What are large language models (LLMs), how do they work, and popular use cases

Large language models (LLMs) are at the core of today’s most advanced artificial intelligence (AI) systems. They power everything from chatbots and content tools to code generators and virtual assistants.

In this article, you’ll learn what large language models are, how they work, and why they matter. You’ll also explore real-world LLM use cases, popular examples, and how to deploy models on your server.

By the end of this guide, you’ll have a clear understanding of how LLMs process human language – whether you’re simply curious about tools like ChatGPT and Gemini or planning to build smarter apps yourself.

What are large language models?

Large language models are advanced AI systems trained on vast amounts of text data to understand and generate human-like language.

LLMs are called “large” because they often contain billions or even trillions of learned parameters. This scale helps them capture complex patterns in human text and produce contextually relevant responses.

They rely on transformer architecture – a neural network designed to process language with greater accuracy and contextual awareness than older models.

Nowadays, LLMs power different app types, including chatbots, search engines, virtual assistants, productivity tools, and web app development agents.

How do large language models work?

LLMs predict the next word in a sentence, so they can perform tasks like answering questions, translating documents, writing code, or summarizing research papers.

They use a deep learning architecture called the transformer, which captures relationships between words in a sequence, even when far apart.

This design helps LLMs understand context, meaning, and intent more effectively than traditional models like recurrent neural networks (RNNs) and statistical language models.

During training, the model learns to predict the next token (a word or word piece) based on the surrounding context. Over time, it adjusts numerous parameters to handle increasingly complex tasks.

To work effectively, LLMs rely on several key components:

• Tokenization – the input text is broken into smaller tokens that the model can process.
• Attention mechanism – the model uses self-attention to identify which input parts are most relevant to each token prediction.
• Parameters – these weights are adjusted during training to help the model recognize patterns in language.

Popular machine learning frameworks used to train and run LLMs include:

• TensorFlow – a flexible, open-source platform developed by Google.
• PyTorch – a developer-friendly framework widely used for research and production.
• Hugging Face Transformers – a library that makes working with pre-trained transformer models easier and more accessible.

Modern LLMs also use advanced learning techniques like:

• In-context learning – learns to complete a task based on examples provided in the prompt.
• Few-shot learning – performs a task after seeing only a few examples.
• Zero-shot learning – handles tasks without seeing any prior examples.

These techniques let LLMs generalize across a broad range of tasks without retraining, making them highly adaptable and efficient.

Large language model use case examples

LLMs power a wide range of tools and workflows across industries. Here are some of the most practical ways they’re used today.

Customer support automation

LLMs can answer customer questions in real time, reduce response times, and improve satisfaction. Connecting an LLM with automation tools like n8n lets you build AI-driven support tickets, chatbot replies, or smart routing – all without manual input.

Content creation

LLMs help writers brainstorm ideas, draft content, and refine tone and structure. For instance, you can use LLMs to generate marketing copy, social media captions, or product descriptions – streamlining content creation without replacing human review or final editing.

Language translation and localization

LLMs can quickly translate content while preserving tone and context, making them ideal for global content strategies. Unlike traditional translation tools, they can adapt to nuance and cultural variation with minimal configuration.

SEO and data analysis

LLMs extract insights from large datasets, analyze sentiment, and generate summaries to speed up decision-making. They also help optimize website content by suggesting edits aligned with search engine best practices.

Code generation and debugging

LLMs trained in programming languages can generate code snippets, explain syntax, and detect bugs across languages. Developers can use these tools to build features faster, reduce errors, and learn new technologies through natural-language prompts.

Web app development

LLMs can build fully functional web apps from a simple prompt, making development accessible to users without coding experience. For instance, you can use an AI web app builder like Hostinger Horizons, which handles everything from layout to logic based on what you type in.

Retrieval-augmented generation (RAG)

Some advanced setups combine LLMs with external data sources to generate accurate, up-to-date responses. This technique, known as retrieval-augmented generation, improves accuracy using real-time information.

Specialized tasks with fine-tuned models

Organizations can customize LLMs for specialized tasks like legal document analysis, medical summarization, or compliance checks. Fine-tuning enhances model performance for specialized content while reducing errors and increasing relevance.

Most popular large language models

With so many LLM options available today, it’s important to understand the strengths of each. Below are some of the most popular models and what makes them stand out:

• Generative Pre-trained Transformer (GPT) – a transformer-based model known for its versatility, accuracy, and ability to handle nuanced prompts. It’s widely used in content creation, coding assistance, chatbots, and AI agents.
• Bidirectional Encoder Representations from Transformers (BERT) – optimized for understanding language rather than generating it. BERT excels at sentiment analysis, question answering, and classification tasks.
• Language Model for Dialogue Applications (LaMDA) – fine-tuned for open-ended conversation. While it’s designed for chatbots and virtual assistants, it remains research-focused and is not publicly available for broad use.
• Large Language Model Meta AI (LLaMA) – an open-source series known for its efficiency and strong performance across various model sizes. Developers and researchers use LLaMA to build custom AI apps and self-hosted deployments.
• Text-to-Text Transfer Transformer (T5) – reframes all natural language processing (NLP) tasks as text-to-text. It performs well across tasks like translation, summarization, classification, and question answering.
• Gemini – a multimodal model that processes and generates both text and images. It excels at creative and analytical tasks that require complex contextual understanding.
• Grok – designed to be less filtered than other models. It supports candid, often humorous conversations and handles controversial topics more openly. It’s best suited for casual, entertainment-focused interactions – not for tasks requiring strict safety or neutrality.
• Claude – built for intuitive, safe, and helpful AI conversations. It excels at long-form Q&A, summarization, document understanding, and coding. Claude is known for strong alignment and handling long contexts.
• Mistral – an open-source model focused on flexibility and performance, especially in resource-constrained or low-latency environments. It supports fine-tuning and works well in custom deployments.

