What hardware is used to train deep learning models?

Deep learning models are trained on hardware that can perform large numbers of matrix and tensor operations efficiently.

Common hardware

• CPUs: useful for data loading, preprocessing, orchestration, and smaller models, but usually slower than accelerators for large neural-network training.
• GPUs: the most common hardware for deep learning because they can perform many calculations in parallel.
• TPUs: specialized accelerators designed for machine-learning workloads, especially large tensor operations.
• Other AI accelerators: specialized chips from different vendors can also be used for training and inference.

Why are GPUs widely used?

Neural networks rely heavily on operations such as matrix multiplication.

GPUs contain many processing cores that can execute these calculations in parallel, making them much faster than CPUs for many deep-learning workloads.

Training data
     ↓
CPU handles loading/preprocessing
     ↓
GPU / accelerator performs tensor computations
     ↓
Model parameters are updated

What about memory?

Accelerator memory, such as GPU VRAM, is important because it stores model parameters, activations, gradients, and batches during training.

Larger models or batch sizes usually require more memory.

Training very large models

When one accelerator is not enough, training can be distributed across multiple GPUs or other accelerators, sometimes across many machines.

This can use techniques such as data parallelism and model parallelism.

In short, deep learning is commonly trained using GPUs or other AI accelerators, while CPUs handle supporting tasks such as data preparation and system coordination.