Impressive model, for sure. I've been running it on my Mac, now I get to have it locally in my iPhone? I need to test this. Wait, it does agent skills and mobile actions, all local to the phone? Whaaaat? (Have to check out later! Anyone have any tips yet?)
I don't normally do the whole "abliterated" thing (dealignment) but after discovering https://github.com/p-e-w/heretic , I was too tempted to try it with this model a couple days ago (made a repo to make it easier, actually) https://github.com/pmarreck/gemma4-heretical and... Wow. It worked. And... Not having a built-in nanny is fun!
It's also possible to make an MLX version of it, which runs a little faster on Macs, but won't work through Ollama unfortunately. (LM Studio maybe.)
Runs great on my M4 Macbook Pro w/128GB and likely also runs fine under 64GB... smaller memories might require lower quantizations.
I specifically like dealigned local models because if I have to get my thoughts policed when playing in someone else's playground, like hell am I going to be judged while messing around in my own local open-source one too. And there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
Note: I tried to hook this one up to OpenClaw and ran into issues
To answer the obvious question- Yes, this sort of thing enables bad actors more (as do many other tools). Fortunately, there are far more good actors out there, and bad actors don't listen to rules that good actors subject themselves to, anyway.
> And there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
I checked the abliterate script and I don't yet understand what it does or what the result is. What are the conversations this enables?
LLMs are very helpful for transcribing handwritten historical documents, but sometimes those documents contain language/ideas that a perfectly aligned LLM will refuse to output. Sometimes as a hard refusal, sometimes (even worse) by subtly cleaning up the language.
In my experience the latest batch of models are a lot better at transcribing the text verbatim without moralizing about it (i.e. at "understanding" that they're fulfilling a neutral role as a transcriber), but it was a really big issue in the GPT-3/4 era.
I have a project where I'm using LLMs to parse data from PDFs with a very complicated tabular layout. I've been using the latest Gemini models (flash and pro) for their strong visual reasoning, and they've generally been doing a really good job at it.
My prompt states that their job is to extract the text exactly as it appears in the PDF. One data point to be extracted is the race of each person listed. In one case, someone's race was "Indian". Gemini decided to extract it as "Native American". So ridiculous.
I was attempting to help someone who runs a small shop selling restored clothing set up a gemini pipeline that would restage images she took of clothing items with bad lighting, backgrounds, etc.
Basically anything that showed any “skin” on a mannequin it would refuse to interact with. Even just a top, unless she put pants on the mannequin.
In my experience, though, it's necessary to do anything security related. Interestingly, the big models have fewer refusals for me when I ask e.g. "in <X> situation, how do you exploit <Y>?", but local models will frequently flat out refuse, unless the model has been abliterated.
It’s so dispiriting to me that we’ve achieved those closest thing yet to an “objective truth” machine (with the caveat of garbage in, garbage out, etc.) and these big companies are either afraid to actually let it exist, want to push their own politics, or a combination of the two.
"closest thing yet" is still a long way from close; as you say, gin=gout, and the internet without an attempt to be our best selves is instead our loudest propagandists and all our cultural stereotypes.
Of course, humans are also impacted by these things, at best we can be a little deliberate about rejecting a few of the more on-the-nose examples.
1) Coming up with any valid criticism of Islam at all (for some reason, criticisms of Christianity or Judaism are perfectly allowed even with public models!).
2) Asking questions about sketchy things. Simply asking should not be censored.
3) I don't use it for this, but porn or foul language.
4) Imitating or representing a public figure is often blocked.
5) Asking security-related questions when you are trying to do security.
6) For those who have had it, people who are trying to use AI to deal with traumatic experiences that are illegal to even describe.
> Coming up with any valid criticism of Islam at all (for some reason, criticisms of Christianity or Judaism are perfectly allowed even with public models!).
When’s the last time you tried this? ChatGPT and Gemini have no trouble responding with all the common criticisms of Islam.
