iPhone vs Android for Local AI: Which Runs LLMs Better?

For running language models on your phone, recent iPhones are the more predictable choice: every one has a GPU that local AI apps can use through Apple’s Metal, so the only variable is how much RAM the model has. Android has the higher ceiling and far more choice, but GPU acceleration depends on the chip. Snapdragon phones with Adreno 700-series or newer GPUs get it in apps built on llama.cpp; Pixels, Exynos Galaxy phones and most MediaTek devices fall back to the slower CPU.

If you already own a phone, the answer is simpler: try it. Most phones from the last few years run a 2B to 4B model.

iPhone vs Android at a glance #

What to expect on an iPhone #

GPU acceleration is universal. llama.cpp supports Apple’s Metal graphics API, so apps built on it use the GPU on any iPhone they run on at all. After model size, that’s the single biggest speed factor, and it means a four-year-old iPhone gets the same kind of acceleration as a new one.

RAM is the limit. Apple was stingy with memory for years, so the model you can run tracks the model of iPhone closely:

iOS manages memory aggressively. Load a big model, switch to the camera or a game, and iOS may close the AI app in the background, forcing a reload when you return. Smaller models survive app switching better, which is a real quality-of-life difference that benchmarks never show.

Apple Intelligence is a separate thing. iPhones from the 15 Pro onward also run Apple’s own on-device model for Apple’s own features. Local AI chat apps that run open models don’t depend on it, so an iPhone too old for Apple Intelligence is still a perfectly good local AI phone.

What to expect on Android #

GPU acceleration depends on the chip. llama.cpp has an OpenCL backend for Qualcomm’s Adreno GPUs, so apps built on it accelerate on Snapdragon phones with Adreno 700-series graphics or newer, roughly the 8-series flagships from the Snapdragon 8 Gen 1 onward. Google’s Tensor chips, Samsung’s Exynos and most MediaTek chips use other GPU designs, so those phones run on the CPU. Small models remain usable on a fast CPU, just slower, and the gap shows up most on long answers.

NPUs are mostly unused by chat apps. llama.cpp has a Hexagon backend for Qualcomm’s NPU, but most chat apps rely on the GPU or CPU. Do you need an NPU to run AI explains why that’s unlikely to change soon.

More RAM for the money. Android flagships commonly ship 8 to 12 GB and sometimes more, which buys room for 9B models plus other apps staying open.

More variety, more variance. Two Android phones with identical RAM can differ substantially depending on the chip, the memory generation and how the maker handles heat. The same app, the same model and the same question can produce very different speeds.

Which runs local AI faster? #

It depends on the exact phones, so the honest answer is to measure. Three things decide it:

1. Model file size. Each word means reading most of the model from memory.
2. GPU or CPU. GPU acceleration is often several times faster.
3. Memory bandwidth and cooling. Flagships sustain their speed longer before throttling.

A recent Snapdragon flagship with 12 GB and a recent iPhone Pro are both comfortable with 9B models. An iPhone 13 and a Pixel will both run 2B to 4B models, the iPhone with GPU help and the Pixel on the CPU. Personal LLM has a built-in benchmark that reports tokens per second and whether the run used the GPU or the CPU, which settles the question for your phone in about a minute. Local LLM speed on your phone explains how to read it.

Does the app experience differ? #

Not much, when an app is built for both. Personal LLM offers the same catalog (Qwen 3.5, Gemma 4, GLM 4.6V Flash, Ministral 3), the same photo questions, document chat and custom GGUF support on iOS and Android, free on both, with ads that can’t load while you’re offline. The “Fits your device” badge reads each phone’s actual RAM, so the recommendations adjust to the hardware.

Where platforms do diverge is in the extras. Apple-only apps can hook into Siri and Shortcuts, which no Android equivalent can match, while Android gives you freer access to files and more phones to choose from at any given price.

Which should you buy? #

If you’re choosing a handset specifically for this, the short version is 12 GB and a supported GPU, on either platform. Best phones for running a local LLM ranks current iPhone, Galaxy, Pixel and used options, with the RAM and chip details for each. How much RAM you need to run an LLM on a phone covers what each tier actually runs.

Frequently asked questions #

Can I run an LLM on an iPhone? #

Yes. Any iPhone running iOS 15.1 or later can run small models, and iPhones with 8 GB of RAM or more handle 9B models. Download the model once on Wi-Fi and it works offline afterwards.

Can I run an LLM on Android? #

Yes, on Android 7 or later. Phones with 3 GB of RAM run small 2B to 4B models, and 8 to 12 GB phones handle 9B models. Snapdragon phones with recent Adreno GPUs are noticeably faster in apps that use GPU acceleration.

Does the Pixel’s Tensor chip help with local LLMs? #

Not much in llama.cpp-based apps, which generally run on the Pixel’s CPU because its GPU isn’t an Adreno. Google’s own on-device features use the Tensor chip’s AI hardware, but third-party chat apps usually can’t reach it. Small models still run acceptably on Pixel CPUs.

Do Exynos Galaxy phones run local AI slower than Snapdragon ones? #

In apps that accelerate only Adreno GPUs, yes: an Exynos model falls back to the CPU. The phones are otherwise capable, so a smaller model is the usual answer. Check which chip your region’s model uses before buying for this purpose.

How much storage do I need for local AI? #

Plan on 1 to 7 GB per model, plus up to 1 GB more for image support. Most people keep one or two models, so 5 to 10 GB free is plenty.