What Is OpenVINO?

OpenVINO is an open-source toolkit from Intel for optimizing AI models and running inference on Intel hardware. The name stands for Open Visual Inference and Neural network Optimization, a reminder that it began in 2018 as a computer vision toolkit. It now covers vision, speech, language and generative AI models, and it is released under the Apache 2.0 license.

OpenVINO does not train models. It takes a model that has already been trained in a framework such as PyTorch or TensorFlow, converts and compresses it, then runs it as fast as the target hardware allows. That matters at the edge, because it lets an ordinary Intel processor do useful AI work without a separate accelerator, keeping cost, power and heat down in compact embedded systems. Our explainer on inference vs training covers the difference between the two workloads.

A typical OpenVINO workflow has four stages, each handled by a part of the toolkit.

OpenVINO uses a plugin for each type of device. The same compiled model can target any of them, and the AUTO setting lets the runtime pick a device or start on the CPU while a faster device loads.

Our Intel Core Ultra computers carry all three on-chip engines, so one processor can split a pipeline between CPU, iGPU and NPU. Our iGPU vs dGPU comparison explains when integrated graphics are enough.

Most of the speed gain at the edge comes from quantisation. Converting a model from 32-bit floating point to 8-bit integers makes it about a quarter of the size and lets the hardware process more values per clock, usually with only a small loss of accuracy. NNCF can do this after training with a small calibration dataset, or during training for models that are sensitive to precision. Our guide to INT8 quantization explains the trade-off in more detail.

Always check accuracy after quantising. Run the INT8 model on a held-back set of real images from the site and compare the results with the original model. If a few layers lose too much precision, NNCF can keep them in higher precision while the rest of the network runs in INT8, which usually recovers most of the accuracy for little loss of speed.

OpenVINO installs from the usual package managers, including pip for Python, and runs on Windows and Linux. Intel publishes a large set of Jupyter notebooks that walk through converting and running common vision, speech and language models, and Hugging Face’s Optimum Intel library exports many published models to OpenVINO in a single step.

Before choosing hardware, the benchmark_app tool that ships with OpenVINO measures throughput and latency for a model on each available device. Running it on the CPU, iGPU and NPU of a candidate processor shows quickly which engine suits the workload, and whether quantising to INT8 is worth the effort. It is a cheap test to run on a sample system before ordering a fleet.

OpenVINO is common wherever AI has to run on x86 hardware that is already in place, or where a discrete GPU would add too much power or heat. Typical industrial uses include defect inspection on production lines, people and vehicle counting, license plate recognition, retail analytics, medical imaging and speech interfaces on kiosks. The COM Express reverse vending case study runs OpenVINO on Iris Xe graphics to recognize returned containers. Because the same model runs across Intel CPUs, GPUs and NPUs, one build can be deployed on everything from a small fanless PC to a rack server, which simplifies maintenance across a fleet of edge devices.

We build Intel-based edge AI computers with OpenVINO, drivers and NPU support installed as standard, and can validate your model on the target processor before the system ships.