While the CPU acts as the computer's general manager and RAM provides the working memory, many modern computing tasks, especially in Artificial Intelligence (AI), require a different kind of processing power. This is where the Graphics Processing Unit (GPU) comes in.
Originally designed to handle the complex calculations needed to render 3D graphics for video games and visual applications, developers discovered that the unique architecture of GPUs made them exceptionally well-suited for the types of calculations common in scientific computing and AI.
What makes a GPU different from a CPU? The main difference lies in their design philosophy. A CPU typically has a small number of very powerful cores, optimized for performing complex tasks sequentially or handling a few tasks at once. Think of them as highly skilled specialists who can handle intricate instructions one after another very quickly.
A GPU, on the other hand, usually contains hundreds or even thousands of simpler cores. These cores aren't as powerful individually as CPU cores, but they excel at performing the same operation on many different pieces of data simultaneously. This capability is known as parallel processing.
Imagine you have a thousand simple addition problems to solve. A CPU (with a few fast cores) would work through them rapidly, but largely one after the other. A GPU (with thousands of simpler cores) could assign one addition problem to each core and solve almost all of them at the same time.
A simplified view comparing CPU and GPU architectures and their optimized task types.
Large Language Models, like other deep learning models, rely heavily on mathematical operations performed on large collections of numbers arranged in matrices and vectors. A core operation is matrix multiplication. Training and running LLMs involves countless such multiplications.
Consider multiplying two large matrices. This involves many individual multiplications and additions. A GPU's parallel architecture is perfectly suited for this. It can compute many of these small calculations simultaneously, drastically speeding up the overall process compared to a CPU attempting the same task. The performance of hardware for these tasks is often measured in FLOPS (Floating-Point Operations Per Second), and modern GPUs achieve much higher FLOPS counts than CPUs, especially for parallel workloads. A higher FLOPS number generally indicates a greater capacity for computation.
Example comparison showing how much faster a GPU might complete a massively parallel task (like large matrix operations) compared to a CPU. Actual speedup varies greatly depending on the specific task and hardware.
When you interact with an LLM (a process called inference), the model's parameters (millions or billions of numbers) are used in complex calculations to understand your input and generate a response. These calculations are inherently parallel. Using a GPU allows these operations to happen much faster, resulting in quicker response times from the LLM. For very large models, using a GPU is often not just faster, but practically necessary to get a response in a reasonable amount of time.
In summary, while the CPU manages the overall system, the GPU acts as a specialized accelerator, tackling the massive parallel computations needed for AI tasks like running LLMs. Its ability to perform countless simple operations simultaneously is fundamental to the performance of modern AI applications. We'll see in the next section how the GPU's own dedicated memory, VRAM, is also critically important.
Was this section helpful?
© 2026 ApX Machine LearningEngineered with