Efficient AI you can feel better using.
Most AI models are trying too hard. Ours are cool, calm, and collected.
- Its memory never grows. Eating the world's RAM does not make for actually intelligent systems.
- Twice the input isn't four times the work. For everyone else, it is. And it's holding back what AI could be!
- It runs on hardware you already own. No exotic chips, no special systems code. Doing more with less is the way.
Well, there’s your problem.
We put the modern AI model up on the bench and took it apart. Every fault we fixed uncovered the next one.
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“Every word has to look at every other word.”
Double the text and you quadruple the work. That is the bill the whole industry is paying, and it is why they need a data centre just to say hello.
So we capped it. A cheaper way to pay attention - one that grows in a straight line instead of a steep slope.
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“Now it forgets everything.”
Cheap attention only really remembers what it just read. It loses the thread. We had traded one problem for another.
Not good enough. A model that forgets that much cannot solve for anything. Back to the bench, fearlessly!
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“Can we give it a form of memory?”
Only by keeping the shape of everything it read. The way a musician might remember a song in chords rather than a chain of notes.
That idea comes from physics. And it is what makes the memory constant instead of endlessly growing.
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“And that changes the nature of the problem.”
Once you are thinking in shapes, you are not tied to the grid. Once you are free from the grid, that is the definition of efficient, general purpose AI architecture attained.
There’s the win. That is why it runs anywhere, on hardware you already own, with no exotic systems code.
We stopped treating language like a picture.
We started treating it like sound.
Our architecture blueprint is called FELA. It isn’t a one model trick... it’s a way of thinking about AI architecture. We applied the same lessons to build models that win in 14 other contexts.
"Hyperscaler"? Or Hype and Failure?
Our ultimate goal is to stop the waste and environmental pressure resulting from hyperscaling. We believe we can produce models that have more than enough power, are private, profitable, and don't burn the planet.
What hyperscalers are doing can't work! At Lowdown Labs we are solving the problems.
AI for good, for real.
Join us on our journey to build a radically better future.