Inference startup Infinity raises $15M from Touring Capital, OpenAI and Athropic researchers

Most of these app-level startups wouldn’t have the resources or know-how to write their own kernels — the low-level software that operates chips — and port their apps to other AI chips. So Infinity is trying to build CUDA-alternative kernel software that works with any type of chip, like SRAM, GPUs, phone chips, and Systolic Arrays. Infinity is part of a new wave of startups that are attempting, product by product, to chip away at Nvidia’s market dominance.  

Infinity is attempting to build a universal inference library to run on all chips, allowing these chips to automate replicating state-of-the-art research results. 

Infinity was launched last year by Jeremy Nixon, once a researcher at Google Brain and creator of the hacker network community AGI House. Nixon told TechCrunch he decided to launch this company because he was obsessed with the idea of “automated invention” — the belief that “AI systems can actually be a meta technology.” He himself had invented a machine learning algorithm called Omega, he said, which essentially created new machine learning algorithms and automatically evaluated them in a feedback loop.  

That success got him thinking about other cases where this approach could work, and he turned to hardware, believing that automated systems could also generate the low-level code, like the kernels and so forth, needed to help run chips more effectively. 

Infinity’s AI research agent Ignition is intended to write the low-level code needed for AI inference on Nvidia-alternative chips. It tests, debugs, and measures how fast the hardware performs with the code, and automatically rewrites the code if needed to improve performance. The system is self-optimizing, meaning it continuously learns and improves itself. It also adapts to different chip architectures, regardless of proprietary designs, Nixon says. The result is what Infinity claims is a CUDA-level software stack. 

Customers include the AI chip maker (and would be Nvidia challenger) D-Matrix, and Infinity is in talks with other big chip and cloud companies, Nixon said. 

Humans are in the loop, however, providing high-level direction while the agent does more of the tedious grunt work. In one case study, the startup found the agent works much faster than a human alone, reducing what could have been a years- or months-long process to hours or days. Infinity doesn’t charge an upfront license fee; instead, it takes a cut of performance gains and cost savings, measuring changes in tokens per second.  

Right now, Infinity has 26 employees, including those in design, operations, and engineering.  

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