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

AI infrastructure company Infinity announced a $15 million raise at a $100 million valuation on Monday from investors including Touring Capital, Principal VC, and researchers from companies such as OpenAI and Anthropic. The startup is building software to make it easier for AI chips to run AI models. One big reason Nvidia became the top player is not just its high-performance chips, but also its CUDA software (Compute Unified Device Architecture), which allows its GPUs (originally designed to run graphics) to act as general-purpose processing CPUs. The largest AI development frameworks PyTorch and TensorFlow have been built on top of CUDA. This allows developers to write their apps in popular languages like Python, use those major AI frameworks and their apps will, by default, run on Nvidia chips.

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.