1. Introduction
In recent years, Artificial Intelligence (AI) has evolved from a futuristic concept to an integral part of our daily lives. Virtual assistants on smartphones, recommendation systems on streaming platforms, computer-assisted medical diagnostics, and even self-driving cars are just a few examples of applications that already surround us.
But behind each of these innovations lie two essential elements: AI frameworks and the hardware that supports them.
Frameworks are sets of tools, libraries, and standards that make it easier to develop AI models. They function as a sort of “toolbox” that allows programmers and data scientists to create, train, and run complex algorithms without having to reinvent the wheel for every project. Some of the best-known examples are TensorFlow, PyTorch, and JAX.
These frameworks can run on different types of hardware, but for modern AI to reach its current levels of power and speed, it had to go beyond the traditional processor (CPU) and rely on other types of processing units, such as the GPU and TPU.
2. How AI Models Are Trained and Run
Before diving into hardware, it’s important to understand what it means to “train” and “run” an AI model.
Training an AI model is like teaching a child to recognize objects. You show it thousands of examples—say, pictures of cats—and tell it, “this is a cat.” Over time, the model learns to identify the features that define a cat: the shape of the ears, snout, whiskers, and fur. This process requires an enormous amount of mathematical computation to adjust the internal “weights” of the neural network, something that could take a very long time if done only with a CPU.
Running a model—also called inference—is when the trained AI receives new data and needs to make a decision or produce an answer. In this stage, it is no longer learning; it’s simply applying what it already knows. Following the same example, it’s like showing it a new photo and asking, “is this a cat or not?”
Training is like preparing an athlete for competition: it requires effort, discipline, and repetition. Running is putting the trained athlete into competition: there’s still effort involved, but it’s much quicker.
3. The Role of Hardware: CPU, GPU, and TPU
3.1 CPU — Central Processing Unit
The CPU is the traditional computer processor, responsible for coordinating almost all of the machine’s tasks. It’s excellent at handling a wide range of operations but not as efficient when it needs to repeat millions of simple calculations in parallel.
If we compare it to a kitchen, the CPU would be like a gourmet chef: extremely skilled, capable of making complex dishes, but preparing only a few at a time with great care.
3.2 GPU — Graphics Processing Unit
The GPU was originally designed to process images and graphics, especially for games and 3D animations. Rendering a complex scene requires simultaneous calculations of colors, textures, and lighting for millions of pixels. This calls for a different kind of processing: instead of a few powerful cores like a CPU, a GPU has thousands of smaller cores capable of working in parallel.
It was exactly this ability to perform massive parallel computations that caught the attention of AI researchers. Neural networks—especially deep learning models—carry out millions of matrix multiplications and additions, something GPUs can execute extremely efficiently.
In the kitchen analogy, the GPU would be an industrial kitchen with hundreds of cooks all preparing the same dish at the same time, each handling a small part of the recipe. The result is that the work is finished much faster.
3.3 TPU — Tensor Processing Unit
The TPU is a special type of processor created by Google specifically to accelerate neural network computations. It was not designed to handle graphics or general-purpose tasks but to efficiently process the mathematical operations typical of deep learning.
TPUs are especially useful in large data centers and Google Cloud environments, where they can deliver extremely high performance with reduced energy consumption.
4. Why the GPU Became the Engine of AI
Before the popularization of GPUs in AI, training deep neural networks could take weeks or even months, even on powerful computers. This wasn’t just inconvenient—it limited innovation, since any change to a model meant restarting a long training cycle.
With GPUs, that time dropped dramatically. Models that once took weeks to train could now be trained in days or even hours. This opened the door to more ambitious experimentation, allowing researchers to test more ideas in less time.
Moreover, GPUs were already widely available due to the gaming and graphics industries, which made their adoption by the scientific community easier. The transition was almost natural: it only required adapting the frameworks to use the GPU’s parallel processing power.
5. How NVIDIA Took the Lead
NVIDIA’s success in the AI world was no accident—it was the result of strategic vision, software investment, and the creation of a robust ecosystem.
Strategic foresight — Long before deep learning became mainstream, NVIDIA was already investing in research and development to make its graphics cards useful not only for gaming but also for scientific and high-performance computing.
Proprietary software — The company created CUDA, a platform that allows its GPUs to be programmed for general-purpose tasks, and cuDNN, a library optimized for neural networks. These tools gave developers an easy and efficient way to use GPUs for AI.