That is why they were pushed away from this. At least with vibe coded software, errors may prevent compilation, then when we're past that simply bad experiences, before they become human catastrophes.
Assuming you’re not copy/pasting for these tasks. What’s the stack required to use local models for coding? I’ve got a capable enough machine to produce tokens slowly, but don’t understand how to connect that to the likes of VSCode or a JetBrains ide.
You need some way to give it tools - the essential ones for coding are running bash commands, reading files and editing files.
You need the LLM to be able to respond with tool use requests, and then your local harness to process them and respond to it. You can read how tool calling works with eg Claude API to get the idea: https://platform.claude.com/docs/en/agents-and-tools/tool-us...
Under the hood something like Claude Code is calling the API with tools registered, and then when it gets a tool use request it runs that locally, and then responds to the API with the result. That’s the loop that enables coding.
Integrating with an IDE specifically is really just a UI feature, rather than the core functionality.
You're comparing apples to oranges there. Qwen 3.5 is a much larger model at 397B parameters vs. Gemma's 31B. Gemma will be better at answering simple questions and doing basic automation, and codegen won't be it's strong suit.
Qwen3.5 comes in various sizes (including 27B), and judging by the posts on HN, /LocalLlama etc., it seems to be better at logic/reasoning/coding/tool calling compared to Gemma 4, while Gemma 4 is better at creative writing and world knowledge (basically nothing changed from the Qwen3 vs. Gemma3 era)
Haven't built anything on the agent skills platform yet, but it's pretty cool imo.
On Android the sandbox loads an index.html into a WebView, with standardized string I/O to the harness via some window properties. You can even return a rendered HTML page.
Definitely hacked together, but feels like an indication of what an edge compute agentic sandbox might look like in future.
>there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
Mind giving us a few of the examples that you plan to run in your local LLM? I am curious.
Not to mention that doing what the big model makers do literally dumbs the model down.
They should at least allow something like letting you prove your age and identity to give you access to better/unaligned models, maybe even requiring a license of some sort. Because you know what? SOMEONE in there absolutely has access to the completely uncensored versions of the latest models.
This app is cool and it showcases some use cases, but it still undersells what the E2B model can do.
I just made a real-time AI (audio/video in, voice out) on an M3 Pro with Gemma E2B. I posted it on /r/LocalLLaMA a few hours ago and it's gaining some traction [0]. Here's the repo [1]
I'm running it on a Macbook instead of an iPhone, but based on the benchmark here [2], you should be able to run the same thing on an iPhone 17 Pro.
Thanks for sharing! I'm still torn about it. Sure it'll feel more natural if you have the AI head animation, but I don't want people to get attached to it. I don't want to make the loneliness epidemic even worse.
Thanks! Although, I can't claim any credit for it. I just spent a day gluing what other people have built. Huge props to the Gemma team for building an amazing model and also an inference engine that's focused for edge devices [0]
1) I am able to run the model on my iPhone and get good results. Not as good as Gemini in the cloud, but good.
2) I love the “mobile actions” tool calls that allow the LLM to turn on the flashlight, open maps, etc. It would be fun if they added Siri Shortcuts support. I want the personal automation that Apple promised but never delivered.
3) I am so excited for local models to be normalized. I build little apps for teachers and there are stringent privacy laws involved that mean I strongly prefer writing code that runs fully client-side when possible. When I develop apps and websites, I want easy API access to on-device models for free. I know it sort of exists on iOS and Chrome right now, but as far as I’m aware it’s not particularly good yet.
For me the hallucination and gaslighting is like taking a step back in time a couple of years. It even fails the “r’s in strawberry” question. How nostalgic.
It’s very impressive that this can run locally. And I hope we will continue to be able to run couple-year-old-equivalent models locally going forward.
I haven't seen anybody else post it in this thread, but this is running on 8GB of RAM. It's not the full Gemma 4 32B model. It's a completely different thing from the full Gemma 4 experience if you were running the flagship model, almost to the point of being misleading.
