What Is a GPU? How Graphics Processors Power Artificial Intelligence.

What Is a Graphics Processing Unit?

A graphics processing unit, or GPU, is a processor designed to perform large numbers of calculations in parallel.

GPUs were originally developed to accelerate graphics processing, but their architecture is also well suited to many artificial intelligence workloads.

Modern GPUs are widely used to train neural networks, run AI inference, process images, and perform scientific simulations.

In robotics, GPUs help developers train models and run demanding perception or AI applications.

Key Takeaways

• GPUs use parallel processing to accelerate workloads involving large numbers of similar mathematical operations.
• They are widely used for AI training, inference, computer vision, and simulation.
• GPU performance depends on more than computing cores, including memory bandwidth, numerical precision, and software optimization.

How Does a GPU Work?

Imagine an AI model analyzing thousands of images.

The model performs extensive calculations involving arrays of numbers.

Many of these operations can be divided into smaller calculations that run simultaneously.

A GPU contains processing resources designed to execute parallel workloads efficiently.

Instead of handling each calculation sequentially, the GPU can distribute suitable operations across many execution units.

This can provide substantial performance advantages for workloads with enough parallelism.

However, not every computational problem benefits equally from GPU acceleration.

GPU vs. CPU

A CPU is a general-purpose processor optimized for handling many different computing tasks.

A GPU is designed to execute large quantities of suitable operations in parallel.

For example, a robot's CPU may coordinate navigation software, communications, and other applications.

Its GPU may process camera images through a deep learning model.

The two processors commonly work together.

Modern CPUs also support parallel processing, and GPUs can execute many types of general-purpose calculations. Their strengths differ rather than being mutually exclusive.

Why Are GPUs Important for AI?

Neural networks rely heavily on mathematical operations involving matrices and tensors.

GPUs can accelerate many of these calculations.

Specialized GPU hardware, such as matrix-processing units, can provide additional performance for supported AI workloads.

Software ecosystems such as NVIDIA CUDA help developers use GPU computing capabilities.

Other GPU computing platforms also exist.

The choice of hardware depends on model requirements, software compatibility, memory, energy consumption, and cost.

Are GPUs Used Inside Robots?

Yes.

Some robots use embedded GPU computing platforms to process camera information and run AI models locally.

For example, a mobile robot may use a GPU to execute object-detection software while its CPU coordinates navigation.

However, smaller or simpler robots may use CPUs, NPUs, microcontrollers, or other hardware instead.

What Limits GPU Performance?

A GPU's arithmetic capacity is only part of its performance.

Large models also require data to move between memory and processing units.

If memory bandwidth is insufficient, computing resources may spend time waiting for information.

Power consumption, cooling, and software efficiency also matter.

Frequently Asked Questions

Does every AI model need a GPU?

No. Smaller models and suitable workloads can run on CPUs, NPUs, and other processors.

Is a GPU the same as a graphics card?

No. The GPU is the processor. A graphics card is a larger hardware assembly that may contain a GPU, memory, cooling, and other components.

The Bottom Line

GPUs provide much of the parallel computing capacity behind modern AI. In Physical AI, they support everything from training robotic models to running vision and perception systems on deployed machines.