size, quantization, context length and whether the model remains resident in memory. Cloud response time depends on your network path and provider load.
A local agent can continue inferring when the provider or internet connection is unavailable, provided the Ollama server and the rest of the Assist pipeline are reachable on your network. A cloud agent cannot complete a new provider request during an internet outage or provider interruption. Local hardware can still fail, overheat, run out of memory or be powered down, so “offline” does not mean “failure-proof.”
How to compare speed fairly
- Use the same Assist voice stages for both tests, or measure the conversation stage separately with typed text.
- Keep the entity list, instructions and conversation-history limit identical.
- Run the same set of short commands, status questions and multi-step requests.
- Record time to first response and time to completed tool action, not just the time until text appears.
- Repeat tests when the local model is already resident and after a cold start; compare those states separately.
This produces a useful result for your hardware and network without inventing a platform-wide latency claim.
Which option is more private?
Home Assistant’s privacy FAQ says the platform runs on your own hardware and stores data locally unless you enable optional services. That statement describes the core platform, not the destination of every optional integration.
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With Ollama configured on a local server, inference can remain on the local network. Data may still leave your network through other Assist stages, remote logging, backups or integrations, so audit the whole pipeline rather than treating the LLM choice as the only privacy setting.
How do cost and energy differ?
Cloud shifts spending toward usage fees. You avoid buying and maintaining an inference machine, but each request is subject to the provider’s current pricing and account policies. Home Assistant’s 2024 comparison reported that its recommended Google model was 14 times cheaper than its recommended OpenAI model at that time; that was a model-specific, dated comparison, not a permanent price ratio.
Local shifts spending toward hardware, electricity, model storage, updates and maintenance. If you already own a capable computer, the marginal cost per request can be low, but sustained inference still consumes power and may keep a GPU or CPU active. The official material does not publish a universal dollar break-even point because request volume, model choice, existing hardware and electricity prices vary.
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For a useful estimate, count your typical daily requests, identify the exact cloud model and price sheet you would use, and measure the local machine’s power draw under the workload. Recalculate after changing models or context limits.
How should I limit the safety risk of LLM device control?
Least privilege is the most important control for either deployment. Give the agent only the entities required for the tasks it performs, use unambiguous names and separate read-only sensors from devices that can unlock, heat or otherwise create consequences.
Safer exposure practices
- Start below Home Assistant’s recommendation of 25 entities for local experiments, then add devices one at a time.
- Keep doors, locks, alarms and other high-impact devices out of a general-purpose agent unless you have a specific, tested workflow.
- Use a separate restricted conversation configuration for actions and a non-controlling configuration for general questions.
- Check the Assist transcript and Home Assistant logs after testing unexpected commands.
- Do not grant the Assist agent administrative Home Assistant API credentials merely to make a device command work.
- Use explicit automation confirmations for actions where a mistaken interpretation could cause harm or expense.
Cloud access does not bypass these controls, and local inference does not make unsafe exposure harmless. A local model can make mistakes while remaining entirely inside your network.
What hardware is needed for a local Home Assistant LLM?
The evidence supports categories rather than one required specification: a mini PC, a GPU-equipped computer or a Jetson-class device can host an Ollama server, provided it has enough memory for the selected model and context. Model size, quantization and context window determine the practical requirement.
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Before choosing hardware, verify the current model’s memory needs, whether your operating system and accelerator are supported, expected thermals and noise, and whether the machine can stay available when Home Assistant needs it. A smaller model may fit existing hardware but lose tool-call reliability; a larger model may improve responses while increasing startup time, power use and maintenance.
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When does a hybrid local-and-cloud design make sense?
A hybrid design is useful when different requests have different privacy or capability requirements. Keep routine device control on a local, restricted Ollama agent and route occasional complex questions to a cloud agent, or keep all voice processing local while using a cloud conversation model for better command interpretation.
