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Curious how to train a large language model without drowning in technicalities? We break down the process with clear explanations and practical examples.

Learn How to Train a Large Language Model in 5 Steps

You’ve seen what large language models can do, from chatbots that sound almost human to software that spits out working code in seconds. But what happens behind the scenes before an AI like that comes to life? It all starts with one big task: training.

From ChatGPT to Claude to Gemini, every advanced AI assistant you see today started the same way: massive datasets, powerful computers, clever math, and months of training.

To go from clueless to eerily coherent, LLMs are fed billions of words f

You’ve seen what large language models can do, from chatbots that sound almost human to software that spits out working code in seconds. But what happens behind the scenes before an AI like that comes to life? It all starts with one big task: training.

From ChatGPT to Claude to Gemini, every advanced AI assistant you see today started the same way: massive datasets, powerful computers, clever math, and months of training.

To go from clueless to eerily coherent, LLMs are fed billions of words from books, articles, and online chatter. Sure, they simply spot patterns and make text predictions, but with enough data and computing power, they get remarkably good at guessing what comes next.

But that’s just the beginning. There’s also cleanup, fine-tuning, evaluation, and a whole lot of engineering in between. This article briefly looks at what happens during an LLM’s training and how they become the AI assistants we interact with today.


What Does It Mean to Train a Large Language Model (LLM)?

Training an LLM isn’t so much about teaching facts as it is about teaching probabilities. You give the model billions of words from books, forums, articles, and conversations, and ask it to predict what word should come next (like learning that “peanut butter” is more likely to be followed by “and jelly” than “and car tires”).

LLMs start out clueless, make a lot of bad guesses, and get nudged closer to the right ones each time. They don’t truly “understand” anything (at least not yet); they just get really good at guessing what comes next. Over time, LLMs become much better at completing sentences, answering questions, conversing in your tone, writing code etc.

Practically speaking, LLM training happens in three main phases:

 1. Pre-training: The model studies general text (books, websites, code) to learn basic language patterns. Think of this as the general-purpose school for AI.
 2. Fine-tuning: The model gets specialized training for specific tasks, like being helpful in a chat or writing safer, more accurate replies. This is like vocational training after graduation.
 3. Prompting: This is the act of giving the model a specific task to complete. When you ask an LLM to write up an essay or code in a specific language, you’re prompt the model.

Without training, an LLM is like a library with no index—full of data but useless at finding the right answer. Training shapes how it responds, what it prioritizes, and even when it refuses to answer.


Key Ingredients You Need to Train an LLM

Before training an LLM, you need several resources in place. If even one piece is off, the whole thing can collapse into a barely literate mess. Here’s what goes into the mix:

 1. Massive Datasets: LLMs learn by example, so they need a lot of them. Think terabytes of text from books, Wikipedia entries, social media threads, code repositories, and web crawls. But you need a balance of quantity and quality. Repetitive or biased training data can often skew how a model “thinks.”
 2. Computing Power: Training models like GPT or Claude involves running trillions of calculations. That takes fleets of GPUs (specialized chips designed for parallel math). Most teams rent this power from cloud providers like AWS, Google Cloud, Azure, and specialized players like TensorWave.
 3. Model Architecture: Modern LLMs use transformer architectures with attention mechanisms that help the model focus on relevant parts of text. But size isn’t everything. More parameters mean more capacity, but also more cost and risk. Balancing size, depth, and efficiency is part science, part art.
 4. Training Algorithms: With your data and model ready, it's time to teach your LLM. You'll need loss functions that tell the model how wrong it is, and optimizers (like Adam or SGD) that help it improve. Efficiency tricks like mixed-precision training or gradient checkpointing can stretch your compute budget further without sacrificing quality.

And that’s the base recipe. Everything else (model from safety to speed) builds on these four ingredients.


How to Train a Large Language Model (Step by Step)

Training an LLM from scratch is no small feat. It’s also not cheap. Case in point, OpenAI’s CEO confirmed that training GPT-4 cost over $100 million. While outsourcing LLM training to a specialized service is faster, it’s often the most expensive path.

Another alternative (particularly for businesses with strong machine learning teams) is to fine-tune an existing model on custom data. So if your team has the technical chops, here’s how the LLM training process typically unfolds:


Step 1: Set Clear Training Goals

Before touching any code or data, nail down exactly what you need your LLM to do. Will it generate marketing content? Answer customer questions? Summarize legal documents? Write Python scripts? Or do something else entirely? Your goal shapes everything from the type of data you collect to the specific model you choose to train.

A clear objective also helps you avoid overtraining a general-purpose model when a smaller, focused one will do the job better. Plus, it forces you to think about real-world use early so you’re not just training a model for training’s sake.

Perhaps most importantly, setting clear goals determines your evaluation metrics. A customer service model might prioritize factual accuracy and tone, while a code generator needs technical precision.


Step 2: Build Your Data Foundation

Your LLM will only be as good as the data it learns from. After all, your training data is your LLM’s only window to understanding the world. The more high-quality and relevant your dataset is, the better your model turns out.

