What Is LLM (Large Language Model)?
Also known as: Large Language Model, Foundation Model
Definition
An LLM (Large Language Model) is a neural network with billions of parameters trained on huge volumes of text to predict the next token in a sequence, which gives it broad language understanding, reasoning, and generation ability.
LLM (Large Language Model) Explained
Nearly every modern LLM is a transformer. Input text is split into tokens, each token becomes a vector, and the model passes those vectors through dozens of layers of self-attention and feed-forward blocks. Self-attention lets every token weigh every other token in the context, which is how the model resolves references, tracks entities across paragraphs, and follows instructions. The final layer emits a probability distribution over the vocabulary, and a sampler picks the next token. The process repeats one token at a time.
Scale is what separates an LLM from earlier language models. Parameter counts run from roughly 7 billion at the small end of the useful range to hundreds of billions at the frontier. Training consumes trillions of tokens of text and costs millions of dollars in GPU time, which is why almost no enterprise trains one from scratch. Pretraining is followed by instruction tuning and preference alignment, which turn a raw next-token predictor into something that follows directions and refuses obviously harmful requests.
The critical mental model is that an LLM stores compressed statistical patterns, not a queryable database of facts. It has no lookup table and no notion of a citation unless you give it one. Facts that appeared frequently and consistently in training data are recalled reliably. Facts that are rare, recent, or specific to your company are not present at all, and the model will still produce fluent text about them. That is the origin of hallucination and the reason retrieval augmentation exists.
For enterprise work the practical questions are narrower than the headlines suggest. What is the context window, since ERP documents are long. What license governs commercial use. Can the weights be downloaded for on-prem deployment. How well does it follow structured output formats such as JSON, because most ERP integrations need parseable results rather than prose. Benchmark leaderboards correlate weakly with performance on your actual documents, so build a small internal evaluation set early.
Why It Matters
- One general-purpose model can replace dozens of narrow rule-based automations, collapsing a long backlog of point solutions into a single platform.
- Model choice sets your hardware bill: parameter count and precision directly determine how much GPU VRAM the deployment requires.
- License terms vary sharply across open-weight families, and some restrict commercial or defense use in ways that surface late in procurement.
- LLMs generate fluent text regardless of whether they know the answer, so every enterprise deployment needs retrieval, citations, and evaluation.
In Practice
A finance team asked an LLM to summarize aged receivables straight from a prompt with no data attached. The model returned a polished, plausible, entirely invented summary with specific dollar figures. Nothing was wrong with the model. It was asked to produce text about data it had never seen, and that is exactly what it did.
Frequently Asked Questions
What is the difference between an LLM and generative AI?
Generative AI is the broad category of models that produce new content, including images, audio, video, and code. An LLM is the subset specialized in text and trained on language corpora. Most enterprise generative AI projects today are LLM projects, but image generation, speech recognition, and time-series forecasting models are generative or predictive AI without being language models.
Does an LLM learn from the questions my employees ask it?
Not by default. Inference does not update model weights, so a conversation changes nothing permanent about the model. The model appears to remember only within a single context window. Cloud vendors may log prompts for abuse monitoring or future training depending on contract terms, which is a policy question rather than a technical one, and one reason regulated firms deploy on-prem.
Related Terms
SLM (Small Language Model)
An SLM (Small Language Model) is a compact language model, typically between about 1 and 15 billion parameters, designed to run on a single modest GPU or even a CPU while still handling focused enterprise tasks well.
Token
A token is the basic unit of text a language model processes, usually a word fragment rather than a whole word. In English, one token averages roughly four characters, so about 100 tokens covers 75 words.
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is an architecture that searches your own document store for passages relevant to a question, inserts them into the LLM prompt, and instructs the model to answer only from that retrieved evidence.
Go Deeper
GPU Sizing Calculator for LLM Inference
Work out how many GPUs you need to serve a given open-weight model to your user base, based on memory footprint and token throughput.
Small Language Models vs LLMs for Enterprise Workloads
Small language models vs LLMs for enterprise workloads: accuracy, cost, GPU footprint, and when a 7B model beats a frontier model for ERP automation.
On-Prem LLM Deployment Architecture: Reference Guide
Reference architecture for on-prem LLM deployment: inference servers, GPU sizing, RAG pipelines, and security zones for regulated manufacturers.
Working with LLM (Large Language Model) in a live environment? Our engineers do this every day - and our AI agents automate most of it.