Need models

I poked through what's out there. There are a few Android apps / projects already dipping into what Gen 5 / advanced NPUs can do. Not massive mainstream ones yet (because the hardware is just arriving), but enough to see patterns. Here are the ones I found:

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Examples of Android / AI apps supporting (or trialing) NPU / on-device acceleration

App / Project What it does / supports Notes & evidence

LiteDiffusion / Stable Diffusion on Android (local mode) Runs diffusion model locally on Snapdragon NPU / GPU Reddit users report a "fast stable diffusion Android app based on Snapdragon NPU" that supports 6 model checkpoints. On Snapdragon chips the NPU version is much faster; CPU version is very slow.
LM Studio on Snapdragon NPU Local LLM inference Some users reported success with LM Studio running a small 3B model (quantized) using Snapdragon NPU. They warn NPUs currently have memory / bandwidth limits.
Qualcomm DLC model support (developer / OEM side) Running models via Android app with NPU support Qualcomm developer forums discuss building Android apps that leverage NPU capabilities for DLC (deep learning comp) models.
Honor's "Agent-powered Magic Color" (future feature) Image editing via semantic commands Honor teased that their Magic 8 Pro will use the Snapdragon 8 Elite Gen 5 + low-bit quantization for more efficient on-device edits.

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What's holding apps back / limiting support

Memory & addressable limits: NPUs often cannot address large model weights fully; you need small / quantized models. One Redditer said NPUs are limited to 4-8 GB addressable for now.

Model quantization & format matching: The models must be converted / quantized (INT8, INT4, etc.) to run on NPUs - many apps still run GPU/CPU fallback.

Bandwidth & I/O bottlenecks: Moving weights and data between storage, RAM, and the NPU introduces delay if internal bus / memory is weak.

Software SDK / support lag: App developers need frameworks & SDKs from Qualcomm / Android that expose NPU features - until that stack is solid, many AI apps still rely on cloud.

Thermal / sustained load constraints: Even with NPU acceleration, long sessions (audio, video, long transcripts) may overheat phones, triggering throttling.

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What this means for Gen 5 + 24+1TB device (for Android AI apps)

When Gen 5 phones arrive, many AI apps out there will begin enabling on-device modes (if they weren't before) - locally summarizing, image editing, translation, etc.

You'll see hybrid mode: apps that run small parts locally (on NPU) and send heavier parts to cloud - giving speed + privacy.

In apps like Gemini / ChatGPT, the experience may improve for short tasks (local prefilter, partial inference) but long tasks (1 hr transcripts) will still lean on the cloud until model sizes shrink or devices get more capability.

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