It's their E2B and E4B variants (so 2B and 4B but also quantized)
The relevant constraint when running on a phone is power, not really RAM footprint. Running the tiny E2B/E4B models makes sense, this is essentially what they're designed for.
Between the GPU, NPU and big.LITTLE cores, many phones have no fewer than 4 different power profiles they can run inference at. It's about as solved as it will get without an architectural overhaul.
OP Here. It is my firm belief that the only realistic use of AI in the future is either locally on-device for almost free, or in the cloud but way more expensive then it is today.
The latter option will only bemusedly for tasks that humans are more expensive or much slower in.
This Gemma 4 model gives me hope for a future Siri or other with iPhone and macOS integration, “Her” (as in the movie) style.
> or in the cloud but way more expensive then it is today.
Why? It's widely understood that the big players are making profit on inference. The only reason they still have losses is because training is so expensive, but you need to do that no matter whether the models are running in the cloud or on your device.
If you think about it, it's always going to be cheaper and more energy-efficient to have dedicated cloud hardware to run models. Running them on your phone, even if possible, is just going to suck up your battery life.
> It's widely understood that the big players are making profit on inference.
This is most definitely not widely understood. We still don't know yet. There's tons of discussions about people disagreeing on whether it really is profitable. Unless you have proof, don't say "this is widely understood".
I don’t have “proof” but the existence of so many providers of free models on OpenRouter strongly suggests inference is running at a profit. There’s no winner-takes-all angle to being a faceless provider there (often the consumer doesn’t know who fulfilled the request), so there’s just no incentive at all for these small provider companies to exist unless inference is profitable under the right conditions.
The big players are plausibly making profits on raw API calls, not subscriptions. These are quite costly compared to third-party inference from open models, but even setting that up is a hassle and you as a end user aren't getting any subsidy. Running inference locally will make a lot of sense for most light and casual users once the subsidies for subscription access cease.
Also while datacenter-based scaleout of a model over multiple GPUs running large batches is more energy efficient, it ultimately creates a single point of failure you may wish to avoid.
> It's widely understood that the big players are making profit on inference.
If you add in the cost of training, it’s not profitable.
Not including the cost of training is a bit like saying the only cost of a cup of coffee is the paper cup it’s in. The only way OpenAI gets to charge for inference is by selling a product people can’t get elsewhere for much cheaper, which means billions in R&D costs. But because of competition, each model effectively has a “shelf life”.
At least Anthropic claims that they are profitable on a per model basis. But since both revenue and training costs are growing exponentially, and they need to pay for model N training today, and only get revenue for model N-1 today, the offset makes it look worse than it is.
Obviously that doesn’t help them turn a profit, until they can stop growing training costs exponentially.
So it’s really a race to see whether growth in revenue or training costs decelerates first.
> It's widely understood that the big players are making profit on inference.
I love the whole “they are making money if you ignore training costs” bit. It is always great to see somebody say something like “if you look at the amount of money that they’re spending it looks bad, but if you look away it looks pretty good” like it’s the money version of a solar eclipse
The reason it matters is that if they are making a profit on inference, then when people use their services more, it cuts their losses. They might even break even eventually and start making a profit without raising the price.
But if they're losing money on inference, they will lose more money when people use their services more. There's no way to turn that around at that price.
> It's widely understood that the big players are making profit on inference.
Are they? Or are they just saying that to make their offerings more attractive to investors?
Plus I think most people using agents for coding are using subscriptions which they are definitely not profitable in.
Locally running models that are snappy and mostly as capable as current sota models would be a dream. No internet connection required, no payment plans or relying on a third party provider to do your job. No privacy concerns. Etc etc.
> Plus I think most people using agents for coding are using subscriptions which they are definitely not profitable in.
Where on earth do people get this idea? Subscriptions that are based around obscure, vendor defined "credits" are the perfect business model for vendors. They can change the amount you can use whenever they want.
It's likely they occasionally make a loss on some users but in general they are highly profitable for AI companies:
> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025
and
> OpenAI projected a gross margin of around 46% in 2025, including inference costs of both paying and nonpaying ChatGPT users.
