Lola Vision Systems Wants to Make Running AI Models on Chips Easier
A Bet on Where AI Computing Is Headed
The story begins nearly 12 years ago, when Tayo Adesanya started a career working with microchips and AI processors. He mainly helped large manufacturers decide which chips to use in their hardware. Those years, he told TechCrunch, gave him early insight into where demand in the AI computing market was headed.
"Starting Lola Vision Systems was a bet on where the world was headed and what I was seeing," he said.
In 2024, he launched Lola Vision Systems, an AI infrastructure company that builds software and chips for running AI models on devices. Its core product is software that translates AI models into instructions a specific chip can run. Adesanya calls this software a "compiler toolchain," and he says it is a massive bottleneck: manually setting up an AI model on new hardware can take "roughly 200 hours" just to begin testing.
Rebuilding the Software Layer
Lola Vision says it has rebuilt that software layer and is also developing its own semiconductor chips, with the goal of automating more of the process. A client provides its code and the AI model it wants to useβwhether custom-built or open sourceβand the software translates both into instructions the client's chip can execute.
"Speed is only part of it," Adesanya said. He explained that faster setup gives aerospace and "other mission-critical companies" time to "run more accurate models on their own data, at a lower power."
Image Credits: Tayo Adesanya
"For these customers," he said, "accuracy and reliability aren't nice to have. They determine whether a product passes regulatory review and whether it works reliably in the field."
An Alternative to Nvidia's Dominance
Lola Vision, based in Washington, D.C., is one of several startups trying to offer an alternative to Nvidia's technology for running AI on devices. Right now, Adesanya said, many companies start with Nvidia's Jetson, a line of compact computing modules for running AI on devices, or with open-source AI models. Adesanya claimed these "often break or run poorly out of the box, so teams spend days or weeks getting them to run at all, then even more weeks debugging until the models are usable."
"Even then," he continued, "power consumption often blows edge computing budgets, or the board can't deliver enough compute for the medium to large models the product actually needs to run successfully. This leads to the recognition models lagging behind targets or misreading objects."
Edge computing means running AI directly on a device, such as a camera or drone, rather than in a remote data center. Recognition models are AI systems that identify objects.
According to the company, a dozen corporate customers are already using its technology. As of 2026, the demand for on-device AI has only accelerated, with edge inference becoming a critical battleground for chipmakers and startups alike.