AI enables machines to learn, reason, and make decisions similar to humans.

AI is a field; Machine Learning learns from data; Deep Learning uses neural networks for complex tasks.

It creates new content, text, images, or code, by learning patterns from existing data.

A massive AI model trained on text to understand and generate human-like language.

Training teaches a model using large datasets; inference applies the trained model to make predictions or outputs.

A GPU (Graphics Processing Unit) is a parallel processor built for graphics and now used to accelerate AI and data workloads.

GPUs perform thousands of calculations at once, dramatically speeding up AI model training and inference.

High-performance CPUs, GPUs or accelerators, fast NVMe storage, and low-latency networking.

Training uses multi-GPU setups and large memory; inference runs on smaller, optimized systems for fast results.

Fast SSDs and high-throughput networking to handle large datasets and prevent bottlenecks.