The GPU — Graphics Processing Unit — was originally designed to render images and video. Today, GPUs are widely used for tasks far beyond graphics, including scientific computing, data processing, and AI training. Understanding what a GPU does and why it differs from a CPU helps explain why it appears so frequently in discussions about AI infrastructure.
What a GPU Does
A GPU is designed for parallel processing. Where a CPU has a small number of powerful cores optimized for sequential tasks, a GPU has thousands of smaller cores designed to perform many simple calculations simultaneously. This makes GPUs well-suited for tasks that can be broken into many independent operations running at the same time.
Rendering a 3D image, for example, requires calculating the colour and brightness of millions of pixels independently. A GPU can process many of these calculations in parallel, completing the task far faster than a CPU could sequentially.
CPU vs GPU — Different Strengths
- CPU — few powerful cores, optimized for complex sequential logic, general-purpose tasks, running operating systems and applications
- GPU — thousands of smaller cores, optimized for parallel arithmetic, graphics, scientific simulation, and AI workloads
Most computers use both. The CPU handles general program execution; the GPU handles tasks that benefit from parallelism.
GPUs and AI
Training an AI model involves performing enormous numbers of mathematical operations — specifically matrix multiplications — on large datasets. These operations are highly parallelizable, which makes GPUs well-suited for the task. Training a large model on a CPU would take impractically long; GPUs can perform the same calculations orders of magnitude faster.
Inference — using a trained model to generate output — also benefits from GPU acceleration, particularly for large models or high-volume applications.
Multiple manufacturers produce GPUs used in AI and data work. The underlying principle — parallel processing of matrix operations — applies regardless of manufacturer. When evaluating GPU options for a specific workload, consider software compatibility, memory capacity, and total cost of ownership alongside raw performance.
GPU Memory
GPUs have their own dedicated memory, separate from system RAM. This memory — often called VRAM — holds the data the GPU is actively processing. For AI workloads, the amount of GPU memory limits the size of models that can be loaded and the batch size that can be processed at once. Running large language models locally requires GPUs with substantial memory.
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