Google AI Edge – On-device cross-platform AI deployment
42 comments
·June 1, 2025salamo
Really happy to see additional solutions for on-device ML.
That said, I probably wouldn't use this unless mine was one of the specific use cases supported[0]. I have no idea how hard it would be to add a new model supporting arbitrary inputs and outputs.
For running inference cross-device I have used Onnx, which is low-level enough to support whatever weights I need. For a good number of tasks you can also use transformers.js which wraps onnx and handles things like decoding (unless you really enjoy implementing beam search on your own). I believe an equivalent link to the above would be [1] which is just much more comprehensive.
arbayi
https://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.
ricardobeat
This is a repackaging of TensorFlow Lite + MediaPipe under a new “brand”.
echelon
The same stuff that powers this?
It's pretty impressive that this runs on-device. It's better than a lot of commercial mocap offerings.
AND this was marked deprecated/unsupported over 3 years ago despite the fact it's a pretty mature solution.
Google has been sleeping on their tech or not evangelizing it enough.
6gvONxR4sf7o
Anybody have any experience with this? I just spend a while contorting a custom pytorch model to get it to export to coreml and it was full of this that and the other not being supported, or segfaulting, and all sorts of silly errors. I'd love if someone could say this isn't full of sharp edges too.
smeej
I got it all set up and tested out Gemma3 1B on a Pixel 8a. That only took a few minutes, which was nice.
But it was garbage. It barely parsed the question, didn't even attempt to answer it, and replied in what was barely serviceable English. All I asked was how it was small enough to run locally on my phone. It was bad enough for me to abandon the model entirely, which is saying a lot, because I feel like I have pretty low expectations for AI work in the first place.
DrSiemer
Why would you ask a 1B model anything? Those are only useful for rephrasing output at best.
throwaway314155
> All I asked was how it was small enough to run locally on my phone
Bit off-topic, but did you expect to see a real or honest answer about itself? I see many people under the impression that models know information about themselves that isn't in the system prompt. Couldn't be further from the truth. In face, those questions specifically lead to hallucinations more often resulting in an overconfident assertion with a "reasonable" answer.
The information the model knows (offline - no tools allowed) stops weeks if not months if not years prior to when the model is done training. There is _zero_ information about its inception, how it works, or anything similar in its weights.
Sorry, this is mostly directed at the masses - not you.
smeej
Not really, but I did expect an answer, or at least a non-answer, that showed it understood the question, and that an answer was expected.
yeldarb
Is this a new product or a marketing page tying together a bunch of the existing MediaPipe stuff into a narrative?
Got really excited then realized I couldn’t figure out what “Google AI Edge” actually _is_.
Edit: I think it’s largely a rebrand of this from a couple years ago: https://developers.googleblog.com/en/introducing-mediapipe-s...
hatmanstack
Played with this a bit and from what I gathered it's purely a re-arch of pytorch models to work as .tflite models, at least that's what I was using it for. It worked well with a custom finbert model with negligible size reduction. It converted a quantized version but outputs were not close. From what I remember of the docs it was created for standard pytorch models, like "torchvision.models", so maybe with those you'd have better luck. Granted, this was all ~12 months ago, sounds like I might have dodged a pack of Raptors?
davedx
More information here: https://ai.google.dev/edge/mediapipe/solutions/guide
(It seems to be open source: https://github.com/google-ai-edge/mediapipe)
I think this is a unified way of deploying AI models that actually run on-device ("edge"). I guess a sort of "JavaScript of AI stacks"? I wonder who the target audience is for this technology?
wongarsu
Some of the mediapipe models are nice, but mediapipe has been around forever (or 2019). It has always been about running AI on the edge, back when the exciting frontier of AI were visual tasks.
For stuff like face tracking it's still useful, but for some other tasks like image recognition the world has changed drastically
babl-yc
I would say the target audience is anyone deploying ML models cross-platform, specifically ones that would require supporting code beyond the TFLite runtime to make it work.
LLMs and computer vision tasks are good examples of this.
For example, a hand-gesture recognizer might require: - Pre-processing of input image to certain color space + image size - Copy of image to GPU memory - Run of object detection TFLite model to detect hand - Resize of output image - Run of gesture recognition TFLite model to detect gesture - Post processing of gesture output to something useful
Shipping this to iOS+Android requires a lot of code beyond executing TFLite models.