You can pick models that are snappy, or models that are as capable as SOTA. You don't really get both unless you spend extremely unreasonable amounts of money on what is essentially a datacenter-scale inference platform of your own, meant to service hundreds of users at once. (I don't care how many agent harnesses you spin up at once, you aren't going to get the same utilization as hundreds of concurrent users.)
This assessment might change if local AI frameworks start working seriously on support for tensor-parallel distributed inference, then you might get away with cheaper homelab-class hardware and only mildly unreasonable amounts of money.
If you can run free models on consumer devices why do you think cloud providers cannot do the same except better and bundled with a tone of value worth paying?
I don’t think OP’s point has anything to do with AI companions.
The big benefit of moving compute to edge devices is to distribute the inference load on the grid. Powering and cooling phones is a lot easier than powering and cooling a datacenter
Local ai is probably a good direction, i agree. But there was a part of their point that had to do with ai companions: the bit where they say we are closer to “her”-like ai companions. That was the bit i was responding to.
I get the local ai thing. I agree it’s probably a good direction. The bit that has to do with the movie “her” is the bit at the end where they are excited about “her”-like companions on our phones.
You can’t steal pixels or frequencies. But you can use someone’s image or their voice to sell your product without their permission.
You can get all existential about it if you want - I just know that if someone used my face or my voice to shill for a product without my permission i’d be pissed. I’m pretty sure you would be too.
I find it odd they are using the term “edge” to brand this, if it’s target is the general public.
I’ve been to a few tech conferences and saw the term used there for the first time. It took me a little bit to see the pattern and understand what it meant. I have never heard the term used outside of those circles. It seems like “local” would be the term average users would be familiar with. Normal people don’t call their stuff “edge devices”.
It's not - Apple is working with Google right now to make Siri into the public-facing version of this. This is kinda just the tech preview before all the branding has been painted on.
From app developer and user,
My main concern for now is bloating devices. Until we’ll have something like Apples foundation model where multiple apps could share the same model it means we have something horrible as Electron in the sense, every app is a fully blown model (browser in the electron story) instead of reusing the model.
With desktops we have DLL hell for years. But with sandboxed apps on mobile devices it becomes a bigger issue that I guess will/should be addressed by the OS.
For my app I’ve been trying to add some logic based on large model but for bloating a simple Swift app with 2-3GB of model or even few hundred MBs feels wrong doing and conflicting with code reusability concepts.
My son just started using 2B on his Android. I mentioned that it was an impressively compact model and next thing I knew he had figured out how to use it on his inexpensive 2024 Motorolla and was using it to practice reading and writing in foreign languages.
Nice! Tried on iPhone 16 pro with 30 TPS from Gemma-4-E2B-it model.
Although the phone got considerably hot while inferencing. It’s quite an impressive performance and cannot wait to try it myself in one of my personal apps.
It's strange that my iPhone 14 is at regular temperature when using the E2B model. But also it's a lot slower (not sure how to measure the exact tokens per second, ~12 if I had to guess)
It's at least somewhat limited in non-English content. It knows how to make lentil soup, so I was happy that I never need to look up recipe sites with awful UX and ads, but then it couldn't find a recipe for "Kalter Hund"/"Kalte Schnauze". So sad ;)
Still, absolutely fabulous. What a time to be alive!
It doesn’t render Markdown or LaTeX. The scrolling is unusable during generation. E4B failed to correctly account for convection and conduction when reasoning about the effects of thermal radiation (31b was very good). After 3 questions in a session (with thinking) E4B went off the rails and started emitting nonsense fragment before the stated token limit was hit (unless it isn’t actually checking).
Is it me or does the App Store website look... fake? The text in the header ("Productiviteit", "Alleen voor iPhone") looks pixelated, like it was edited on Paint, the header background is flickering, the app icon and screenshots are very low quality, the title of the website is incomplete ("App Store voor iPho...")
