Luminal, an AI infrastructure company based in San Francisco, has raised $25 million in Series A funding led by Standard Capital.
In a statement on Thursday, Luminal said the money will fund more engineering and go-to-market work as it adds support for more AI models and chip platforms. The company emphasized that all of its seed investors, such as Y Combinator, Felicis, SagaVC, and Liquid 2, doubled down on investment.
Luminal builds a search compiler and an inference cloud that help AI models run on GPUs and application-specific integrated circuits (ASICs). The compiler tests mathematically equivalent ways of running a model on a given chip and picks the fastest. Once a model is optimised for a chip, that configuration can be reused, making the search worthwhile across repeated workloads.
The inference cloud lets developers deploy AI models without managing the infrastructure underneath. Together, the two products let AI companies use different hardware platforms and cut the cost of running models, Luminal said.
Much of the industry depends on Nvidia’s GPU ecosystem, while optimizing models for other chips often takes specialist engineering and extensive tuning. Luminal’s approach can widen the hardware choices available to AI companies, beyond Nvidia’s ecosystem, without forcing them to give up speed or cost. It puts the opportunity at $5 trillion in AI infrastructure.
Joe Fioti, co-founder and CEO of Luminal, said the company began by building in the open on GitHub as AI adoption grew. As the Luminal sees strong demand for a more efficient way to run models across different hardware, Joe emphasized that the cost of running AI should not limit who can build it.
Since launching its public cloud after a $5.3 million seed round in November 2025, Luminal has reached 90 trillion tokens processed a month within four months. A large share of Luminal’s tokens runs on Amazon’s Trainium chips, under a deepening partnership with Amazon.
Luminal says the Series A will fund development of its compiler and inference cloud, wider model and hardware support, and expansion of its go-to-market effort.
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