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There is no universal Android-phone minimum for running a local large language model (LLM). The requirement depends on the particular model, its format and task, the inference runtime, and what resources the phone has available. As a practical starting point for experimentation, consider a phone with 8–12 GB of RAM and 128–256 GB of storage; 12 GB RAM and 256 GB storage offer more headroom, but those figures are guidance, not official compatibility requirements. Check the specific model and runtime on the handset you intend to use before buying a phone for local AI.
Why do RAM and storage requirements differ?
RAM and internal storage solve different problems. Storage holds the downloaded model file and related app data. RAM is used while the runtime loads and runs the model. A model’s file size therefore does not tell you exactly how much RAM it will use during inference, and a memory estimate is not a download-size estimate.
The model is only one part of the calculation. A text-only task may have a different footprint from a broader mobile or multimodal workload, and the inference tool and environment can change the result. Google’s Gemma 4 model documentation labels its memory figures approximate and notes that they can vary with the inference tool and environment. The estimates below are useful for understanding scale, not for declaring that every phone with a particular RAM label will work.
There is also a difference between the RAM printed on a phone’s specifications and the memory available to a model. The operating system and other running apps use memory too, so the advertised total should not be treated as a guaranteed model allocation. The sources do not establish one universal amount of RAM that every Android phone reserves for inference.
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How much RAM do the published Android examples use?
Google’s Gemma 4 figures show why the answer should be model-specific: the estimates differ by model and by workload. They describe approximate inference memory, not the total RAM a phone must have.
| Gemma 4 model and workload | Approximate mobile inference memory | What the figure means |
|---|---|---|
| E2B, text-only | 0.84 GB | Google’s approximate estimate for the text-only workload. |
| E2B, broader mobile workload | 1.1 GB | Google’s approximate estimate for mobile use, not a total-phone requirement. |
| E4B, text-only | 2.2 GB | Google’s approximate estimate for the text-only workload. |
| E4B, broader mobile workload | 2.5 GB | Google’s approximate estimate for mobile use, not a total-phone requirement. |
All four figures are Google’s approximate model-memory estimates in its Gemma 4 documentation, accessed in 2026. They are not minimum phone-RAM specifications, and they should not be added to a phone’s RAM figure or interpreted as a promise about speed. A phone needs room for Android and other active apps as well as the model’s runtime needs.
The practical implication is to start with the workload you actually want. If your goal is text generation, look for a published estimate for that model and text configuration. If you want a broader mobile workload, use the relevant broader estimate rather than assuming the text-only number covers it. Do not infer a precise requirement from a model’s name or parameter count alone when documentation provides workload-specific figures.
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How much storage does the downloaded model take?
Storage planning starts with the model artifact size, then leaves room for the app, any other model variants you download, updates, and your ordinary phone data. Google’s LiteRT-LM overview lists the following Gemma 4 model sizes:
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| Model | Listed model file size |
|---|---|
| Gemma 4 E2B | 2,583 MB |
| Gemma 4 E4B | 3,654 MB |
These are the listed model sizes in Google’s LiteRT-LM overview, accessed in 2026. Treat them as the size of the listed artifacts, not as the total storage an installed app will use. The page does not establish a single total-storage allowance covering every app, update, model variant, and personal file.
For a buying decision, compare the actual free space you expect to have with the model file you plan to download; do not equate a phone’s advertised storage capacity with free storage. A 128 GB or 256 GB capacity is a broad planning choice, not a model-specific compatibility threshold. The more models and other files you keep locally, the more useful extra capacity becomes.
What do real-device measurements tell you?
Google’s LiteRT-LM overview reports peak CPU memory measurements on a Samsung S26 Ultra for two Gemma 4 models. The measurements illustrate one model-and-device setup; they are not a complete Android compatibility list or a prediction for a different handset.
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|---|---|---|
| Gemma 4 E2B | 1,733 MB | 2,583 MB |
| Gemma 4 E4B | 3,283 MB | 3,654 MB |
These are the figures reported by the LiteRT-LM overview for that named device and runtime context. The table places peak CPU memory beside artifact size to make clear that they are different measurements. It does not show that a model file must fit in RAM at its full file size, nor does it establish that another phone will have the same peak use.