It looks like there is some sort of glow effect on the text that isn't rendering right on your browser? It arguably doesn't have the best contrast, but seems to be as intended in Safari 26.3. Looks similar on Chrome macOS too: https://imgur.com/yq5PrKm.
These new models are very impressive. There should be a massive speedup coming as well, AI Edge Gallery is running on GPU, but NPUs in recent high end processors should be much faster. A16 chip for example (Macbook Neo and iphone 16 series) has 35 TOPS of Neural Engine vs 7 TFLOPS gpu. Similar story for Qualcomm.
The Apple Silicon in the MacBook Neo is effectively a slimmed down version of M4, which is already out and has a very similar NPU (similar TFLOPS rating). It's worth noting however that the TFLOPS rating for Apple Neural Engine is somewhat artificial, since e.g. the "38 TFLOPS" in the M4 ANE are really 19 TFLOPS for FP16-only operation.
My iPhone 13 can’t run most of these models. A decent local LLM is one of the few reasons I can imagine actually upgrading earlier than typically necessary.
I’ve got a 17 pro and tbh I haven’t found any use for local models yet. They are a neat curiosity but the online ones are absolutely massively far ahead. Considering they are being given away for free currently, it’s hard to justify not making use of them over dumber local models.
It's so ridiculous that Google made a custom SoC for their phones, touting its AI performance, even calling it Tensor, and Apple is still faster at running Google's own model.
Google really ought to shut down their phone chip team. Literally every chip from them has been a disappointment. As much as I hate to say it, sticking with Qualcomm would have been the right choice.
If this Gemma tokenizer I found online is accurate then my Pixel 10 Pro XL is getting ~22 tok/s on Gemma 4 E2B using the NPU, vs. 40 tok/s is what people are saying the MLX version gets on iPhone.
Actually I found official performance numbers from Google saying iPhone gets 56 tok/s and Qualcomm gets 52. They don't even bother listing Tensor in their table. Maybe because it would be too embarrassing. Ouch! https://ai.google.dev/edge/litert-lm/overview
E4B is pretty good for extracting tables of items from receipt scans and inferring categories, wish this could be called from within a shortcut to just select a photo and add the extracted table to the clipboard
Still didnt release training recipe, data, methodology etc unlike deepseek. Mostly released to get developer ecosystem across their android built in ai. Still good and interesting, but not exactly philanthropic to the open source progress.
Gemma 4 E4B is an incredible model for doing all the home assistant stuff I normally just used Qwen3.5 35BA4B + Whisper while leaving me with wayy more empty vram for other bullshit. It works as a drop in replacement for all of my "turn the lights off" or "when's the next train" type queries and does a good job of tool use. This is the really the first time vramlets get a model that's reliably day to day useful locally.
I'm curious/worried about the audio capability, I'm still using Whisper as the audio support hasn't landed in llama.cpp, and I'm not excited enough to temporarily rewire my stuff to use vLLM or whatever their reference impl is. The vision capabilities of Gemma are notably (thus far, could be impl specific issues?) much much worse than Qwen (even the big moe and dense gemma are much worse), hopefully the audio is at least on par with medium whisper.
I recently got to a first practical use of it. I was on a plane, filling landing card (what a silly thing these are). I looked up my hotel address using qwen model on my iPhone 16 Pro. It was accurate. I was quite impressed.
After some back and forth the chat app started to crash tho, so YMMV.
I don't normally do the whole "abliterated" thing (dealignment) but after discovering https://github.com/p-e-w/heretic , I was too tempted to try it with this model a couple days ago (made a repo to make it easier, actually) https://github.com/pmarreck/gemma4-heretical and... Wow. It worked. And... Not having a built-in nanny is fun!
It's also possible to make an MLX version of it, which runs a little faster on Macs, but won't work through Ollama unfortunately. (LM Studio maybe.)
Runs great on my M4 Macbook Pro w/128GB and likely also runs fine under 64GB... smaller memories might require lower quantizations.
