NOnolist_policy·May 5, 2026
Works great in the latest version of Google AI Edge Gallery: https://github.com/google-ai-edge/gallery/releases
In Accelerating Gemma 4: faster inference with multi-token prediction draftersgoogle-ai-edge
gallery
Kotlin2025–2026
google-ai-edge / gallery
gallery
A gallery that showcases on-device ML/GenAI use cases and allows people to try and use models locally.
16Distinct people
20Mentions
0Explicit recommenders
25.3KGitHub stars
What to know before exploring
What it is
A gallery that showcases on-device ML/GenAI use cases and allows people to try and use models locally.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month20 dated mentions
Observed mentions: 2025-05-20 19:54:29 UTC to 2026-05-05 21:11:24 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| May 2025 | 2025-05-01 00:00:00 UTC | 2025-06-01 00:00:00 UTC | 3 | 1 | 2 |
| Jun 2025 | 2025-06-01 00:00:00 UTC | 2025-07-01 00:00:00 UTC | 8 | 3 | 5 |
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 1 | 0 | 1 |
| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 1 | 0 | 1 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 5 | 0 | 5 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
20 sourcesIt's up on GitHub: https://github.com/google-ai-edge/gallery/releases
In Accelerating Gemma 4: faster inference with multi-token prediction draftersThey released the source (well, currently only the Android version) at https://github.com/google-ai-edge/gallery .
At a glance, I see they do gather analytics about how much the app is used (model downloads, model invocations etc) without message content, pretty much just the model used.
In Google Gemma 4 Runs Natively on iPhone with Full Offline AI InferenceTwo (very quick) minutes on their GitHub repo and it's pretty obvious that they're using firebase-analytics and at the very least seem to be sending URLs[1] and infos such as the model you download or the capacities[2] you use.
[1] https://github.com/google-ai-edge/gallery/blob/main/Android/...
[2] https://github.com/google-ai-edge/gallery/blob/main/Android/...
In Gemma 4 on iPhoneThe app is open source[0], although given Apple's stance on sideloading it's hard to confirm if you're using the open version.
[0] https://github.com/google-ai-edge/gallery
In Gemma 4 on iPhoneFound my answer: https://github.com/google-ai-edge/gallery?tab=readme-ov-file
In Ask HN: Best LLM model for a RAG-based Android app across all smartphones?Google AI Edge Gallery: https://github.com/google-ai-edge/gallery/releases
In Google releases Gemma 4 open modelsLooks useful, though something went wrong doing NPU image generation on my phone.
Reminds me a lot of https://github.com/google-ai-edge/gallery which is a proof-of-concept app by Google themselves for their AI libraries. However, your app supports more and larger models without having to manually import anything, which is very useful.
In Show HN: Off Grid – Run AI text, image gen, vision offline on your phoneHave you tried Google's Gemma-3n-E4B-IT in their AI Edge Gallery app? It's the first local model that's really blown me away with its power-to-speed ratio on a mobile device.
See: https://github.com/google-ai-edge/gallery/releases/tag/1.0.3
In Open models by OpenAISomethings really screwy with on-device models from Google, I can't put my finger on what, and I think being ex-Google is screwing with my ability to evaluate.
Cherry-picking something that's quick to evaluate:
"High throughput: Processes up to 60 frames per second on a Google Pixel, enabling real-time, on-device video analysis and interactive experiences."
You can download an APK from the official Google project for this, linked from the blogpost: https://github.com/google-ai-edge/gallery?tab=readme-ov-file...
If I download it, run it on Pixel Fold, actual 2B model which is half the size of the ones the 60 fps claim is made for, it takes 6.2-7.5 seconds to begin responding (3 samples, 3 diff photos). Generation speed is shown at 4-5 tokens per second, slightly slower than what llama.cpp does on my phone. (I maintain an AI app that inter
Read the rest
alia, wraps llama.cpp on all platforms)
So, *0.16* frames a second, not 60 fps.
The blog post is so jammed up with so many claims re: this is special for on-device and performance that just...seemingly aren't true. At all.
- Are they missing a demo APK?
- Was there some massive TPU leap since the Pixel Fold release?
- Is there a lot of BS in there that they're pretty sure won't be called out in a systematic way, given the amount of effort it takes to get this inferencing?
- I used to work on Pixel, and I remember thinking that it seemed like there weren't actually public APIs for the TPU. Is that what's going on?
In any case, either:
A) I'm missing something, big or
B) they are lying, repeatedly, big time, in a way that would be shown near-immediately when you actually tried building on it because it "enables real-time, on-device video analysis and interactive experiences."
Everything I've seen the last year or two indicates they are lying, big time, regularly.
But if that's the case:
- How are they getting away with it, over this length of time?
- How come I never see anyone else mention these gaps?
LOL when you realize that Google wants you to download the apk and sideload it instead of installing from the Play Store ( https://github.com/google-ai-edge/gallery#-get-started-in-mi... )
You know how terrible the store and the publishing process are -- their own people don't even use it.
In Google AI Edge – On-device cross-platform AI deploymentRun LLM models on your Android phone locally
2 points · Open discussionhttps://github.com/google-ai-edge/gallery
A gallery that showcases on-device ML/GenAI use cases and allows people to try and use models locally.
In Google AI Edge – On-device cross-platform AI deploymentShowing the 15 most recent of 20 sources. Enable JavaScript to search and browse every indexed mention.