The Google Mediapipe approach is to package this graph pipeline, and shared processing "nodes" into a single C++ library where you can pick and choose what you need and re-use operations across tasks. The library also compiles cross-platform and the supporting tasks can offer GPU acceleration options.
One internal debate Google likely had was whether it was best to extend TFLite runtime with these features, or to build a separate library (Mediapipe). TFLite already supports custom compile options with additional operations.
My guess is they thought it was best to keep TFLite focused on "tensor based computation" tasks and offload broader operations like LLM and image processing into a separate library.
danielb123
Years behind what is already available through frameworks like CoreML and TimyML. Plus Google has to first prove they won't kill the product to meet the next quarterly investor expectations.
babl-yc
This isn't really true. They are different offerings.
CoreML is specific to the Apple ecosystem and lets you convert a PyTorch model to a CoreML .mlmodel that will run with acceleration on iOS/Mac.
Google Mediapipe is a giant C++ library for running ML flows on any device (iOS/Android/Web). It includes Tensorflow Lite (now LiteRT) but is also a graph processor that helps with common ML preprocessing tasks like image resizing, annotating, etc.
Google killing products early is a good meme but Mediapipe is open source so you can at least credit them with that. https://github.com/google-ai-edge/mediapipe
I used a fork of Mediapipe for a contract iOS/Android computer vision product and it was very complex but worked well. A cross-platform solution would not have been possible with CoreML.
NetOpWibby
I wish MediaPipe was good for facial AR but in my experience it’s lacking.
mattnewton
Tensorflow light has been battle tested on literal billions of devices over the years and this looks like a rebrand/extension of that plus media pipe, one of the biggest users of it. Google has been serious about on device ML for over 5 years now, I don't think they are going to kill this. Confusingly rebrand it maybe :)
elpakal
The generative AI piece is not available in Apple ecosystems right? I think that would be huge and I really hope Apple gives us something similar. And I gotta say the chat piece of this seems really useful too.
Also where the f is Swift Assist already
coderatlarge
is it possible to go to the iPhone app store and get an app that is essentially an ollama like model downloader and launcher?
spacecadet
Its just a rebranded tensorflow lite, Ive been using that on edge devices since 2019... CoreML is great too!
bigyabai
My brother in Christ, CoreML only exists because Apple saw Tensorflow and wanted the featureset without cooperating on a common standard. TF was like 2 years old (and fairly successful) by the point CoreML was announced. To this day CoreML is little more than a proprietary BLAS interface, with nearly zero industry buy-in.
Terrifying what being an iOS dev does to a feller.
init0
This can be done with WebLLM, no?
stanleykm
i really wish people who make edge inference libraries like this would quit rebranding them every year and just build the damn things to be fast and small and consistently updated.
bigyabai
ONNX exists but since they don't change their name very often not a whole lot of people know about it.
pzo
ONNXRuntime is actually quite popular mostly because Hugging Face transformers - many people just don't know they using it under the hood. What is missing is transformers native so you can easily deploy it not only on desktops and servers. Transformers.js is some kind of attempt - can deploy on Web and React Native.
zb3
So can we run Gemma 3n on linux? So much fluff yet this is unclear to me.
saratogacx
In the model's community section Goog confirms they're working on a gguf version so you can host it like most other models.
https://huggingface.co/google/gemma-3n-E4B-it-litert-preview...
quaintdev
As far as I know it's based on Gemini nano architecture which exclusively runs on Android and Chrome. So I'm guessing you can't run it on Linux outside Chrome.
My take: tensorflow lite + mediapipe was great but google really neglected it in the last 3 years or so. Mediapipe didn't have many meaningful update in last 3 years. A lot of models today are outdated or slow. TF Lite supported NPU (like apple ANU) but mediapipe never did. They had also too much mess with different branding: MLKit, Firebase ML, TF lite, LiteRT.
This days probably better to stick with onnxruntime via hugging face transformers or transformers.js library or wait until executorch mature. I haven't seen any SOTA model officially released having official port to tensorflow lite / liteRT for a long time: SAM2, EfficientSAM, EdgeSAM, DFINE, DEIM, Whisper, Lite-Whisper, Kokoro, DepthAnythingV2 - everything is pytorch by default but with still big communities for ONNX and MLX