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EDGE AI · HARDWARE

iGPU vs dGPU: What Is the Difference?

An iGPU (integrated GPU) is built into the processor and shares the system memory, so it adds no extra power, heat or cost. A dGPU (discrete GPU) is a separate graphics processor with its own memory, offering far more performance but needing more power, cooling and space. For edge AI, iGPUs suit lighter inference; dGPUs suit heavy workloads.

01

The Short Answer

Modern integrated graphics, such as Intel Iris Xe and Arc, are capable enough to run vision models in compact, fanless systems, especially with toolkits like OpenVINO. When a system must run large models, many camera streams or training, a discrete GPU, or a dedicated AI platform such as NVIDIA Jetson, is the better fit.

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    Choose an iGPU For compact, low-power systems running lighter inference.
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    Choose a dGPU For heavy vision, many streams or large models.
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    Consider Jetson For high AI performance per watt in a small module.
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    Check the thermals A dGPU adds heat that fanless designs must handle.
02

Why It Matters

Our OpenVINO FAQ covers Intel’s inference toolkit, and the COM Express reverse vending case study shows an iGPU running deep learning. See our edge AI computers and NVIDIA GPUs.