CPU vs GPU vs NPU: What Does an Industrial AI Application Really Need?

As industrial AI and Edge AI develop, three acronyms increasingly appear in industrial computing specifications: CPU, GPU and NPU.

They are sometimes compared solely through computing performance.

But this can be misleading.

A CPU, GPU and NPU are not designed to execute the same workloads in the same way.

When selecting a platform for machine vision, AI inference or Edge processing, the first question should therefore not be:

“Which processor is the most powerful?”

It should be:

“What type of computing does the application actually need?”

These resources are not necessarily competitors.

Within an industrial computing platform, they can work together.

The CPU – Central Processing Unit remains the central processor in most industrial computing systems.

It runs the operating system, applications, communications and a wide range of general-purpose workloads.

In an industrial PC, the CPU may simultaneously manage:

• Windows or Linux;

• HMI or SCADA software;

• network communication;

• PLC communication;

• database access;

• data acquisition;

• business applications.

Its main strength is flexibility.

A CPU can execute many different types of tasks even when they involve very different processing logic.

Yes.

An application using an AI model does not automatically require a GPU or NPU.

Depending on the model, inference frequency, data volume and required performance, a modern CPU may be sufficient for some AI workloads.

This can be relevant when:

• the model is relatively lightweight;

• inference is not performed continuously;

• only a limited number of streams are processed;

• extremely low latency is not required;

• sufficient CPU resources remain available.

Adding an accelerator simply because an application includes “AI” is therefore not always justified.

The GPU – Graphics Processing Unit was originally developed for graphics processing.

Its highly parallel design also makes it particularly effective for certain artificial intelligence workloads.

While a CPU is optimised for complex general-purpose tasks, a GPU can execute a very large number of operations in parallel.

This becomes especially relevant when applications process large volumes of similar data.

Images contain large amounts of data that may benefit from parallel processing.

When an application needs to analyse several images or video streams simultaneously, GPU acceleration can become relevant.

Typical use cases include:

• object detection;

• classification;

• image segmentation;

• AI-assisted visual inspection;

• video analytics;

• multi-camera processing;

• demanding deep-learning models.

The CPU generally continues to manage the system and application while the GPU accelerates specific workloads.

The presence of a camera does not automatically mean that a GPU is required.

Requirements depend on factors including:

• number of cameras;

• resolution;

• frame rate;

• model complexity;

• required response time;

• pre-processing and post-processing operations.

A single-camera application using a lightweight model therefore has very different requirements from a system analysing several high-resolution streams simultaneously.

The NPU – Neural Processing Unit is specifically designed to accelerate operations used by neural networks.

Unlike a GPU, which can execute a broad variety of parallel workloads, an NPU is more specialised.

One of its main advantages is the ability to execute certain inference workloads with high power efficiency.

This is particularly relevant for Edge systems.

A platform installed directly on a machine or inside compact equipment can face very different constraints from a server.

It may need to combine:

• local AI processing;

• low power consumption;

• limited thermal envelope;

• compact dimensions;

• fanless operation.

In these applications, energy efficiency can be just as important as raw computing performance.

An NPU may therefore execute the AI workload without systematically requiring a more power-hungry discrete GPU.

Depending on platform performance and model compatibility, an NPU may be relevant for:

• anomaly detection;

• object recognition;

• classification;

• OCR;

• smart-camera processing;

• local AI assistance;

• low-power Edge applications.

However, as with a GPU, the presence of an NPU alone does not guarantee application performance.

The software and model must be able to use the available accelerator.

NPUs and AI accelerators are frequently compared according to their number of TOPS – Tera Operations Per Second.

This provides an indication of theoretical processing capability for certain operations.

However, comparing platforms solely according to TOPS is insufficient.

Real application performance also depends on:

• the model;

• numerical precision;

• framework;

• model optimisation;

• available memory;

• memory bandwidth;

• inference software;

• number of simultaneous streams.

