You do not need to start with the largest model to make local AI useful. On a phone with limited memory, a smaller model and a focused question can be a better first step than a download that the phone cannot load.
OGAM (Off Grid AI Mobile) lets you choose smaller local models, inspect the model already loaded, and unload it when you need room for another job. Once the required model is downloaded, the supported task can run on the phone without internet.
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This guide keeps the work on your phone. A remote model is another option, but it does not solve the same offline, on-device task.
Storage space is not working memory
A model file uses storage while downloaded. Loading it also needs RAM for its weights, conversation context, and working data. Having room for the file does not mean the model will run comfortably.
The operating system and other active work use memory too. On iPhone, the app’s usable memory can be lower than the device’s total RAM. On Android, the amount currently available also changes as the phone works.
Use the app’s compatibility and estimated RAM information as a starting point. An estimate helps you choose; it is not a guarantee that every session will load successfully.
Start with a small, useful job
Choose one task before choosing the model. A short rewrite, a small checklist, or a few ideas gives you a clear first result without a long document or conversation.
For example:
Rewrite this sentence more clearly without adding facts: “We need to check the draft before we can decide when to send it.”
Check that the model preserves the meaning. A model that handles that task can be useful even if it is not the largest model in the catalog.
If you are setting up from scratch, Settings → Storage → Auto Setup offers a Lean plan with smaller downloads and lower memory use. Review the included models and total size before downloading. The guided selection is included in OGAM 0.0.111.
Free memory without deleting a downloaded model
Open the model selector in chat. Its Currently Loaded section identifies a model that is actually in memory. Use Unload when you are done with it, then select the local model needed for the next task.
Unloading and deleting are different operations. Unload releases the running model; it does not require you to download its file again for the next use. A selected model can also be waiting to load, so selection alone does not mean it is already using all its runtime memory.
Avoid keeping unrelated model work active when you are trying to load a larger task. For example, finish an image job before testing a different chat model.
Keep the conversation within the phone’s limits
For GGUF text models, review Settings → Model Settings and the text-generation controls. Context Length affects how much material the model can hold in its active conversation context. A smaller context can reduce memory demand, but it also limits how much history and input the model can use.
LiteRT models have separate controls; this Context Length setting belongs to the GGUF text-model workflow.
Use a fresh chat for a new topic instead of carrying a long unrelated conversation into every request. Provide the passage needed for the current question rather than an entire collection of documents.
Do not solve a failed load by raising every memory-related setting. First try a smaller model, unload work you no longer need, and keep the input short. A larger allowance cannot create physical memory.
A practical first-success path
- Download a small compatible local text model, or use Auto Setup’s Lean plan.
- Open the chat model selector and unload a model you no longer need.
- Select the downloaded local model and send one short request.
- Check the answer before adding a longer conversation or larger input.
- After setup, switch off the network and repeat a task that does not require current web information.
If the app closes or the load fails, return to a smaller model and less context. If only the answer quality is poor, make the question narrower or compare another small model. A memory problem and a model-quality problem need different changes.
Make the phone useful before making the model bigger
Get OGAM and start with one short task. Keep the smallest model that meets that need, unload it when you finish, and add capability only when you have a reason to use it.