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OpenAI reportedly spent over $100 million training GPT-4, making it one of the most expensive machine learning models ever created. By 2026, frontier model training runs are estimated to cost $500 million to $1 billion.

The datasets used to train frontier large language models are massive, often consisting of trillions of tokens. For example, the dataset for Llama 3 was over 15 trillion tokens, sourced from a vast collection of publicly available text and code from the internet.

In-context learning is the ability of a large language model to learn a new task at inference time simply by being provided with a few examples in the prompt (few-shot prompting), without any updates to the model's weights. This is a powerful, emergent property of scale.

Mixture of Experts (MoE) is an architecture where instead of one large neural network, the model consists of many smaller 'expert' networks. For any given input, a routing mechanism selects a small subset of these experts to process it. This allows models to scale up their parameter count dramatically while keeping computational costs manageable.

Emergent abilities are phenomena where AI models suddenly demonstrate new, unpredictable skills once they reach a certain scale (i.e., more parameters and training data). These abilities are not explicitly programmed but emerge from the model's complexity, such as performing arithmetic or answering logic puzzles.

Set up monitoring for your production LLM applications to track token usage patterns. This helps identify optimization opportunities and avoid unexpected costs when usage scales up.

Set up monitoring for your production LLM applications to track token usage patterns. This helps identify optimization opportunities and avoid unexpected costs when usage scales up.

Large language models exhibit emergent abilities that weren't explicitly trained for, such as few-shot learning, chain-of-thought reasoning, and code generation, which appear at certain model scales.