To help you choose the right model, take a look at this quick comparison:

Model	Developer	Access	Size (parameters)	Speed	Best use cases
GPT	OpenAI	Closed-source (API only)	~1.8T (GPT-4)	Medium to slow	General-purpose AI, creative writing, code generation, advanced chatbots
BERT	Google	Open-source	1.27B (BERT. xlarge)	Fast	Text classification, sentiment analysis, search ranking
LaMDA	Google	Closed-source (limited access)	Up to 137B	Medium	Conversational AI, dialogue systems (research-focused)
LLaMA	Meta	Open-source (with request)	400B (LLaMA 4 Maverick)	Fast to medium	Custom AI apps, academic research, open-source deployments
T5	Google	Open-source	~11B (T5-11B)	Medium	Translation, summarization, extractive and generative Q&A
Gemini	Google	Closed-source (API only)	No official number	Fast	Multimodal tasks, coding, reasoning, research and enterprise use
Grok	xAI	Open-source	314B (Grok-1)	Medium	Edgy conversations, opinionated dialogue, humor, casual Q&A
Claude	Anthropic	Closed-source (API only)	No official number	Medium	Long-form reasoning, safe AI for business, document Q&A, coding
Mistral	Mistral AI	Open-source	140.6B (Mixtral-8x22B-v0.3 )	Fast	Low-latency apps, fine-tuned deployments, structured data extraction

Larger models with trillions of parameters often generate more accurate and nuanced responses, but they require more computing power. Smaller models, on the other hand, run faster and use resources more efficiently.

But parameter count isn’t everything – training quality, data diversity, and model architecture play a major role too. For example, a well-trained 70B model can outperform a poorly optimized 175B one.

Which model is best for what?

Some LLMs are better at classifying and understanding language, while others excel at generating content or handling complex conversations. Use this guide to choose the right one:

• Use GPT for creative writing, coding, and natural dialogue with detailed context.
• Choose BERT when classifying content, extracting entities, or working with sentence-level understanding.
• Go with LLaMA if you need an open-source model for custom deployments, speed, and flexibility.
• Pick LaMDA for research or theoretical discussions, since public access is limited.
• Consider T5 for flexible NLP tasks like summarization, translation, and extractive Q&A.
• Use Gemini for multimodal projects that involve both text and images.
• Pick Grok for casual or edgy chatbot experiences that prioritize humor and candid responses.
• Choose Claude for long-form reasoning, safe interactions, document Q&A, and coding support.
• Go for Mistral if you need fast, open-source LLMs for fine-tuned apps or lightweight, low-latency use cases.

Self-hosting and deploying LLMs on a VPS

Running an LLM on your virtual private server (VPS) gives you better control, privacy, and flexibility. It’s beneficial when working with sensitive data, experimenting with prompt engineering, or fine-tuning models for specific tasks.

Self-hosting is ideal for developers, businesses, and researchers who want to deploy open-source language models like LLaMA or Mistral, avoid third-party limitations, and reduce ongoing costs.

There are several ways to run an LLM on a VPS. One is to use Ollama, a lightweight model runner that simplifies LLM deployment.

To install Ollama on your VPS, make sure your server has at least 4 to 8 CPU cores, 16 GB of RAM, and 12 GB of disk space.

Hostinger’s LLM hosting is an excellent option for running Ollama. Our KVM 4 plan – 0/month – includes 4 CPU cores, 16 GB of RAM, and 200 GB of NVMe storage, which should be enough for most use cases.

Hostinger also simplifies deployment with a preconfigured template that lets you install Ollama with just a few clicks – perfect for users with little to no technical expertise.

Once you’ve installed Ollama on your VPS, you can run your preferred model and fine-tune it using command-line tools. Follow our Ollama CLI tutorial to get started.

Alternatively, run Ollama through a graphical interface using OpenWebUI, which gives you a ChatGPT-style experience – ideal for easier collaboration with teammates or clients.

Conclusion

Large language models have transformed industries with their ability to process and generate human-like language responses.

Before choosing a model, define your goals, understand each model’s strengths, and consider your technical setup. Focus on training data quality, task-specific tuning, and deployment options – whether you’re building a chatbot, coding assistant, or internal tool.

Once you’re ready, deploy an LLM on your VPS and fine-tune it to fit your specific needs. This way, you gain full control over your AI stack and can create custom apps that enhance your workflows.

Large language models FAQ