I actually can’t wait for the future where I upgrade hardware in order to upgrade my ai as an alternative to an expensive subscription.
There are many problems I want to work on which require billions of tokens. These are completely inaccessible without corporate project sponsorship at the moment. An asic generation machine which can pump out a few 10s of thousands of tokens per second at opus4.6 quality is more than sufficient.
Couldn't try it because the demo app is iOS only and the web version just crashes my browser. The small model is impressive but if you front load a 1.8GB text encoder model, the savings aren't quite as useful.
what trade off would one need to clear to justify the hardware and the work to get this running locally as part of a broader system? It’s a lot of work setting up and maintaining a production harness/system on a local device. I don’t personally repeatedly generate images at a scale where using a lab’s app somehow burns all my tokens. I like the ideas of local ai but I don’t see widespread adoption of it happening in commercial or customer situations anytime soon no matter how little/good enough they get. Even Uber- token burn whiplash but I doubt their answer will be “run some of it local”. IT nightmare, I’d imagine.
The white paper says "mean-active memory pressure down to 1.95 GB for 1-bit Bonsai Image 4B and 2.38 GB for
Ternary Bonsai Image 4B". Storage is on the linked page, and is about half that.
That is very low, looks like it should run in base MacMini M4 with 16GB RAM. I understand it is not released yet? What sort of harness is necessary for this type of model? (I have only used coding agents through GH Copilot in VS Code, the JetBrains AI tool and Pi, this last one was sort of a pain to setup…)
The online demos require WebGPU so Firefox on mobilr and privacy enhanced browsers will break. WebGPU support on Linux and other open source systems is also trash, you can force it to work in Chrome but it won't be happy.
I believe it's the way the HN algorithm works. In order to give new and obscure posts a shot, it will add them to peoples feeds in their front page and see how they measure. Otherwise new posts wouldn't get seen and the flywheel would never get started.
So everyone acts as a sort of beta tester for obscure posts.
On weekends, yes. During the week, that’s also true if they arrive within a short time frame, e.g., three minutes. Almost no one looks at “New”. That is the real issue.
It’s about how quickly they get those points. It doesn’t have to be bots. Sending a post to friends with reputable human profiles, and asking for a vote kinda works of most social networks. Some social networks claim they have protection against this but I wouldn’t bet they catch everything.
Isn't SD XL 3.5B? And the refiner model is even larger. Those can run on an iPhone 13 Pro.
There are many problems I want to work on which require billions of tokens. These are completely inaccessible without corporate project sponsorship at the moment. An asic generation machine which can pump out a few 10s of thousands of tokens per second at opus4.6 quality is more than sufficient.
I do wonder how these compare to existing image generation models. I've tried https://github.com/alichherawalla/off-grid-mobile-ai for a while but I find the image generation models rather lacking.
So everyone acts as a sort of beta tester for obscure posts.
Is it compatible with Ollama, ComfyUI or are those providers unneeded, compatible with low-end hardware?
Also, where does "./setup.sh/ drop the components in Linux?
Thank you, Sol
having trouble loading the webgl browser demo on my phone but no biggy