Why AI Models Use GPUs
AI models, especially deep learning models, must perform enormous amounts of computation to train on and infer from massive data. Since using only CPUs is too slow and inefficient for this work, GPUs specialized for large-scale parallel computation are essential.
What Is a GPU?
- A GPU (Graphics Processing Unit) was originally designed to quickly perform graphics operations, such as pixel rendering and 3D graphics processing.
- The reason graphics in games and videos can appear smooth without stuttering is also the GPU’s fast parallel computation.
CPU vs GPU Structure Comparison
| Category | CPU | GPU |
|---|---|---|
| Number of cores | A few high-performance cores (4-32) | Thousands to tens of thousands of small cores |
| Processing method | Sequential processing | Parallel processing |
| Strengths | General logic processing, complex branch handling | Large volumes of simple repetitive operations, matrix/vector operations |
| Suitability for AI | Low | Very high |
The matrix multiplication and vector operations required for deep learning training fit perfectly with the GPU’s parallel processing method.
Why GPUs Are Essential for AI
Parallel Computing Ability
- Deep learning models must simultaneously calculate many parameters and connections between neurons.
- GPUs process these all at once in parallel, dramatically improving speed.
Matrix/Vector Operation Optimization
- Neural networks are made up of many matrix and vector multiplications.
- Example:
y = Wx + b(weight matrix x input vector + bias) - GPUs were originally optimized for matrix operations for graphics processing, such as pixel calculation and 3D rendering, so they fit AI computation well.
Shorter Training Time
- During model training, millions to billions of parameters must be updated.
- Training that would take weeks or months using only CPUs can be reduced to hours or days with GPUs.
Large-Scale Data Processing
- AI handles high-dimensional data such as images, speech, and text.
- GPUs can process large batches of data simultaneously, making training and inference faster.
Stronger Inference Performance
- GPUs provide fast responses not only for training but also for real-time services, such as chatbot responses, image/speech recognition, and autonomous driving sensor data analysis.
Ecosystem Support
- Representative deep learning frameworks such as PyTorch and TensorFlow are optimized for GPUs based on CUDA, NVIDIA’s library.
- When using GPUs, optimized kernels can be used automatically, providing additional performance gains.
Relationship Between GPUs and AI
Early AI researchers realized that CPUs alone had limitations when training on large amounts of data. They applied graphics-processing GPUs to deep learning training, and the parallel computation structure matched AI perfectly. Since then, AI and GPUs have become inseparable, and most AI research and services today are built on GPU-based systems.
AI-Specific Hardware Beyond GPUs
Recently, AI-specialized chips other than GPUs have also been developed and used.
- TPU (Tensor Processing Unit): Developed by Google and optimized for tensor operations
- NPU (Neural Processing Unit): For mobile devices and optimized for energy efficiency
- FPGA, ASIC: Custom chips specialized for specific AI operations
However, GPUs are still the most widely used in terms of versatility, performance, and ecosystem.
Summary
- CPU = strong at sequential processing (general-purpose processor)
- GPU = specialized for parallel computation (optimized for AI and deep learning)
- The reason AI models use GPUs = to process large-scale matrix/vector operations quickly and simultaneously