I specifically like dealigned local models because if I have to get my thoughts policed when playing in someone else's playground, like hell am I going to be judged while messing around in my own local open-source one too. And there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
Note: I tried to hook this one up to OpenClaw and ran into issues
To answer the obvious question- Yes, this sort of thing enables bad actors more (as do many other tools). Fortunately, there are far more good actors out there, and bad actors don't listen to rules that good actors subject themselves to, anyway.
[1] https://github.com/jundot/omlx
I checked the abliterate script and I don't yet understand what it does or what the result is. What are the conversations this enables?
In my experience the latest batch of models are a lot better at transcribing the text verbatim without moralizing about it (i.e. at "understanding" that they're fulfilling a neutral role as a transcriber), but it was a really big issue in the GPT-3/4 era.
My prompt states that their job is to extract the text exactly as it appears in the PDF. One data point to be extracted is the race of each person listed. In one case, someone's race was "Indian". Gemini decided to extract it as "Native American". So ridiculous.
Basically anything that showed any “skin” on a mannequin it would refuse to interact with. Even just a top, unless she put pants on the mannequin.
It was infuriating.
In my experience, though, it's necessary to do anything security related. Interestingly, the big models have fewer refusals for me when I ask e.g. "in <X> situation, how do you exploit <Y>?", but local models will frequently flat out refuse, unless the model has been abliterated.
But it does refuse being critical of the usual topics: israel, islam, trans, or race.
So wanting to discuss one of those is the real reason people would use an uncensored model.
Of course, humans are also impacted by these things, at best we can be a little deliberate about rejecting a few of the more on-the-nose examples.
2) Asking questions about sketchy things. Simply asking should not be censored.
3) I don't use it for this, but porn or foul language.
4) Imitating or representing a public figure is often blocked.
5) Asking security-related questions when you are trying to do security.
6) For those who have had it, people who are trying to use AI to deal with traumatic experiences that are illegal to even describe.
Many other instances.
When’s the last time you tried this? ChatGPT and Gemini have no trouble responding with all the common criticisms of Islam.
Or indeed an incompetent but enthusiastic helper accidentally getting them to posion themselves and friends with botox:? https://news.ycombinator.com/item?id=40724283
That is why they were pushed away from this. At least with vibe coded software, errors may prevent compilation, then when we're past that simply bad experiences, before they become human catastrophes.
I guess there are things it's better at?
You need the LLM to be able to respond with tool use requests, and then your local harness to process them and respond to it. You can read how tool calling works with eg Claude API to get the idea: https://platform.claude.com/docs/en/agents-and-tools/tool-us...
Under the hood something like Claude Code is calling the API with tools registered, and then when it gets a tool use request it runs that locally, and then responds to the API with the result. That’s the loop that enables coding.
Integrating with an IDE specifically is really just a UI feature, rather than the core functionality.
I'm not sure if I can make the 35B-A3B work with my 32GB machine
You won't have much RAM left over though :-/.
At Q4, ~20 GiB
https://huggingface.co/unsloth/Qwen3.5-35B-A3B-GGUF
So far gemma 4 seems excellent at role playing, document analysis, and decent at making agentic decisions.
On Android the sandbox loads an index.html into a WebView, with standardized string I/O to the harness via some window properties. You can even return a rendered HTML page.
Definitely hacked together, but feels like an indication of what an edge compute agentic sandbox might look like in future.
Mind giving us a few of the examples that you plan to run in your local LLM? I am curious.
https://news.ycombinator.com/item?id=47654013
Not to mention that doing what the big model makers do literally dumbs the model down.
They should at least allow something like letting you prove your age and identity to give you access to better/unaligned models, maybe even requiring a license of some sort. Because you know what? SOMEONE in there absolutely has access to the completely uncensored versions of the latest models.
I just made a real-time AI (audio/video in, voice out) on an M3 Pro with Gemma E2B. I posted it on /r/LocalLLaMA a few hours ago and it's gaining some traction [0]. Here's the repo [1]
I'm running it on a Macbook instead of an iPhone, but based on the benchmark here [2], you should be able to run the same thing on an iPhone 17 Pro.
