Understanding and mastering key concepts in artificial intelligence, particularly generative AI and large language models (LLMs), is crucial for keeping up with technological advancements. LLMs, a subset of AI and machine learning, are designed to understand and generate human language by being trained on vast datasets. The lifecycle of these models includes training, where the model learns from data, and inference, where it applies this learning to new data. These models are characterized by parameters, weights, and tokens, each playing a unique role in their architecture and function. The size of an LLM, often measured by its parameter count, memory footprint, and compute requirements, influences its performance and accuracy. While larger models tend to excel in complex tasks, they also demand more computational resources and risk overfitting. To mitigate these challenges, techniques like quantization and sparsity are employed to reduce model size and maintain efficiency. The ongoing trend in AI involves developing increasingly larger models, such as OpenAI's GPT-3, pushing the boundaries of computational capabilities.