Running Enterprise Computer Vision on CPUs With Ultralytics YOLO26

What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure?

In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production.

Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos.

Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images.

His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking.

Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment.

Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated.

YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers.

This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations.

The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects.

He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions.

We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created.

The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical.

Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data.

Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description.

That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object.

The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing.

Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response.

For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like.

He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system.

Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me.

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