[0] https://www.reddit.com/r/LocalLLaMA/comments/1sda3r6/realtim...
[1] https://github.com/fikrikarim/parlor
[2] https://huggingface.co/litert-community/gemma-4-E2B-it-liter...
Show HN: Real-time AI (audio/video in, voice out) on an M3 Pro with Gemma E2B - https://news.ycombinator.com/item?id=47652007
[0] https://github.com/google-ai-edge/LiteRT-LM
1) I am able to run the model on my iPhone and get good results. Not as good as Gemini in the cloud, but good.
2) I love the “mobile actions” tool calls that allow the LLM to turn on the flashlight, open maps, etc. It would be fun if they added Siri Shortcuts support. I want the personal automation that Apple promised but never delivered.
3) I am so excited for local models to be normalized. I build little apps for teachers and there are stringent privacy laws involved that mean I strongly prefer writing code that runs fully client-side when possible. When I develop apps and websites, I want easy API access to on-device models for free. I know it sort of exists on iOS and Chrome right now, but as far as I’m aware it’s not particularly good yet.
It’s very impressive that this can run locally. And I hope we will continue to be able to run couple-year-old-equivalent models locally going forward.
It's their E2B and E4B variants (so 2B and 4B but also quantized)
https://ai.google.dev/gemma/docs/core/model_card_4#dense_mod...
So much so that this was what made Apple increase their base sizes.
The latter option will only bemusedly for tasks that humans are more expensive or much slower in.
This Gemma 4 model gives me hope for a future Siri or other with iPhone and macOS integration, “Her” (as in the movie) style.
Why? It's widely understood that the big players are making profit on inference. The only reason they still have losses is because training is so expensive, but you need to do that no matter whether the models are running in the cloud or on your device.
If you think about it, it's always going to be cheaper and more energy-efficient to have dedicated cloud hardware to run models. Running them on your phone, even if possible, is just going to suck up your battery life.
This is most definitely not widely understood. We still don't know yet. There's tons of discussions about people disagreeing on whether it really is profitable. Unless you have proof, don't say "this is widely understood".
We need to see the cash flows.
Also while datacenter-based scaleout of a model over multiple GPUs running large batches is more energy efficient, it ultimately creates a single point of failure you may wish to avoid.
If you add in the cost of training, it’s not profitable.
Not including the cost of training is a bit like saying the only cost of a cup of coffee is the paper cup it’s in. The only way OpenAI gets to charge for inference is by selling a product people can’t get elsewhere for much cheaper, which means billions in R&D costs. But because of competition, each model effectively has a “shelf life”.
Obviously that doesn’t help them turn a profit, until they can stop growing training costs exponentially.
So it’s really a race to see whether growth in revenue or training costs decelerates first.
Vast amounts of capital have been poured in, but they continue to raise more. Presumably because they need more.
Is the capital being invested without any expectation of ROI?
I love the whole “they are making money if you ignore training costs” bit. It is always great to see somebody say something like “if you look at the amount of money that they’re spending it looks bad, but if you look away it looks pretty good” like it’s the money version of a solar eclipse
But if they're losing money on inference, they will lose more money when people use their services more. There's no way to turn that around at that price.
Are they? Or are they just saying that to make their offerings more attractive to investors?
Plus I think most people using agents for coding are using subscriptions which they are definitely not profitable in.
Locally running models that are snappy and mostly as capable as current sota models would be a dream. No internet connection required, no payment plans or relying on a third party provider to do your job. No privacy concerns. Etc etc.
Where on earth do people get this idea? Subscriptions that are based around obscure, vendor defined "credits" are the perfect business model for vendors. They can change the amount you can use whenever they want.
It's likely they occasionally make a loss on some users but in general they are highly profitable for AI companies:
> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025
and
> OpenAI projected a gross margin of around 46% in 2025, including inference costs of both paying and nonpaying ChatGPT users.
https://archive.is/aKFYZ#selection-1075.0-1083.119
This assessment might change if local AI frameworks start working seriously on support for tensor-parallel distributed inference, then you might get away with cheaper homelab-class hardware and only mildly unreasonable amounts of money.
