The Numbers That Define an LLM

2026-07-07 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

The article "The Numbers That Define an LLM" introduces Large Language Models (LLMs) as fundamental next-token predictors, explaining their core function of predicting the subsequent token given an input sequence. It highlights the common numerical metrics frequently used to describe LLMs, which can be overwhelming for newcomers. These metrics include parameters (e.g., 7B, 70B), context window size (e.g., 128k), attention heads (e.g., 32), hidden size (e.g., 4096), quantization levels (e.g., 4-bit), and inference speed (e.g., 30 tokens per second). The piece emphasizes that a "token" is the basic unit an LLM processes, often a word or part of a word, and suggests using tools like OpenAI's tiktoken to explore real-world tokenization.

Key takeaway

For AI students or software engineers beginning to explore LLMs, understanding the core numerical specifications is crucial. Familiarize yourself with terms like parameters, context window, and quantization to accurately assess model capabilities and resource requirements. This foundational knowledge will enable you to interpret technical discussions and make informed decisions when selecting or deploying different LLM architectures, moving beyond surface-level understanding to practical application.

Key insights

LLMs are next-token predictors defined by key numerical metrics like parameters and context window.

Principles

• LLMs fundamentally predict the next token in a sequence.
• Tokens are the basic processing units for LLMs.

In practice

• Explore OpenAI's tiktoken to understand tokenization.

Topics

• Large Language Models
• LLM Parameters
• Context Window
• Tokenization
• Attention Heads
• Quantization

Best for: AI Student, Software Engineer, Data Scientist

Related on AIssential

• The Language of AI: Understanding Tokens in NLP — Tokens are the fundamental units AI uses to process text, impacting cost, memory, and understanding in LLMs.
• Where can I learn the basic LLMs and local LLMs concepts? — Understanding LLM terminology like quantization, inference, and prompt processing is crucial for new practitioners.
• Why LLMs Get Math Wrong — Large Language Models (LLMs) often make mistakes in complex math problems and misinterpret tables because they process all input as flat text token sequences, treating math as patterns to predict rath…
• The Essence of LLM: Function — An LLM is a mathematical function mapping token sequences to probability distributions, driving all its emergent behaviors.
• What is an LLM? Tokens, Embeddings, and the Big Picture — LLMs operate on subword tokens, not characters, making tokenization a core determinant of their capabilities and predictable failure points.
• The Truth About Huge LLMs Context Windows — Huge LLM context windows often mask an underlying attention problem limiting practical utility.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.