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The pitch is bold for a seed round. Infinity says it can strip away the one advantage that has kept Nvidia untouchable. It has just raised money to try.
The company closed a $15 million seed round at a $100 million valuation on Monday, it announced. Backers include Touring Capital, Principal VC, executives at major chip firms, and researchers from OpenAI and Anthropic.
The CUDA problem
To understand what Infinity is chasing, look at why Nvidia won. Its chips are quick, but its real moat is CUDA, the software layer it has built up over nearly two decades.
The big AI frameworks, PyTorch and TensorFlow, sit on top of CUDA. Write your app in Python and it runs on Nvidia hardware by default. That convenience is why Nvidia holds an estimated 80% of the data-centre AI accelerator market.
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Rival chips from AMD, Qualcomm, and AWS often match Nvidia on raw power. What they lack is the software. Porting models to new silicon means writing kernels, the low-level code that drives a chip, and few teams can afford the effort.
An agent that writes the chip code
Infinity wants to automate that work. Its AI agent, Ignition, generates, tests, and rewrites those kernels itself, and keeps tuning them as performance data comes back. Human engineers set the direction. The agent does the grind.
The results Infinity reports are striking, though they come from the company itself. Working with the chip maker d-Matrix, it says Ignition hit 92% of a new chip’s peak performance 10 hours after first touching the hardware. Within 10 days, three frontier models were running on it end to end.
On another test, Infinity says it lifted a model’s output from about 1,400 to more than 20,000 tokens a second in a single day. That would beat vLLM, a widely used open-source inference framework. The claims are not yet independently checked.
Automating invention
The founder gives the project its flavour. Jeremy Nixon is a former Google Brain researcher who created AGI House, a San Francisco hacker network that says it has spawned hundreds of startups.
Nixon told TechCrunch he is obsessed with “automated invention,” the idea that AI can be a kind of meta technology. He once built an algorithm, Omega, that invented other machine-learning algorithms and scored them in a loop. Ignition applies the same instinct to hardware.
He announced the raise on X with a flourish, hailing the arrival of AI systems that “enable, optimize and invent” the next generation of AI systems.
A crowded race, and the caveats
Infinity joins a wave of startups trying to break the CUDA lock-in. It says it already earns millions in annual recurring revenue and employs 26 people. Its business model takes a cut of the speed and cost gains it delivers, rather than a licence fee.
The caveats are real. This is a seed-stage firm with one public chip partner, and the headline benchmarks are self-reported. The prize, though, is large. As inference races to become two-thirds of all AI compute spending this year, cheaper ways to run it are worth a fortune.
“The next era of AI will be defined not just by who makes the best chip, but by who can make any chip run state-of-the-art models at blazing speeds,” Nixon said. If Ignition works as billed, the moat that made Nvidia untouchable starts to look shallower.
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