[HN Gopher] Google AI Edge - On-device cross-platform AI deployment
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Google AI Edge - On-device cross-platform AI deployment
Author : nreece
Score : 157 points
Date : 2025-06-01 06:32 UTC (16 hours ago)
(HTM) web link (ai.google.dev)
(TXT) w3m dump (ai.google.dev)
| davedx wrote:
| 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 wrote:
| 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 wrote:
| 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.
| yeldarb wrote:
| 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...
| rvnx wrote:
| Make your own opinion here: https://mediapipe-
| studio.webapps.google.com/studio/demo/imag...
|
| Go to this page using your mobile phone.
|
| I am apparently a doormat or a seatbelt.
|
| It seems to be a rebranded failure. At Google you get promoted
| for product launches because of the OKRs system and more rarely
| for maintenance.
| tfsh wrote:
| Perhaps you missed the associated documentation? This is a
| classification tool which requires input labels "uses an
| EfficientNet architecture and was trained using ImageNet to
| recognize 1,000 classes, such as trees, animals, food,
| vehicles".
|
| The full list [1] doesn't seem to include a human. You can
| tweak the score threshold to reduce false positives.
|
| 1: https://storage.googleapis.com/mediapipe-
| tasks/image_classif...
| rvnx wrote:
| You're right about human, that would explain it, but still I
| find it surprising that such "common item" as a human is not
| there.
|
| Did you also try on items from the list ?
|
| If there is a match (and this is not frequent), to me it's
| still very low confidence (like noise or luck).
|
| It seems to be a repacking of
| https://blog.tensorflow.org/2020/03/higher-accuracy-on-
| visio...
|
| So an old release from 5 years ago (like very long time in
| AI-world), and AFAIK it has been superseded by YOLO-NAS and
| other models. MediaPipe feels really old tool, except for
| some specific subtasks like face tracking.
|
| And as a side-note, the OKR-system at Google is a very
| serious thing, there are lot of people internally gaming the
| system, and that could explain why it is a "new" launch,
| instead of a rather disappointing rebrand of the
| 2020-version.
|
| I'd rather recommend building on more modern tools, such as:
| https://huggingface.co/spaces/HuggingFaceTB/SmolVLM-256M-Ins.
| .. (runs on iPhone with < 1GB of memory)
| bigyabai wrote:
| > And as a side-note, the OKR-system at Google is a very
| serious thing, there are lot of people internally gaming
| the system.
|
| So you came here to offer a knee-jerk assessment of an AI
| runtime and blamed the failure on OKRs. Then somebody
| points out that your use-case isn't covered by the model,
| and you're looping back around to the OKR topic again. To
| assess an AI inference tool.
|
| Why would you even bother hitting reply on this post if you
| don't want to talk about the actual topic being discussed?
| "Agile bad" is not a constructive or novel comment.
| danielb123 wrote:
| 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.
| spacecadet wrote:
| Its just a rebranded tensorflow lite, Ive been using that on
| edge devices since 2019... CoreML is great too!
| babl-yc wrote:
| 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 wrote:
| I wish MediaPipe was good for facial AR but in my experience
| it's lacking.
| bigyabai wrote:
| 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.
| elpakal wrote:
| 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
| mattnewton wrote:
| 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 :)
| zb3 wrote:
| So can we run Gemma 3n on linux? So much fluff yet this is
| unclear to me.
| quaintdev wrote:
| 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.
| saratogacx wrote:
| 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...
| ricardobeat wrote:
| This is a repackaging of TensorFlow Lite + MediaPipe under a new
| "brand".
| echelon wrote:
| The same stuff that powers this?
|
| https://3d.kalidoface.com/
|
| 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.
| hatmanstack wrote:
| 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?
| stanleykm wrote:
| 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 wrote:
| ONNX exists but since they don't change their name very often
| not a whole lot of people know about it.
| arbayi wrote:
| 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.
| fdoifdois wrote:
| Related: https://stackoverflow.com/q/79454372/320615
| roflcopter69 wrote:
| Genuine question, why should I use this to deploy models on the
| edge instead of executorch? https://github.com/pytorch/executorch
|
| For context, I get to choose the tech stack for a greenfield
| project. I think that executor h, which belongs to the pytorch
| ecosystem, will have a way more predictable future than anything
| Google does, so I currently consider executorch more.
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