Generative AI & GPU: How the Rise of AI Models Is Boosting Demand for High-Performance Servers

The year 2025 marks a new milestone in the history of artificial intelligence. With the emergence of ever-more powerful models like GPT-4 Turbo, Claude 3, and Gemini 1.5, the generative AI ecosystem is experiencing explosive growth. While these tools are impressive in their capabilities, they also pose a major technical challenge: how to provide the computing power needed for their training and deployment? The answer is three letters: GPU.

At APY, we are at the forefront of this revolution. We design and distribute high-performance workstations, GPU servers, and tailor-made solutions for the specific needs of AI, 3D rendering, and scientific computing players. Here's why 2025 is a pivotal year for AI infrastructure... and how we can support you in this transition.

🚀 Ever More Demanding Models

Since the dawn of generative AI, the required computing power has increased dramatically. For example, training GPT-4 Turbo required tens of thousands of high-performance GPUs, consuming hundreds of millions of dollars in cloud resources.

And the trend isn't slowing down. New models introduced in 2025 exploit more parameters, require more complex processing, and are often executed in real time. This complexity requires an infrastructure capable of handling enormous workloads in parallel, with high memory bandwidth, optimized cooling, and controlled power consumption.

🎯 The GPU: The Beating Heart of Modern AI

The GPU (graphics processing unit) is no longer reserved for video games or 3D rendering. It has become the central component of modern AI thanks to its ability to perform massively parallel computations. The latest generations, such as the NVIDIA H200, NVIDIA L45S, and AMD MI355X, are redefining what can be expected from an AI server or workstation.

The AMD MI355X, for example, offers increased performance for AI workloads, although recent analyses suggest they may not match the performance of Nvidia's flagship products.

These cards are now at the heart of data centers and professional workstations. They not only allow models to be trained internally, but also allow complex inferences to be performed locally, without relying on cloud giants.

🖥️ Towards a Cloud/On-Premise Hybridization

While many companies have opted for total outsourcing to the cloud in recent years, we are seeing a partial return to on-premise (local) infrastructures in 2025. Why? To reduce long-term costs, gain privacy, and control performance.

AI workstations now allow you to develop, test, and deploy powerful models locally, while retaining the ability to export certain workloads to the cloud. This hybridization is becoming the dominant model, and APY offers solutions tailored to each use case.

🔧 Our customized solutions

At APY, we understand that each AI project is unique. That's why we offer:

Preconfigured or customizable AI workstations (1 to 4 GPUs)

4U to 7U GPU servers for internal data centers

Ready solutions for CUDA, PyTorch, TensorFlow, etc.

Expert advice to size your infrastructure according to your business needs

Our commitment: to offer you reliable, scalable solutions designed to last in a constantly changing market.

Generative AI is radically transforming our relationship with digital technology, and the infrastructures that support it are evolving at a rapid pace. GPUs have become a strategic resource, just like data or talent.

At APY, we help you make the right technology choices, today, to be ready for tomorrow.

👉 Need power for your AI projects? Discover our professional solutions at apy-groupe.com or contact our team to discuss your specific needs.