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CPUvsGPU
What is the difference between a CPU and a GPU?
Updated 2 min read6 differences
In short
A CPU has a few powerful cores for varied, sequential work; a GPU has thousands of simpler cores that run one operation on lots of data in parallel.
CPU
Central Processing Unit
A CPU (central processing unit) is the processor that executes a program's instructions, doing the arithmetic, logic and control work all software runs on.
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Graphics Processing Unit
A GPU (graphics processing unit) is a processor whose thousands of small cores run the same calculation on lots of data at once, for graphics and AI.
Read the page on GPUCPU and GPU compared
| Aspect | CPU | GPU |
|---|---|---|
| Cores | A few to a few dozen powerful cores | Thousands of simpler cores |
| Designed for | Varied, sequential, branching work | The same operation on lots of data in parallel |
| Memory | System RAM with large caches | Dedicated high-bandwidth VRAM |
| Strengths | Operating systems, databases, application logic | Graphics, AI, simulations, video encoding |
| Programming | Any language | CUDA, shaders, or libraries such as PyTorch |
| Role | Runs and coordinates the whole program | Accelerates highly parallel parts |
The difference, explained
A CPU is a generalist. Its cores are complex and fast, with large caches, branch prediction and the ability to run many instructions out of order, so they handle the unpredictable logic of operating systems, databases, web servers and most application code very well. A typical CPU has from a handful to a few dozen cores.
A GPU is a specialist in parallel work. It has thousands of smaller cores that run the same instructions on different data at once, which is exactly what drawing millions of pixels requires. Its own high-bandwidth memory, VRAM, feeds those cores with data. That design makes GPUs far faster than CPUs for matrix math.
That is why GPUs power modern AI. Training and running neural networks is mostly huge matrix multiplications, so frameworks such as PyTorch send that work to GPUs, typically through NVIDIA's CUDA platform. GPUs also accelerate video encoding, scientific simulation and cryptography, while the CPU coordinates the program, prepares data and runs everything else.
A common misconception is that a GPU makes any program faster. Code with lots of branching, sequential steps or small tasks runs better on a CPU, and copying data between CPU and GPU memory takes time. The two work together: the CPU runs the program, the GPU accelerates the parts that are massively parallel.
Which one should you use?
Choose CPU when…
- Your code has complex logic and many decisions.
- Tasks are small, varied or must run in sequence.
- You run general software such as web servers and databases.
Choose GPU when…
- You train or run machine learning models.
- You process graphics, video or large matrices.
- The work splits into many identical, independent operations.
Readers ask
Why are GPUs used for AI?
Neural networks are mostly matrix multiplications that break into millions of independent operations, which a GPU's thousands of cores can perform at the same time.
Can a GPU replace a CPU?
No. A computer still needs a CPU to run the operating system and coordinate work. The GPU is an accelerator for specific, highly parallel tasks.
What is an integrated GPU?
A GPU built into the same chip as the CPU and sharing system memory. It saves power and is enough for everyday graphics, while dedicated GPUs are much faster for games and AI.