AI 분야에서 차세대 대형 기술을 찾으려는 열망은 꽤 야심 찬 이 회사의 기술이 어떻게 기존 AI 시스템을 복제할 수 있는지 처음으로 보여주는 모델 이와 관련되어 있다.
” 새로운 Un-0 모델의 출력 결과는 스테이블 거기에 도달하기 위한 인프라의 상당 부분은 여전히 구축 중 "우리는 프롬프트가 입력되고 추론 결과가 출력되는 네트워크 케이블 — 그리고 Unconventional은 이를 해결할 수 있는 몇 안 되는 프로젝트 중 하나입니다.
2012년부터 플랫폼 정책과 신기술에 중점을 두고 활동해 왔습니다.
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The drive to discover the next big thing in AI has funded some pretty ambitious projects — but one company is taking it as a chance to rebuild computing architecture from the ground up.
Led by Naveen Rao, formerly the head of AI at Databricks, Unconventional AI promises to make inference processing vastly more power efficient.
The secret weapon: a new kind of oscillator-based computer architecture.
On Thursday, the company released its first model AI — called Un-0 — an image-generation system tool that shows for the first time how the company s technology can replicate conventional AI systems.
In an accompanying new paper, the company’s research team details how they built a fully functional image-generation model using a software simulation of the new architecture — one that performs just as well as state-of-the-art diffusion models.
“This is the ‘hello world’ of a new kind of computer,” Rao told TechCrunch.
“Over the next year, you re going to start seeing some pretty interesting news around this.
” The output from the new Un-0 model is similar to that of image-generation models like Stable Diffusion or OpenAI’s GPT Image 1.
The impressive part is how it arrives at that performance.
The model is built on an oscillator-based architecture that is completely different from the chips that power conventional computing and traditional LLMs.
The advantages of the oscillator-based computing are complex, but Rao believes it will ultimately reduce power use by as much as 1,000 times.
Much of the infrastructure to get there is still being built.
The current version of Un-0 runs on a software simulation of Unconventional’s oscillator chips, but the company plans to release schematics for an actual chip soon.
From there, the plan is to build an entire inference stack from the ground up, with Unconventional AI eventually supplying compute capacity just like any other provider.
“We will build a new kind of system composed of our chips,” says Rao.
“We will run AI models there, and we will have a network cable where prompts come in and inferences go out, but it ll be done at 1/1000 of power.
” It’s a stunningly ambitious goal, particularly for a company that still counts less than 50 employees.
But given the scale of the AI buildout and the anticipated cost of meeting the growing demand for inference, it may be one of the few efforts to meet the scale of the problem.
As Rao sees it, the available supply of power will be one of the hard limits for AI in the years to come — and Unconventional is one of the few projects able to address it.
“AI scaling is hard because of energy.
It s going to be the fundamental limit in the next few years.
It’s going to be an energy-limited problem, at the end of the day,” he says.
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Russell Brandom AI Editor Russell Brandom has been covering the tech industry since 2012, with a focus on platform policy and emerging technologies.
He previously worked at The Verge and Rest of World, and has written for Wired, The Awl and MIT s Technology Review.
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