US How a CPU and a GPU differ
A CPU (central processing unit) is the general-purpose processor that runs the operating system, applications and control logic. A GPU (graphics processing unit) is a processor built for parallel work, originally for drawing graphics and now widely used for video and AI. The difference between GPU vs CPU comes down to how each spends its silicon.
A CPU has a handful to a few dozen powerful cores, each with large caches, high clock speeds and logic for predicting branches and reordering instructions. That makes it fast at varied, branching tasks that must be done one step after another. A GPU has thousands of simpler cores grouped into blocks that apply the same instruction to many pieces of data at once. It is slower at any single step, but far faster when the same calculation has to be repeated across millions of pixels or numbers.
Every computer needs a CPU; a GPU is added when the workload is highly parallel. That is why AI models on camera feeds run much faster on a GPU, while the CPU keeps the rest of the system running. For the CPU itself in more depth, see our explainer on what a CPU is.
GPU vs CPU side by side
The figures below are typical of current desktop, embedded and edge hardware rather than any single model.
| Feature | CPU | GPU |
|---|---|---|
| Cores | Around 4 to 64, each powerful | Hundreds to many thousands, each simple |
| Clock speed | Typically 3 to 6 GHz | Typically 1.5 to 2.5 GHz |
| Best at | Varied, sequential and branching tasks | The same operation on large blocks of data |
| Memory | System RAM, tens of GB/s per channel | Own GDDR memory, hundreds of GB/s |
| Latency | Low for a single task | Higher per task, high total throughput |
| Typical jobs | OS, applications, databases, control loops | Graphics, video encoding, AI training and inference |
| Power in edge systems | Typically 6 to 65 W | Integrated within the CPU, or roughly 35 to 300 W discrete |
Memory bandwidth matters as much as core count. A discrete GPU’s own graphics memory feeds its cores many times faster than system RAM can, which is a large part of why it runs neural networks so quickly.
Key differences in practice
Those design choices show up in four practical ways when you specify a system.
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Cores. A CPU has a handful to a few dozen cores; a GPU has thousands. Our NVIDIA RTX 2000 Ada cards have 2,816 CUDA cores.
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Work type. CPUs suit varied, branching tasks; GPUs suit large, repeated calculations such as matrix maths and image processing.
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Memory. A discrete GPU has its own fast graphics memory beside the system RAM, so model size is limited by the GPU’s memory.
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Power. A discrete GPU adds power and cooling needs, so the chassis has to be built for it.
Why GPUs run AI faster
A neural network is mostly multiplication and addition across large grids of numbers, repeated layer after layer. Each calculation is independent of its neighbours, so thousands of GPU cores can work on them at once. Modern GPUs add dedicated matrix engines, such as NVIDIA Tensor Cores, and support lower-precision formats like FP16 and INT8 that multiply throughput again.
A CPU can run the same model, and for small models or occasional inference it is often enough. Once a system has to analyze several video streams in real time, or run larger detection and segmentation models, the GPU is typically many times faster for the same power.
A third option is the NPU, a dedicated AI accelerator built into recent processors that runs smaller models at very low power. Our GPU vs NPU guide compares the two for edge AI.
Integrated and discrete GPUs
Most CPUs now include an integrated GPU on the same chip, sharing system memory. It drives the displays, decodes video and can run light AI models. A discrete GPU is a separate card or module with its own memory and power supply, added when the work needs more. Our FAQ on integrated vs discrete graphics explains the trade-off, and our explainer on what a graphics card is covers the discrete card itself.
Choosing CPU-only or GPU computing at the edge
Choosing between CPU-only and GPU computing usually comes down to how much AI or video work runs on site. A gateway, HMI or data logger rarely needs more than the CPU and its integrated graphics. A machine vision cell, traffic analytics system or multi-camera security platform usually does.
Our industrial GPU computers pair both in one system, with the CPU managing I/O and the application while the GPU runs inference.
Quick answers on GPU vs CPU
GPU vs CPU: common questions
Is a GPU faster than a CPU?
Only for parallel work. A GPU is far faster for graphics, video and AI, where the same calculation repeats. A CPU is faster for varied, step-by-step tasks such as running an application.
Can a GPU replace a CPU?
No. A GPU cannot run an operating system or manage the computer on its own. It works alongside the CPU, which hands it the parallel work.
Do I need a GPU for AI?
Not always. Small models can run on a CPU or NPU. Real-time inference on several camera streams, or larger models, usually needs a GPU.
When should you use a GPU instead of a CPU?
When the workload is parallel. Use a GPU for AI inference and training, video encoding, image processing and 3D graphics. Keep control logic and general software on the CPU.
Is integrated graphics a GPU?
Yes. Integrated graphics is a GPU built into the processor, sharing system memory. It is less powerful than a discrete GPU but handles displays, video and light AI.
We fit and configure the GPU, drivers and AI software stack as standard in our edge computers, matched to the models and camera streams the system will run.