AI glossary
GPU (Graphics Processing Unit)
A GPU (graphics processing unit) is a processor originally designed to render computer graphics. It is built around thousands of small cores that can each perform simple arithmetic in parallel, unlike a CPU’s fewer cores, which are optimized for executing different tasks sequentially.
What a GPU is
To understand why GPUs are the default choice for AI workloads, it helps to first distinguish a GPU from the central processing unit (CPU). A CPU is like a general-purpose manager: it has a few powerful cores capable of handling complex, diverse instructions quickly, one after another. This makes CPUs excellent for operating systems, running applications, and handling logic-heavy tasks.
A GPU, by contrast, is a specialized worker. It contains thousands of smaller, simpler cores. These cores are not designed to handle complex, varied instructions efficiently. Instead, they are built to perform the exact same operation on many different pieces of data simultaneously. This architecture, known as parallel processing, allows the GPU to handle massive workloads by breaking them down into tiny, simultaneous operations.
Why GPUs suit neural network math
The primary reason developers and data scientists prefer GPUs for machine learning comes down to the underlying math. Training a neural network involves multiplying large matrices and tensors. These operations consist of millions or billions of small, independent arithmetic calculations.
Because these calculations do not depend on each other, they can be executed in parallel. A CPU would process these sequentially, creating a bottleneck. A GPU’s architecture, with its many cores executing the same kind of operation in parallel on different data chunks, is naturally suited to this workload. Consequently, GPUs can train neural networks dramatically faster than CPUs for the same task. This efficiency is why the choice between GPU and CPU infrastructure often hinges on the need to process large datasets without waiting days for a single model to converge.
CUDA and general-purpose GPU computing
Early on, GPUs were strictly for graphics. To use them for other tasks, developers had to hack them into simulating physics or other non-visual processes. The landscape changed in 2007 when NVIDIA released CUDA (Compute Unified Device Architecture).
CUDA is a programming platform that allowed developers to write general-purpose code, not just graphics rendering code, to run on NVIDIA GPUs. This innovation opened the door to using GPUs for scientific computing and, later, machine learning. It standardized how software interacts with the hardware, making it easier to put GPUs’ parallel processing power to use for AI training. Today, CUDA remains a dominant force in the industry, enabling libraries like TensorFlow and PyTorch to efficiently utilize GPU resources.
The AlexNet moment
While GPUs had been used for scientific computing for years, their role in artificial intelligence shifted permanently in 2012. This year is widely recognized as the start of the modern deep learning boom, triggered by a specific event: the ImageNet Large Scale Visual Recognition Challenge.
A convolutional neural network called AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, won the challenge by a wide margin over the next-best entry. Crucially, AlexNet was trained using two NVIDIA GTX 580 GPUs. Its victory demonstrated that GPUs could make training deep neural networks on large datasets practical and significantly faster than previous methods. Before this moment, many researchers doubted that deep learning was scalable. The AlexNet win proved that parallel processing on GPUs could handle the massive computational load required for complex visual recognition, validating GPUs as AI hardware for the entire industry.
GPUs today
Since 2012, the demand for GPU power has only increased. NVIDIA has become the dominant supplier of GPUs used for training and running large AI models. Data-center GPU chips, such as the A100 and H100, are widely used by AI labs and cloud providers for this purpose. These chips are optimized for the specific tensor operations required by modern artificial neural networks.
However, a single GPU has limits. The memory and compute power of one chip are often insufficient relative to the size of today’s largest models. Therefore, training runs commonly use many GPUs working together across multiple machines rather than relying on a single chip. This parallel scaling is essential for pre-training large language models and other complex systems, where every training step relies on gradient descent running across the whole GPU cluster.
FAQ
What is the main difference between a CPU and a GPU for AI?
A CPU has fewer cores optimized for sequential tasks, while a GPU has thousands of cores optimized for parallel processing. AI workloads, which involve massive matrix multiplications, benefit greatly from this parallelism.
Why is CUDA important for AI development?
CUDA allows developers to write code that runs directly on NVIDIA GPUs for general computing tasks, not just graphics. It provides the infrastructure for popular AI frameworks to access GPU hardware efficiently.
Did the AlexNet win change how AI is trained?
Yes. The 2012 win by AlexNet on NVIDIA GPUs proved that deep learning was scalable and practical, triggering a massive shift toward GPU-based infrastructure for AI research and development.