Desert Ant Labs launches on-device AI models that beat cloud APIs
Desert Ant Labs: local, fast models that run on device
Desert Ant Labs, a European AI lab, launches 18 on-device models for audio, vision, and text, designed to run on phones and laptops. Models like Voz transcribe 10 minutes of audio in 2 seconds (4.7x faster than Whisper), and Redact masks personal data in real time across 27 languages. All models are free up to 100k monthly active devices, with a single SDK for Swift, Kotlin, and JavaScript.
There's more compute available in people's hands than in every AI data center on earth.
- 1dom
This is a cool way of approaching AI models. I'm a big fan of local LLMs, local specific models like this should be even more powerful.
> Every model is free up to 100k monthly active devices. No tokens, no logins.
I dunno about the business model though. Cloud LLM billing makes sense: you're getting another computer to do work with each request, and using their compute via their gateway that they bill you.
These local models are like old school software. They're producing the weights, and then giving them to people. If I'm happy with the weights you've given me, and I'm not using your compute for inference and not wanting or needing any updates off you, why should you continue getting money off me and my customers?
The whole "but we need to keep it updated for your security" doesn't really work as well for software designed to run fully offline like these local models are.
I'm not saying they shouldn't get paid, but I guess I feel a personal sadness that it's less obvious how to successfully monetise such a sincerely useful and beneficial approach towards AI models.
- momojo
> The world ships more than a billion capable phones, tablets, and laptops a year, most with a chip built for exactly this work, paid for and idle most of the day. Run the model there and the economics flip: no per-call cost, no round-trip, and nothing leaves the device.
This. I run small models (>50MB) for bio-imaging/biotech applications, it feels like every README implies that you need a discrete GPU to get started. While some do, many, especially the most useful ones, do not. Sure it matters if you're also going to do fine-tuning, but I believe your typical user just wants to detect some nuclei and get some cell-body ratios.
The laptop on your desk won't be running Meta's SAM, but it has more than enough compute to crunch 100's of your H&E slides overnight.
- shelled
I recently had to use dictation for a few weeks and I was pleasantly surprised that many of the apps (in use/vogue) did support models on my 2021 16GB M1 Pro mac (many of those even supported connecting to a remote or local model endpoint) and at the same time for any worthwhile STT enhancement the model size was hitting higher I would have comfortably wanted. Even though I don't necessarily need dictation any more I intend to keep a custom fully offline setup and try these models (not sure they support live/streaming STT).
If any of you are interested there are apps like https://github.com/altic-dev/FluidVoice (this one's a great app) and this https://sam-pop.github.io/WhisperDictation. The latter, even though it has just 7 stars right now, seems to be more "intuitive". I just hope they expose a way to "connect" to available models on the machine or remotely)
- nater5000
I definitely think there's a lot to be done with small models dedicated to specific tasks. I've always thought the REAL value is in having large models be able to easily build small models for custom tasks (which I know is kind of a thing), but perhaps just providing the small models directly is the more accessible approach.
>accessible via one SDK for Swift, Kotlin, and JavaScript
Lol well let me know when there's a Python SDK and I'll give it a try then. Obviously this isn't a deal breaker if you have a real case, but as someone who is willing to spin something up and try it out if there's a quick "pip install" command, this is getting put back on the shelf for now.
- sipjca
at first i got very excited about a new fast transcription model (voz) but turns out its just parakeet v3 with some new inference code which is macOS/iOS specific