AI Colocation: the infrastructure enabling Artificial Intelligence

Conventional IT infrastructures were designed to support business applications, databases, virtualization, and web services. Artificial Intelligence workloads follow entirely different principles.

As mentioned above, training advanced models requires enormous amounts of data and parallel processing capabilities. AI relies on specialized hardware components such as GPUs, NPUs, and TPUs, designed to perform millions of operations simultaneously.

This computing power must be supported by extremely high-performance storage systems capable of transferring data at the speed required by machine learning algorithms, as well as networks able to guarantee minimal latency and high bandwidth.

The result is an infrastructure that is significantly more power-hungry and complex to manage than a traditional data center.

One of the least visible aspects of AI is its energy consumption.

The latest-generation GPUs can require significantly more power than traditional servers while simultaneously generating large amounts of heat.

To ensure operational continuity and consistent performance, AI-focused data centers must adopt dedicated architectures featuring high-density racks, advanced cooling systems, and carefully engineered airflow management.

Building this type of infrastructure internally requires substantial investments not only in hardware, but also in the facilities needed to support it.

Colocation enables organizations to leverage infrastructures specifically designed for AI without having to bear the costs of building and operating a dedicated data center.

The organization retains full control over its computing resources, while infrastructure-related aspects are entrusted to a specialized partner such as Tinext Cloud.

This approach lowers the barrier to entry for Artificial Intelligence, making resources available that are typically accessible only to large enterprises through hyperscale providers.

Furthermore, colocation allows infrastructure to be scaled progressively, following the evolution of projects and computing requirements.

One of the most common mistakes in AI projects is overprovisioning infrastructure. Not every model requires the most powerful GPUs available on the market, and not every workload needs the same architecture.

The real challenge lies in finding the right balance between performance, cost, and scalability.

This is why the initial assessment phase becomes crucial. Testing different hardware configurations before making a final investment makes it possible to determine which platform is truly required to achieve the project's objectives.

Tinext Cloud has developed an AI Colocation service hosted within secure and sovereign Swiss data centers, specifically designed to support high-density computing infrastructures.

The approach goes far beyond simply providing physical hosting for hardware and covers the entire lifecycle of the project.

Organizations can rely on certified environments optimized for AI workloads, support in selecting the most suitable architecture, direct server procurement, installation services, and ongoing operational management.

For the definition of hardware configurations, Tinext Cloud also collaborates with specialized technology partners such as HPE, with the goal of identifying the solutions best suited to the needs of each organization.

A distinctive element of the service is the ability to conduct trials on different hardware and GPU configurations before proceeding with the final investment.

This approach allows organizations to validate the actual performance of AI models, avoiding unnecessary purchases and optimizing the balance between costs and results. Companies can also choose from different service levels, ranging from infrastructure procurement and hosting to complete operational management handled by Tinext Cloud specialists.

The goal is to allow internal teams to focus on developing models and applications while delegating infrastructure complexity to a partner with extensive experience in data centers, storage technologies, and AI-driven solutions.