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Chapter 1: Foundations of LLM Inference
A large language model turns text into numbers before it can reason about anything. Each chunk of text is first split into a token, the smallest unit the model consumes. Every token is then mapped to an embedding, a dense vector that captures its meaning in a high-dimensional space.
When a user sends a request, the text they write is called a prompt. The process of running the model over that prompt to produce an answer is called inference. The time between sending the prompt and receiving the first response is the latency that users feel directly.
A minimal inference call looks like this:
def generate(prompt: str, model) -> str:
tokens = model.tokenize(prompt) # split prompt into tokens
embeddings = model.embed(tokens) # map each token to an embedding
output = model.forward(embeddings) # run inference
return model.detokenize(output)
Two numbers dominate the user experience. First, the number of tokens in the prompt, because a longer prompt costs more compute. Second, the latency of the first token, because a slow first token makes the whole system feel sluggish. Throughout this book we keep returning to these ideas: token, embedding, prompt, inference, and latency. Getting their definitions right now will save confusion later.