What is a Large Language Model (LLM)? Neural Architectures Explained

Overview #

A Large Language Model (LLM) is a deep neural network trained on vast text corpora using transformer self-attention mechanisms to understand, generate, reason with, and translate natural language.

Transformer Architecture & Self-Attention #

Introduced in 2017 ('Attention Is All You Need'), transformer neural networks revolutionized AI by replacing recurrent neural networks (RNNs) with multi-head self-attention. Instead of processing text sequentially, transformers evaluate relationships between all tokens across a sequence simultaneously. This mathematical mechanism allows models like Claude 3.5 Sonnet and GPT-4o to understand nuanced long-distance dependencies across hundreds of thousands of words.

Pretraining, RLHF & Post-Training Alignment #

Building a frontier LLM involves three critical phases:

1. Self-Supervised Pretraining: Next-token prediction across trillions of tokens to learn grammar, factual knowledge, and reasoning primitives.
2. Supervised Fine-Tuning (SFT): High-quality conversational and coding demonstration datasets to train the model into a responsive assistant.
3. Reinforcement Learning from Human/AI Feedback (RLHF / RLAIF): Direct preference optimization (DPO) and test-time reasoning compute reinforcement (as seen in DeepSeek R1 and OpenAI o1) to verify mathematical steps and prevent hallucinations.

Parameter Count vs. Inference Efficiency #

Model capabilities were historically dictated purely by dense parameter counts (e.g. 70B, 405B). In 2026, architectural advances such as Mixture of Experts (MoE) activate only a sparse subset of parameters per token (e.g. 37B active parameters out of 671B total in DeepSeek V3/R1). This drastically cuts server compute and latency while maintaining state-of-the-art cognitive performance.

Code Example: Querying Deep Reasoning LLMs via API100 #

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.apihundred.com/v1",
  apiKey: process.env.API100_API_KEY,
});

// DeepSeek R1 executes test-time reasoning before outputting final answers
const completion = await client.chat.completions.create({
  model: "deepseek-r1",
  messages: [{ role: "user", content: "Solve this discrete mathematics proof." }],
});

console.log(completion.choices[0].message.content);

Frequently Asked Questions #

Q: What is an LLM?

An LLM (Large Language Model) is a machine learning model built on transformer neural architectures trained on billions to trillions of parameters to process and generate natural language and code.

Q: What is Mixture of Experts (MoE)?

Mixture of Experts is an architectural technique where an LLM routes each incoming token to a specialized sub-network (expert), activating only a fraction of total parameters per token to achieve high intelligence at lower compute cost.