How graphics cards became the heart of artificial intelligence

From Graphics to Artificial Intelligence

A few decades ago, no one expected that graphics cards—originally designed for rendering images—would become one of the most important technologies of the 21st century. Initially, GPUs were used almost exclusively in the context of computer games, 3D graphics rendering, or multimedia processing. Their core advantage was parallel processing capability, which allowed them to generate realistic shadows, reflections, and animations in fractions of a second.

Over time, it turned out that this very feature—parallelism—was invaluable in areas far beyond gaming. Scientists began using GPUs for scientific calculations, physical simulations, and large-scale data analysis. This opened the door to artificial intelligence, which requires not so much complex operations as repetitive, massively parallel mathematical computations.

Why are GPUs better than CPUs in AI?

Traditional CPUs were designed for sequential tasks. They excel at general-purpose operations, running operating systems, and office applications. However, neural networks—the foundation of machine learning—are based on vast amounts of matrix operations that are far more efficiently executed in parallel.

GPUs, with their thousands of cores, can perform hundreds of thousands of mathematical operations simultaneously. In practice, this means that training a large language model, which could take months on CPUs, can be completed in just days or weeks on GPUs. In the rapidly growing AI sector, where time-to-market is critical, this advantage is invaluable.

It is also worth emphasizing that the GPU ecosystem has expanded thanks to tools such as NVIDIA CUDA and AMD ROCm, which allow developers to write code optimized for parallel computing. These frameworks have contributed significantly to the explosive growth of GPU use in machine learning.

Training and Inference – the Two Faces of AI

Training an AI model involves repeatedly processing massive datasets. Each example is analyzed, results are compared with a reference, and the model’s parameters are adjusted to minimize errors in subsequent iterations. This process can require millions of iterations, each involving millions of mathematical operations. GPUs, thanks to their architecture, significantly accelerate this process.

Once the model is trained, it can be deployed in practice—this stage is called inference. This is when AI classifies images, translates text, forecasts sales, or analyzes traffic. At this point, speed is crucial. GPUs, capable of processing data streams almost instantaneously, are indispensable in real-time applications—from autonomous vehicles to predictive smart city systems.

The Breakthrough: From GeForce to Data Centers

The breakthrough for GPUs came when researchers realized they could be used for more than just graphics. Early studies in “General Purpose GPU Computing” (GPGPU) demonstrated that GPUs were excellent for scientific simulations. With the development of programming libraries, scientists began using them in bioinformatics, genome analysis, and climate modeling.

The next step was introducing GPUs into data centers. Companies like NVIDIA saw the potential in AI and started designing chips optimized specifically for machine learning. Servers equipped with dozens of graphics cards became the standard in research labs and tech firms developing AI models on a global scale.

The New Generation of GPUs: NVIDIA Blackwell

In 2025, NVIDIA introduced the Blackwell architecture, marking another milestone in the evolution of GPUs for AI. Blackwell was designed for enormous models—from generative AI to big data analytics. It offers not only vastly greater computational power but also optimized energy efficiency, which is critical given rising energy costs and the need for sustainable development.

Blackwell also brings new capabilities for working with multimodal AI models that simultaneously process images, text, audio, and sensor data. This paves the way for more advanced systems capable of understanding context in a way that more closely resembles human perception.

Practical Applications of GPUs

The potential of GPUs can be seen across many industries. Here are some examples:

• Smart Cities – real-time camera image analysis, traffic prediction, and public transport management.
• Medicine – accelerated analysis of diagnostic images, disease prediction, and personalized therapies.
• Finance – instant transaction analysis, fraud detection, and predictive market modeling.
• Industry – quality control on production lines using AI, data analysis from IoT sensors.
• Hospitality – supporting process automation in hotels and office buildings, personalization of guest experiences, and energy management.

In each of these cases, the key advantage of GPUs is their ability to work in real time and process massive volumes of data. This is what makes AI a practical tool supporting everyday business and operational decisions.

The Future of GPUs and AI

As artificial intelligence continues to advance, the role of GPUs will only grow. Larger models demand ever greater computational power, but at the same time, there is an increasing emphasis on energy efficiency. That is why GPU manufacturers focus not only on raw performance but also on reducing energy consumption and heat emissions.

We can expect future GPU generations to be more integrated with specialized processors (ASICs, NPUs), while data centers will adopt hybrid architectures combining different types of processors. However, GPUs will remain the cornerstone, as their flexibility and programming capabilities make them the best choice in a rapidly evolving AI landscape.

Conclusion

GPUs have evolved from graphics accelerators for video games to the very heart of the artificial intelligence revolution. Their unique architecture allows the processing of massive amounts of data at speeds that just a few years ago seemed impossible. It is thanks to GPUs that AI is developing at such a rapid pace, finding applications across every aspect of life—from medicine and transportation to business and entertainment.

The new generation of cards, such as NVIDIA Blackwell, accelerates this progress even further, opening the door to more advanced applications and new AI models. Ewosoft leverages these capabilities in its solutions, driving digital transformation for cities, hotels, office buildings, and enterprises. This demonstrates that the future belongs to those who can combine computing power with the practical use of data.

Want to learn more about the role of GPUs in artificial intelligence?
Contact us: info@ewosoft.com – we’ll show you how we use GPUs in our solutions for Smart Cities, hotels, and businesses.