GPUs are used for AI instead of CPUs because their parallel processing architecture is ideal for the massive, repetitive calculations required for tasks like training neural networks and running inference. GPUs have thousands of cores that can perform many simple operations at once, while CPUs use fewer, more powerful cores designed for sequential tasks.
Key differences and why GPUs are better for AI
- Parallel vs. Sequential processing:
- GPUs: Designed for parallel processing, meaning they can break down a large problem into many smaller pieces and solve them all at the same time using their thousands of cores.
- CPUs: Designed for sequential processing, excelling at handling complex, one-off tasks one after another. They are not efficient at the kind of repetitive, parallel work needed for AI.
- Massive data and matrix math:
- AI, especially deep learning, relies heavily on matrix multiplication and other complex math that can be performed simultaneously on large datasets.
- GPUs are built to handle these calculations much faster than CPUs, enabling them to accelerate the training of AI models by up to 100 times.
- Specialized hardware:
- Modern GPUs are engineered with specialized hardware, such as tensor cores,which are specifically optimized to accelerate the matrix and tensor operations that are the foundation of modern AI and deep learning.
- Throughput vs. speed:
- A CPU might be faster at a single, simple operation (like a CPU-based calculator), but a GPU can achieve far greater overall throughput by performing thousands of simple operations at once (like a GPU-powered image processor).
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