In recent years, theartificial intelligence (TO THE) has made great strides, bringing incredible innovations in every sector, from medicine to video games, thanks also to the role played graphic cards (GPU). These units have an optimized memory and an excellent calculation speed accelerating the training processes of the AI. Without them, the development of this cutting -edge tool would be much slower. Why does AI need these hardware components? To understand it, we have to take a journey between mathematics, computer science and technology.
What is a GPU: the comparison with the CPUs
The GPU (Graphics Processing Unit) were born to elaborate images and renders graphics in real time, as happens in video games. Their architecture is different from that of CPU (Central Processing Unit): they are designed to perform many parallel operationsmaking them perfect for homework in which they need Many simultaneous calculationsas in the case of AI.
CPUs, on the other hand, are processors used by computer To process any type of data. They are extremely versatile and designed to carry out a large one variety of operations in sequence, but when it comes to performing millions of calculations at the same time, they show theirs limits.
How GPUs accelerate the AI and Machine Learning
Artificial intelligence is based on techniques such as Machine Learning and, in particular, the Deep Learning. But what is Deep Learning? It is a branch of Machine Learning which uses artificial neural networks with many layers (hence the term “deep”, that is profound). These networks simulate the functioning of the human brain in a simplified way, developing large quantities of data to recognize patterns, images, texts or sounds. Each layer of the network processes information and passes to the next one, gradually improving the recognition capacity. To train a DEEP Learning -based AI, you need huge quantities of data And calculation powersince the learning process requires billions of mathematical operations to continuously update the parameters of the network. Precisely for this reason, GPU play a fundamental role in artificial intelligence. Here are the main reasons:
- Parallel processing – While a CPU has few powerful cores, a GPU has thousands of small cores that can perform calculations simultaneously. This accelerates tens or hundreds of times the training of the AI models.
- Calculation speed – Mathematical operations at the base of AI, such as matricial products, are extremely optimized on the GPUs, reducing processing times from days to hours.
- Optimized memory – Modern GPUs have high band -width memories (HBM) that allow you to manage large quantities of data quickly.
- Software optimization – Framework such as Tensorflow and Pytorch exploit GPUs to accelerate calculations, making the training of models accessible to researchers and developers.
Nvidia, AMD and the domain of the GPUs for the AI
When it comes to GPU for artificial intelligence, a name stands out on everyone: Nvidia. The company, famous for its gaming graphic cards, has invested billions in the development of GPUs dedicated to AI, such as the Tesla series And RTX With specific Tensor Core for Deep Learning. Also AMD And Intel They are entering the sector with advanced solutions, but Nvidia still dominates the market.
In addition to traditional graphic cards, there are specialized accelerators such as TPU (Tensor Processing Units) of Google, designed exclusively for Deep Learning. However, the GPUs still remain the most common solution for their versatility.
Ai without GPU: what would happen
Without the GPUthe development of the AI would be incredibly slower. Advanced models like Chatgpt or From they would take years to be trained instead of weeks or days. Even the inference, i.e. the use of an already trained model, would be much slower, making impossible applications such as facial recognition in real time or autonomous driving.
In the future, the GPUs will become more and more powerful and optimized for theartificial intelligencewhile new types of processors, such as neuromorphic units, could still change the cards on the table. But for now, one thing is certain: without the GPUs, the Ai would not be so advanced.
