GPU (Graphics Processing Unit)
A graphics processing unit (GPU) is a specialized processor designed to handle large numbers of calculations at the same time. It was originally built to render graphics but is now widely used for many other compute-heavy tasks.
GPUs contain specialized hardware called tensor cores that perform well at fast matrix and vector calculations. Because of this, GPUs are efficient at training deep learning models, running big data analysis, scientific simulations, and cryptocurrency mining.
There are different types of GPUs.
- Integrated GPUs are built into the CPU and are common in laptops and everyday computers, offering lower power use and cost.
- Discrete GPUs are separate chips, usually on dedicated graphics cards, and provide much higher performance for demanding tasks.
- Virtual GPUs are software-based and allow multiple users or virtual machines to share the power of a single physical GPU, commonly in cloud environments.
In AI and machine learning, GPUs significantly speed up training and inference by processing large datasets in parallel. They are particularly effective for training neural networks, where the same mathematical operations must be applied repeatedly across millions or billions of data points. This is why GPUs are commonly used to train large models like GPT and other generative AI systems.
The terms GPU and graphics card are often used interchangeably, but they are not the same thing. The GPU is the actual processor that performs the calculations, while a graphics card is the full hardware unit that contains the GPU along with memory, cooling, and power components.
Similar to GPUs are TPUs, which are processors built to speed up machine learning workloads, especially those based on neural networks.