A higher TOPS figure therefore does not automatically guarantee a faster application.

Conventional industrial computing

HMI, SCADA, PLC communication, data collection or database access: CPU is generally the priority. AI acceleration may provide no meaningful benefit.

Lightweight and occasional AI

A small model running occasionally may sometimes be handled directly by the CPU. Evaluate the CPU before adding an accelerator.

Demanding machine vision

Multiple cameras, intensive image processing or complex deep-learning models may require greater parallelism. Evaluate GPU acceleration.

Power-constrained Edge AI

For a compact platform running an AI model locally with limited power consumption and thermal dissipation: Evaluate an NPU.

Complex applications

Some applications can use several resources simultaneously. CPU + GPU, CPU + NPU or even CPU + GPU + NPU may coexist within the same platform.

When a platform integrates an NPU directly, another distribution of workloads becomes possible.

The CPU retains the system's general-purpose functions while the NPU executes compatible AI workloads.

This architecture can be particularly interesting for:

• compact Edge PCs;

• Panel PCs simultaneously running HMI software and AI;

• embedded systems;

• fanless equipment;

• power-constrained applications.

When a platform integrates an NPU directly, another distribution of workloads becomes possible.

The CPU retains the system's general-purpose functions while the NPU executes compatible AI workloads.

This architecture can be particularly interesting for:

• compact Edge PCs;

• Panel PCs simultaneously running HMI software and AI;

• embedded systems;

• fanless equipment;

• power-constrained applications.

Integrating different processor types into one platform reflects an important change in industrial computing.

The system can assign each workload to the component best suited to it:

• CPU → orchestration and general-purpose computing
• GPU → intensive parallel computing
• NPU → optimised AI inference

The platform no longer necessarily relies on one processor performing every task.

It becomes heterogeneous.

The challenge is therefore less about having the most powerful component and more about assigning workloads efficiently.

An accelerator cannot be selected solely from its hardware specifications.

A GPU or NPU needs to be usable by the project's software environment.

Important factors include:

• AI framework;

• inference runtime;

• drivers;

• development tools;

• operating system;

• supported model formats;

• optimisation tools.

A platform with a powerful accelerator but poor compatibility with the project's software stack may be less appropriate than a theoretically slower but fully supported solution.

The processor is only one element of an AI platform.

Machine vision and industrial AI applications may also require:

• sufficient RAM;

• suitable memory bandwidth;

• high-speed networking;

• camera interfaces;

• USB;

• Ethernet;

• PCIe expansion;

• adequate storage.

A powerful GPU cannot compensate for a system architecture that cannot deliver data quickly enough to the processor.

Servers can use substantial cooling systems.

Industrial equipment may have much tighter space and airflow restrictions.

A discrete GPU can significantly increase:

• power consumption;

• heat generation;

• system size;

• cooling requirements.

An integrated NPU can provide a different compromise when the application needs to remain compact and power-efficient.

For fanless systems, the actual ability to dissipate heat must therefore be evaluated alongside AI performance.

This table provides guidance rather than an absolute rule.

Required performance should always be evaluated according to the model, number of streams and expected processing time.

Accelerator selection comes after a more fundamental decision.

If the application does not need local AI processing, comparing GPUs and NPUs may have little value.

First determine where processing needs to take place and whether an Edge AI platform provides a genuine benefit.

For this first step, read our guide Industrial Edge AI: When Does AI Really Belong at the Edge?

Oversizing computing performance in an industrial application can increase power consumption, heat, system size and cost without improving the machine's actual operation.

Selection should therefore start with the real workload.

• CPU for general-purpose functions.
• GPU when parallel processing and workload intensity justify it.
• NPU when specialised AI inference and power efficiency become priorities.

And when several types of workload coexist, the architecture can combine several of these resources.

The right question is therefore not:

“CPU, GPU or NPU: which one is best?”

It is:

“Which computing resource should execute each part of my application?”