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Use a benchmark only when its device, model, runtime, and workload are relevant to your intended setup. The overview offers selected examples rather than a broad phone-by-phone matrix. If your chosen phone is not represented, the published number is a reference point—not a substitute for checking support and testing the exact combination.
Is 8–12 GB of RAM and 128–256 GB of storage enough?
Those ranges are reasonable shopping heuristics for experimentation, not an official minimum. A phone toward the upper end—12 GB RAM and 256 GB storage—gives more general headroom, but those labels alone cannot confirm that a particular model will run well.
Consider the RAM range a way to narrow a shortlist, not a pass/fail test. The Gemma figures show that approximate inference needs vary by model and workload, while the device measurements show that peak use depends on a specific implementation. Available memory also changes with what else is running. Consequently, it would be misleading to promise that every 8 GB phone can run a selected model or that every 12 GB phone will run every model smoothly.
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How should you check a phone before choosing it?
Work from the exact workload back to the hardware. This avoids buying on a RAM number alone or assuming that a model’s download size determines whether it can run.
- Name the model and task. Decide whether you want a text-only model or a broader mobile workload. Find the model’s own memory estimate for that use case; do not substitute a different model’s figure.
- Identify the runtime and model format. Check that the specific model artifact is supported by the runtime you plan to use, and verify the phone’s support for the relevant runtime and hardware acceleration path. A model estimate by itself does not verify compatibility.
- Compare memory in context. Look for a peak-memory result measured with the same model and runtime on the intended phone, if one is available. Treat a measurement from another handset as an example, not a guarantee, and leave room for Android and other active apps.
- Check the actual model download size. Compare the artifact size with the phone’s expected free storage, then account for app data, additional models or variants, updates, and personal files.
- Verify on the intended handset before relying on it. Install or run the specific model-runtime combination if possible, and observe whether it loads and completes the task you need. Published estimates and selected benchmarks are not guarantees of performance on an unmeasured phone.
This process is especially important if the phone is being purchased primarily for local inference. The sources reviewed do not provide a complete, current list of Android handsets, RAM configurations, and model compatibility, so a general hardware range cannot replace checking the phone maker’s and runtime’s current documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does Android Studio’s 12 GB requirement apply to a phone?
No. The 12 GB RAM and 4 GB storage figures on Android Developers’ local-model page refer to running Gemma E4B with Android Studio on a local development computer, not to an Android phone’s minimum specifications.
The distinction matters because a developer computer running an IDE has a different environment from a phone running an inference app. Do not use that row as evidence that a phone needs exactly 12 GB of RAM or 4 GB of storage. The relevant page is Use a local model in Android Studio; its stated requirements should be read in that desktop-development context.
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Which runtime guidance should Android developers follow?
For new Android app work, check Google’s current runtime guidance rather than starting from an older API tutorial. Google says the MediaPipe LLM Inference API for Android, iOS, and Web is in maintenance-only mode and recommends migrating to LiteRT-LM for continued support and performance.
That guidance is about the development path, not a promise that every phone supports every LiteRT-LM model or acceleration route. Review the LLM Inference guide alongside the LiteRT-LM overview for current runtime details, then confirm the model and device combination you intend to ship or use.
What should you take away when comparing phones?
Use RAM and storage as separate filters, then validate the model, workload, and runtime. A phone with 12 GB RAM and 256 GB storage is a sensible general target for someone exploring local models, but it is not an official minimum and cannot stand in for device-specific compatibility checks.
- For RAM, start with the selected model’s workload-specific estimate and seek a comparable runtime/device measurement.
- For storage, start with the actual model artifact size and allow room for apps, updates, other models, and personal files.
- For compatibility, verify the runtime, model format, phone support, and any intended acceleration path.
Frequently Asked Questions
Can I store an Android LLM model on a microSD card?
The cited model and runtime documentation does not establish microSD support or recommend it as a model location. Confirm that both the Android app and the specific runtime can read the model from removable storage before relying on that setup.
Will a local LLM keep working if I delete its downloaded model?
A local inference runtime needs access to the model artifact it is meant to run. If you remove that file, expect to need to download or restore the model before using that model again; the exact app behavior depends on the app.
Does a lower memory estimate mean a model will respond faster?
Not necessarily. The cited figures describe memory use, not response speed, and they do not compare generation performance across phones. Treat memory and speed as separate characteristics when assessing a particular model-runtime-device combination.
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