Make the routing rule explicit in Home Assistant rather than asking one unrestricted model to decide where sensitive data should go. Document which agent receives which entities, and ensure the cloud path never receives devices that the user intends to keep local.
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Use the following decision sequence before committing to one deployment.
- Define the failure you can tolerate. If an internet outage must not stop basic commands, include a local conversation path. If occasional unavailability is acceptable, cloud simplicity may dominate.
- List sensitive context. Identify entity names, occupancy information, cameras, alarms and other data that should not leave the network. Exclude those entities from cloud agents or keep the relevant workflow local.
- Set an accuracy threshold. Test the commands that matter, including ambiguous phrasing and multi-device actions. Prefer the model that completes them correctly with the fewest unsafe interpretations.
- Calculate total cost. Compare provider usage for your request volume with local hardware, electricity, storage and maintenance. Use current provider pricing, not the dated 14-times comparison as a forecast.
- Measure operational effort. Cloud requires credential and integration maintenance; local adds server uptime, model downloads, updates, backups and network troubleshooting.
- Start with a narrow scope. Expose a small set of low-consequence entities, observe transcripts and logs, then expand only when behavior is predictable.
For many households, the result is cloud for broad conversational quality and local services for privacy-sensitive or outage-critical functions. Others will reasonably choose an entirely local stack because data locality outweighs the extra administration.
What commonly goes wrong, and how can I fix it?
The model answers but does not control a device
Check that the selected model supports tools, that control is enabled in the integration and that the target entity is exposed to that conversation agent. A model that lacks tool support can still produce convincing text while being unable to call Home Assistant.
The local agent is slow or stops mid-conversation
Reduce the context window and conversation-history limit, use a smaller or better-quantized model, and verify that the model remains resident when repeated requests are expected. Also check host memory pressure and whether another workload is competing for the accelerator.
The agent selects the wrong device
Rename entities and areas so they are unambiguous, remove similarly named devices from the exposed set and add clearer instructions. Test one room or device family at a time before expanding the list.
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Validate the API credential, account limits, network path and provider status. Confirm that the Assist pipeline is actually selecting the intended cloud agent rather than a stale or unavailable configuration.
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A command exposes more information than intended
Review the entities and conversation history available to that agent, then remove sensitive devices and recreate the agent with narrower scope. Administrative Home Assistant API access should be revoked unless it is explicitly required and protected.
What is the practical verdict?
Use a cloud LLM when documented accuracy, broad capability and easy deployment are your top priorities. Use Ollama locally when keeping inference on your hardware, surviving provider outages and reducing recurring usage dependence justify the server and model-management work. Whichever path you choose, the quality and safety of the result depend heavily on tool-capable models, clear entity naming, limited exposure and tests that match the commands your household actually uses.
Frequently Asked Questions
Can a local Home Assistant LLM work with no internet connection?
Yes, an Ollama conversation agent can continue inference on a reachable local server when the internet or a cloud provider is unavailable. The rest of the Assist pipeline may still depend on cloud speech or text-to-speech services, so verify each stage independently.
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No. The normal Assist API provides intents for reading and controlling exposed entities, while registry, log, automation and configuration management belongs to the separate administrative Home Assistant API. Do not provide administrative credentials just to enable ordinary device commands.
How can I test whether a model is reliable enough for automations?
Create a fixed set of representative requests, including ambiguous names, multi-device actions, status questions and a request for a non-exposed device. Record successful tool calls, wrong-entity selections, refusals and completion time across repeated runs instead of judging from a single impressive answer.
Can I use local speech recognition with a cloud conversation model?
Yes. Assist lets wake word, speech-to-text, conversation agent and text-to-speech stages be selected independently. This allows audio processing to stay local while only the text conversation context is sent to a cloud provider.
Why does a smaller local model sometimes sound fluent but fail automations?
Fluent text generation does not guarantee tool-calling support or reliable argument selection. Device control requires a model that can invoke the exposed Home Assistant tools and maintain the conversation state needed to complete the action.
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