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.
> 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?
I definitely understand the appeal of the desire to “buy it once”, but I think there are a few issues:
- almost no software is static. Look at a package like python Requests and even though it does the simplest thing and has barely changed from a user perspective, it gets updated all the time. This is true for most software. This is doubly true for something like local AI models where both the software and the hardware are changing constantly. Subscriptions motivate sellers to keep their software up to date.
- If I’m an app developer, the idea that I can try something out for $X/month is very appealing versus making an upfront investment of (let’s say) $X*20. This is doubly true for something like local models where I will almost certainly want something new when the models improve.
- To add to the first point, I work at a startup. No one asks questions when I want $20/month licenses. But let’s say I want something that’s gonna be in the 5 figure range annually. If I go to my CFO and ask for $50k upfront and then we implement something and the project fails, I look like an idiot. If I ask for $2000/mo budget for something and then we try it for two months and it fails, no one cares. Subscriptions are just safer in this sense.
How is it not obvious and fair that they are asking you to pay them when you see success (defined as >100K MAU)? How more aligned can you and them be besides this?
> 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?
Because they own the IP and they get to decide the terms of how it’s licensed.
This is like a company taking open source software, saying they like the code the community has given them, and asking why they should continue having to respect the terms of the license after downloading the code. The availability of the software (or models) does not equal a free license to use as you please.
A license allowing 100K devices for free is very generous. The businesses selling more than 100K units of anything will be significant operations. It’s fair that they’re asked to contribute financially.
> why should you continue getting money off me and my customers?
Welcome to the concept of fair market value. Less snarkily you're conflating the concepts of price and cost; theyre not the same thing and theyre not the same for you or the seller.
I love this idea and hope to see more on-device models. How do they make money, though?
I tried out their demo for Clear, the audio quality improvement model.[0] I'm not sure if it's just I don't have refined enough an ear or their demo is broken, but the "raw" and "enhanced" versions sounded exactly the same to me.
The underlying model (DFNet3) is not particularly great but it is very small and fast - imo the best commercially usable denoising model is MossFormer2 (no affiliation - it's just excellent) with one drawback in that it can't remove reverb.
Nvidia's RE-USE model can do what MossFormer2 does _and_ can remove reverb, but it is non-commercial licensed.
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
A lot of the models would be useful in a web context, to improve on the CMS we're making for clients. But they look like most of them are iOS only, few have a node package or something other, and all the benchmark are running it on modern iPhones so I doubt it would be that fast on a 20$ VPS.
This is a cool idea. The most useful one for me would be something that can process pdf files into a json schema. Title and tag generation from a post would also be useful. I'm interested in web app though.
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.
> 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?
I definitely understand the appeal of the desire to “buy it once”, but I think there are a few issues:
- almost no software is static. Look at a package like python Requests and even though it does the simplest thing and has barely changed from a user perspective, it gets updated all the time. This is true for most software. This is doubly true for something like local AI models where both the software and the hardware are changing constantly. Subscriptions motivate sellers to keep their software up to date.
- If I’m an app developer, the idea that I can try something out for $X/month is very appealing versus making an upfront investment of (let’s say) $X*20. This is doubly true for something like local models where I will almost certainly want something new when the models improve.
- To add to the first point, I work at a startup. No one asks questions when I want $20/month licenses. But let’s say I want something that’s gonna be in the 5 figure range annually. If I go to my CFO and ask for $50k upfront and then we implement something and the project fails, I look like an idiot. If I ask for $2000/mo budget for something and then we try it for two months and it fails, no one cares. Subscriptions are just safer in this sense.
How is it not obvious and fair that they are asking you to pay them when you see success (defined as >100K MAU)? How more aligned can you and them be besides this?
> 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?
Because they own the IP and they get to decide the terms of how it’s licensed.
This is like a company taking open source software, saying they like the code the community has given them, and asking why they should continue having to respect the terms of the license after downloading the code. The availability of the software (or models) does not equal a free license to use as you please.
A license allowing 100K devices for free is very generous. The businesses selling more than 100K units of anything will be significant operations. It’s fair that they’re asked to contribute financially.
He's not arguing about their right to license it how they please. He's arguing that someone ought to choose to do it another way.
You made up a position to argue against.
> why should you continue getting money off me and my customers?
Welcome to the concept of fair market value. Less snarkily you're conflating the concepts of price and cost; theyre not the same thing and theyre not the same for you or the seller.
I love this idea and hope to see more on-device models. How do they make money, though?
I tried out their demo for Clear, the audio quality improvement model.[0] I'm not sure if it's just I don't have refined enough an ear or their demo is broken, but the "raw" and "enhanced" versions sounded exactly the same to me.
[0] https://desertant.com/models/clear/
The underlying model (DFNet3) is not particularly great but it is very small and fast - imo the best commercially usable denoising model is MossFormer2 (no affiliation - it's just excellent) with one drawback in that it can't remove reverb.
Nvidia's RE-USE model can do what MossFormer2 does _and_ can remove reverb, but it is non-commercial licensed.
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
I was also thinking that this is almost too good to be true
A lot of the models would be useful in a web context, to improve on the CMS we're making for clients. But they look like most of them are iOS only, few have a node package or something other, and all the benchmark are running it on modern iPhones so I doubt it would be that fast on a 20$ VPS.
This is a cool idea. The most useful one for me would be something that can process pdf files into a json schema. Title and tag generation from a post would also be useful. I'm interested in web app though.
I wonder why they only support Apple platforms, citing CoreML. Doesn't Android have a similar framework, ML Kit?
Shiny layer of marketing and proprietary code on top of open models?
Voz is Parakeet 0.6B v3
Clear is DeepFilterNet 3
Ear is the language predictor from whisper-tiny
...
seems to be…