Integration with frameworks — NVIDIA worked directly with the teams behind TensorFlow, PyTorch, and other frameworks to ensure optimized support for its GPUs from the earliest versions. This meant that anyone wanting to work with cutting-edge AI would almost inevitably choose NVIDIA hardware.
Network effect — When researchers, universities, and companies standardize on a specific hardware and software stack, a virtuous cycle emerges: more users mean more support, more compatible libraries, more skilled developers, and ultimately more advantages for everyone adopting that technology.
6. Popular Frameworks and Hardware Support
To better understand how hardware and software connect, it’s worth mentioning some of the most widely used frameworks:
- TensorFlow: created by Google, heavily used in both research and production. Supports CPU, GPU (NVIDIA and AMD via ROCm), and TPU.
- PyTorch: developed by Facebook (now Meta), favored by researchers for its ease of use. Supports CPU, GPU (NVIDIA, AMD), and Apple’s M1/M2 chips.
- JAX: also from Google, used mainly in advanced research. Optimized for GPU and TPU.
- ONNX Runtime: focused on running pre-trained models quickly across multiple platforms, from CPUs to GPUs and dedicated chips.
All of them can leverage GPU power to accelerate model training and execution, but the best performance is often achieved with NVIDIA cards due to the maturity of its ecosystem.
7. The Future of AI Processing
Although NVIDIA has dominated the field in recent years, other companies are investing heavily to compete in this multibillion-dollar market.
AMD is improving its ROCm ecosystem as an alternative to CUDA. Intel is betting on oneAPI and its own accelerators, such as Habana Gaudi. Google continues expanding its TPU offerings, while companies like Amazon and Microsoft are developing their own AI chips for the cloud. There are also startups focused on building specialized AI chips, promising greater energy efficiency and lower costs.
In the future, we will likely see greater diversity in available options, but GPUs will remain a central component—whether in research or commercial applications.
8. The Project Management Perspective: Managing AI-Driven Initiatives
As artificial intelligence becomes central to digital transformation, project managers face new challenges that go beyond traditional planning, cost, and scope control. Understanding how frameworks and hardware shape AI development is essential for managing expectations, aligning teams, and making informed decisions about resources and timelines.
1. Time and Resource Estimation
Training AI models is computationally intensive. The difference between running models on CPUs versus GPUs or TPUs can mean days versus weeks of processing. Project managers must account for these variations when estimating delivery schedules, cloud usage, and energy costs. Choosing the right hardware directly affects the project’s critical path.
2. Budget and Procurement Decisions
Hardware and cloud resources represent a growing portion of AI project budgets. Knowing whether to invest in on-premise GPUs, rent cloud-based TPUs, or leverage hybrid infrastructure can dramatically impact costs and flexibility. Partnering with technical leads to model these scenarios early helps avoid budget overruns.
3. Team Composition and Skills
AI frameworks like TensorFlow, PyTorch, and JAX require teams with specialized skills in machine learning engineering, data preparation, and MLOps. Project managers need to ensure cross-functional collaboration between data scientists, DevOps engineers, and software developers—while maintaining clear ownership and accountability.
4. Risk and Dependency Management
AI projects often depend on third-party cloud providers or proprietary technologies (such as NVIDIA’s CUDA). This creates potential vendor lock-in risks and dependency constraints. Mitigation plans should include alternative architectures or portability strategies (e.g., ONNX Runtime or open-source backends).
5. Governance, Ethics, and ESG
Beyond technical considerations, AI initiatives intersect with ethical and sustainability dimensions. The energy consumption of GPUs and large models raises ESG questions that are increasingly relevant to boards and stakeholders. Project managers can play a crucial role in aligning AI innovation with the organization’s sustainability and compliance goals.
In short, managing AI projects today requires a blend of technical literacy, strategic foresight, and agile leadership. By understanding the role of frameworks and hardware, project managers can better anticipate constraints, empower teams, and ensure that AI delivers real business value.
8. Conclusion
Frameworks like TensorFlow, PyTorch, and JAX are the tools that enable the creation and execution of AI models, but hardware is the engine driving this revolution. The CPU remains important, but it was the GPU that delivered the performance leap that made deep learning a practical reality.
NVIDIA recognized this opportunity before anyone else, creating not only powerful graphics cards but also the software ecosystem that became the industry standard. Today, thinking of high-performance AI still largely means thinking of NVIDIA GPUs.
However, the landscape is changing. New competitors, specialized chips, and the continued evolution of TPUs suggest a more diverse—and possibly faster and more accessible—future.
Whatever comes next, one thing is certain: CPU, GPU, and TPU will continue to be the invisible gears behind the artificial intelligences that shape our world.