It may be physically "local" but not in spirit.
Seriously????
The big benefit of moving compute to edge devices is to distribute the inference load on the grid. Powering and cooling phones is a lot easier than powering and cooling a datacenter
That’s like using someone’s face in an app and then saying “how can you steal pixels?”
Or rather, what does “ownership” mean? What does it mean to own light waves? What does it mean to own sound waves? Etc
You can get all existential about it if you want - I just know that if someone used my face or my voice to shill for a product without my permission i’d be pissed. I’m pretty sure you would be too.
Also on Android: https://play.google.com/store/apps/details?id=com.google.ai....
It's a demo app for Google's Edge project: https://ai.google.dev/edge
I’ve been to a few tech conferences and saw the term used there for the first time. It took me a little bit to see the pattern and understand what it meant. I have never heard the term used outside of those circles. It seems like “local” would be the term average users would be familiar with. Normal people don’t call their stuff “edge devices”.
It's not - Apple is working with Google right now to make Siri into the public-facing version of this. This is kinda just the tech preview before all the branding has been painted on.
From app developer and user, My main concern for now is bloating devices. Until we’ll have something like Apples foundation model where multiple apps could share the same model it means we have something horrible as Electron in the sense, every app is a fully blown model (browser in the electron story) instead of reusing the model.
With desktops we have DLL hell for years. But with sandboxed apps on mobile devices it becomes a bigger issue that I guess will/should be addressed by the OS.
For my app I’ve been trying to add some logic based on large model but for bloating a simple Swift app with 2-3GB of model or even few hundred MBs feels wrong doing and conflicting with code reusability concepts.
Although the phone got considerably hot while inferencing. It’s quite an impressive performance and cannot wait to try it myself in one of my personal apps.
Still, absolutely fabulous. What a time to be alive!
The design quality is still poor. But that's the new Apple. Design is no longer one of their core strengths.
If you just go to https://apps.apple.com/ it does look better, but I agree, still a bit "off".
On my iPhone it opens on the App Store app, so it looks fine to me.
Screenshot of the header: https://i.imgur.com/4abfGYF.png
Edit: Seems like mix-blend-mode: plus-lighter is bugged in Firefox on Windows https://jsfiddle.net/bjg24hk9/
I’m sure very fast TPUs in desktops and phones are coming.
> We collect information about your activity in our services
Source: https://policies.google.com/privacy#infocollect
[0] https://github.com/google-ai-edge/gallery
Saw this one on X the other day updated with Gemma 4 and they have the built-in Apple Foundation model, Qwen3.5, and other models:
Locally AI - https://locallyai.app/
remember, megacorps are dying for infinite amounts of analytics data
Google really ought to shut down their phone chip team. Literally every chip from them has been a disappointment. As much as I hate to say it, sticking with Qualcomm would have been the right choice.
Actually I found official performance numbers from Google saying iPhone gets 56 tok/s and Qualcomm gets 52. They don't even bother listing Tensor in their table. Maybe because it would be too embarrassing. Ouch! https://ai.google.dev/edge/litert-lm/overview
I assume it is the 26B A4B one, if it runs locally?
I'm curious/worried about the audio capability, I'm still using Whisper as the audio support hasn't landed in llama.cpp, and I'm not excited enough to temporarily rewire my stuff to use vLLM or whatever their reference impl is. The vision capabilities of Gemma are notably (thus far, could be impl specific issues?) much much worse than Qwen (even the big moe and dense gemma are much worse), hopefully the audio is at least on par with medium whisper.
After some back and forth the chat app started to crash tho, so YMMV.
https://github.com/a-ghorbani/pocketpal-ai
https://apps.apple.com/us/app/pocketpal-ai/id6502579498
https://play.google.com/store/apps/details?id=com.pocketpala...