Artificial Intelligence Why the Industry Needs Local AI Infrastructures

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For a long time, the public cloud was considered the preferred solution for the use of AI models. However, it is now becoming clear that it quickly reaches its limits when it comes to critical industrial applications. Local infrastructures not only offer low latency times, but also the necessary data sovereignty and the ability to reliably comply with local and industry-specific security standards.

The increasing spread of artificial intelligence (AI) in the industrial sector requires powerful hardware and software infrastructures that can process high computing loads locally, in real time and with maximum reliability. Although the centralized cloud model is widely used, it is increasingly reaching its limits, especially for industrial applications—whether due to latency, bandwidth or regulatory requirements. In this context, local infrastructures are regaining importance and are becoming the central building block for the successful use of AI, digital twins and edge applications in the industrial environment.

Latency, Security, Sovereignty

Artificial intelligence has long since become a matter of course in many industrial processes, for example in image-based quality control, intelligent robotics or predictive maintenance. However, in such scenarios in particular, excessive latency can jeopardize the precision of automated decisions and, in the worst case, result in operational risks.

In addition to technical requirements, regulatory and security-related aspects are increasingly coming to the fore: companies must retain complete control over their data flows at all times. Particularly in security-critical areas such as healthcare, defense or the energy industry, outsourcing sensitive data to public clouds—especially under foreign jurisdiction—is not an option. Local infrastructures not only offer low latency times, but also the necessary data sovereignty and the ability to reliably comply with local and industry-specific security standards.

Digital Twins: the Fusion of Simulation, AI And Edge Computing

The rapid rise of digital twins impressively underlines the growing relevance of local computing power in industry. According to a Bitkom study, almost two thirds of German industrial companies (63%) see the relevance of this technology for international competition. The virtual images of real systems make it possible to precisely simulate production processes and industrial workflows, detect failures at an early stage and optimize the maintenance of complex systems.

To ensure that the digital twin is not just a planning tool and that real structural change can take place, a high-performance infrastructure is required directly at the data generation location. Only local systems—whether at the edge of the network or in the company's own data center—guarantee coherent real-time processing while minimizing the risks associated with data transfer to external clouds. The transition between simulation and reality can only be seamless if computing power, data analysis and AI are directly linked and processing takes place directly where the data is generated—in the immediate vicinity of production.

Hybrid Architectures: Local Data Center, Edge And "Distributed AI" in Interaction

Industrial AI systems are increasingly developing in the direction of hybrid architectures in which local data centers, edge computing and so-called distributed AI are intelligently networked with one another. This convergence creates the conditions for processing industrial data directly on site, securely and almost in real time.

At the same time, the requirements for flexibility, reaction speed and technological sovereignty are increasing. To be prepared for this, companies need a dynamic infrastructure system: powerful, scalable, open for open source integrations and adaptable to the individual requirements of each location.

Without the Right Infrastructure, AI Remains Just Theory

For a long time, the public cloud was considered the preferred solution for the use of AI models. However, it is now becoming clear that it is quickly reaching its limits for critical industrial applications. Hybrid architectures that intelligently combine local data centers, edge computing and distributed AI offer concrete advantages here: they enable data to be processed in real time while ensuring full control over sensitive data streams.

Edge computing plays a key role here. It allows companies to analyse and process data directly at the point of origin, use resources more efficiently and reduce dependence on centralized infrastructures. These technologies are significantly driving the acceleration of AI workloads. Studies show that the global market for graphics processing units (GPUs) is set for significant growth. It is expected to reach $592.18 billion by 2033, up from $63.22 billion in 2024, with a compound annual growth rate (CAGR) of 28.22% between 2025 and 2033. This is a clear indicator of the growing need for locally powerful computing infrastructure.

The key challenge for the industry now is to build a flexible, powerful and sovereign infrastructure that can exploit the full potential of artificial intelligence while guaranteeing control over data and processes.

Industrial AI Needs Sovereign, Scalable And Resilient Structures

The future of artificial intelligence in industry will neither take place exclusively in the cloud nor entirely on-premise. Rather, a hybrid architecture is emerging that combines the computing power of local infrastructures with the proximity of the edge and the flexibility of the cloud. For industrial companies that want to successfully implement their AI strategies, it is crucial to work with trusted technology partners that have both in-depth software expertise and an understanding of industrial environments. Such providers enable the creation of robust architectures that can be flexibly adapted to different usage scenarios—from classic data centers to edge micro centers and modular embedded solutions. Companies thus benefit from an infrastructure that is precisely tailored to their requirements in terms of flexibility, security and scalability.

AI in the industry

ETMM Dossier – Artificial Intelligence