Four AI Performance Classes Explained: Matching the Right Hardware to Every AI Application

Four AI Performance Classes Explained:
Matching the Right Hardware to Every AI Application

Fast response times, reliable results, and efficient energy consumption. An industrial AI application can only be successful if its hardware is precisely matched to the task at hand.

Both too little and too much computing power can become a problem. An undersized platform may not process data quickly enough. In applications such as autonomous drones, mobile robots, or optical quality inspection, even short delays can be critical. Oversized hardware, on the other hand, increases acquisition costs, power consumption, and cooling requirements without providing corresponding added value for the application.

With GLYN SUPPORT, you get exactly the AI performance your application requires. Here, we have clearly outlined the four key performance classes for you.

AI has arrived in industry.

Artificial intelligence has been making headlines for many months. Initially, the focus was on intelligent search functions and the generation of text, images, and videos. By now, however, AI has firmly established itself in industrial applications.

Use cases range from automated optical quality inspection and intelligent transportation systems to autonomously navigating machines and agricultural drones.

As the variety of applications grows, so does the number of available hardware platforms. Which architecture is best suited to your application? Is an NPU integrated into the processor sufficient? Is an NVIDIA Jetson system required? Or is a high-performance server with dedicated graphics cards the better choice?

To help you answer these questions more easily, we divide our portfolio into four fundamental performance classes.

1. Integrated NPU: an efficient entry point into industrial AI

Integrierte NPU: effizienter Einstieg in industrielle KI

A Neural Processing Unit, or NPU for short, is a computing unit specifically designed for AI workloads. It handles typical inference tasks particularly efficiently in terms of energy consumption and is now integrated directly into many modern processors.

For compact and less complex AI applications, this architecture often already provides sufficient performance. At the same time, power consumption, heat generation, and space requirements remain comparatively low.

A current example is the SOM-5886 COM Express Basic Type 6 module from ADVANTECH. It is based on Intel Core Ultra Series 3 processors of the Panther Lake generation and combines CPU performance, powerful Intel Arc graphics, and an integrated NPU for AI acceleration. This makes the module suitable for compact image processing, analytics, and automation systems, among other applications.

Suitable for:
Simple image classification, condition monitoring, anomaly detection, intelligent user interfaces, and compact edge AI systems.

2. NVIDIA Jetson: High Edge AI performance with fast response times

For more demanding AI applications at the edge, NVIDIA Jetson is often the platform of choice. These platforms combine ARM processor cores with powerful NVIDIA GPUs and a comprehensive software ecosystem.

Jetson systems demonstrate their strengths particularly in applications where large volumes of data need to be processed locally and with the lowest possible latency. Examples include multiple high-resolution camera streams, real-time object detection, and the navigation of autonomous vehicles and machines.

One example is the BOXER-8645AI from AAEON, based on NVIDIA Jetson AGX Orin. This rugged, fanless edge AI system features eight FAKRA connectors for GMSL2 cameras. This allows up to eight high-bandwidth camera streams to be consolidated and analyzed within a single system.

Suitable for:
Machine vision, autonomous vehicles, mobile robots, intelligent transportation systems, agricultural machinery, and latency-critical video analytics.

BOXER-8645AI von AAEON mit NVIDIA Jetson AGX Orin

3. Server and HPC performance: Maximum computing power for complex models

Anyone looking to run, optimize, or train particularly demanding AI models requires significantly more computing power. These systems combine high-performance AMD EPYC, AMD Ryzen, or Intel Xeon processors with dedicated GPUs.

They can process large volumes of data and are suitable for both complex inference workloads and the fine-tuning and training of AI models.

A typical application scenario is the interaction between edge systems and a central AI server: Autonomous drones or vehicles collect new image and sensor data during operation. This data is then transferred to a high-performance server, where the AI model can be optimized using the new information before being deployed back to the connected edge systems.

The ADVANTECH AIR-420 Edge AI HPC supports, among other options, processors from the AMD Ryzen and EPYC 4005 families as well as powerful NVIDIA RTX PRO graphics cards. The compact system is designed for compute-intensive AI inference, model optimization, and local generative AI.

Suitable for:
Training and fine-tuning AI models, large language models, complex image analysis, centralized AI infrastructure, and demanding research and development tasks.

4. AI accelerator cards: Flexibly expanding existing systems

Not every AI application requires a completely new industrial PC. AI accelerator cards make it possible to selectively add additional computing power to existing systems.

Depending on the design, the modules are integrated via M.2 or PCI Express. The performance range extends from compact, energy-efficient modules for individual inference tasks to powerful cards for complex AI models.

The key advantage is flexibility: AI performance can be selected to match the specific application, existing hardware can continue to be used, and future upgrades are often easier to implement.

Well-known vendors include Hailo, DEEPX, Qualcomm, Axelera AI, and Intel. Hailo, for example, offers compact M.2 accelerators for energy-efficient, real-time inference at the edge.

Suitable for:
Upgrading existing industrial PCs, scalable machine vision systems, compact edge devices, and application-specific AI acceleration.

Which AI hardware is right for your application?

The required computing power does not depend solely on the AI model being used. Other important factors include:

Number and resolution of cameras or sensors
Required response time
AI framework and model format used
Available power budget
Ambient temperature and cooling
Available space and interfaces
Requirements for scalability and long-term availability

A one-size-fits-all recommendation is therefore rarely the best approach. What matters is the interaction between the application, software, computing performance, and environmental conditions.

From integrated NPUs and NVIDIA Jetson to flexible accelerator cards and powerful edge AI servers, our specialists help you select the right platform for your requirements.

Get in touch with us. Together, we will find the AI hardware that makes your application reliable, efficient, and future-proof.