For general-purpose models, you’ll need billions of words spanning diverse topics from websites, conversations, etc. Specialized models might focus on industry-specific content like legal documents, financial details, scientific papers, or customer support tickets.

Where do you find this data? Public sources include Common Crawl, GitHub repositories, Kaggle, Hugging Face, Data.gov, and Google’s Dataset Search. Depending on your use case, you might add academic articles, transcripts of human dialogue, etc.

For domain-specific training, your company’s internal documents and knowledge bases provide unique value. Once you’re done collecting data, you’ll need to:

 * Filter out toxic, harmful, and biased text
 * Remove duplicates, broken formatting, and junk content
 * Convert everything to a consistent format (e.g., using lowercase all through)
 * Strip all personally identifiable information (especially if you’re using real user input)

Next comes tokenization (i.e., breaking texts into smaller chunks, called tokens, that the model can understand). Most modern LLMs use subword tokenization (like Byte Pair Encoding), which breaks down unfamiliar words into parts it can recognize.

Lastly, be mindful of ethics. Just because something is publicly available doesn’t always mean it’s fair game. Respect licenses, authorship, privacy, and similar ethical boundaries wherever possible.


Step 3: Choose Your Model Architecture

Like choosing between a pickup truck and a sports car, different model architectures serve different purposes. The transformer architecture forms the backbone of modern LLMs, but you'll need to make key decisions about its configuration, including:

 * Size: Bigger isn’t always better. While GPT-4 has over a trillion parameters, smaller models with 1-7 billion parameters can perform remarkably well for specific tasks with much lower computing costs.
 * Mixture-of-Experts (MoE): This approach divides your model into specialized “expert” networks that activate only for certain inputs. Google’s Gemini and Meta’s LLaMA 3 use this approach to achieve better results without proportional increases in computing needs.
 * Architecture specifics: When configuring your model, you’ll define things like:
   * Number of experts (if using MoE)
   * Number of layers (depth of your model)
   * Loss function (how the model calculates its own mistakes)
   * Hyperparameters like learning rate, batch size, and optimizer
   * Attention heads (how the model focuses on different parts of the input)

These choices affect not just how well the model performs, but how long it takes to train. Go too big, and you’ll need serious hardware and time. Go too small, and the model may struggle with more complex tasks. It’s all about finding the right fit for your data, goal, and budget.


Step 4: Train the Model

Training is where the model actually learns. It takes all that preprocessed data and starts making predictions, usually one word at a time. For example, if the sentence is “The cat sat on the ___,” the model might guess “mat.” If it’s wrong, it adjusts its internal weights slightly. Then it tries again. And again. Millions of times.

This process is called supervised learning, and while the idea is simple, the scale is not. Language models often have hundreds of billions of parameters to adjust. Training a model of that size takes huge datasets and massive computing power.

To speed things up, developers use something called model parallelism. This involves spreading the model out across multiple graphics processing units (GPUs) or servers so it can be trained in pieces, all at once. There are three main ways this happens:

 1. Data parallelism: The same model is copied across different GPUs. Each GPU trains on a different slice of data.
 2. Pipeline parallelism: The model itself is chopped into stages, with each GPU handling a different layer or group of layers (like an assembly line).
 3. Tensor parallelism: A single layer is split across GPUs. This is useful when even one layer is too big to fit on a single chip.

These approaches often get combined (known as 3D parallelism) to handle large models. Here’s a visual representation it what it looks like from AWS:

The math gets complex, but the point is simple: by breaking up the load, you can train massive models faster and more efficiently. Still, this step is where most of your costs and time go. You’ll need GPUs, optimized training frameworks, distributed computing setups, and engineers who know how to keep it all running smoothly.


Step 5: Evaluate and Fine-Tune Your Model

You’ve trained your model, but it’s not done learning yet. Evaluation and fine-tuning take your model from “technically working” to “actually useful.” You’ll typically use benchmarks like MMLU, HumanEval, and HellaSwag to measure things like knowledge, coding ability, and factual correctness.

But for specialized use cases, you’ll also want custom test sets that reflect your actual domain. Legal teams can, for instance, test contract analysis, while customer service apps need conversation handling tests. The goal is to find blind spots. If the model hallucinate facts or misinterprets instructions, you’ll need to resolve these issues before deployment.

Fine-tuning addresses the weaknesses you’ve found. This process uses much smaller, higher-quality datasets than pre-training, often with human-labeled examples. There are several approaches:

 * Supervised fine-tuning (SFT): Training on examples of ideal outputs, usually created by humans. This teaches the model to follow instructions and match preferred response styles.
 * Reinforcement Learning from Human Feedback (RLHF): Having humans rank different model responses from best to worst, then training the model to generate higher-ranked outputs. This dramatically improves output quality but requires careful implementation.
 * Parameter-efficient fine-tuning: If you don’t want to retrain the whole model, techniques like LoRA or adapters let you train small “add-on” layers while keeping the base frozen. This saves time and compute, which makes it ideal for teams with tighter budgets.

Throughout fine-tuning, you’ll continue evaluation in a cycle of test → adjust → test again. The model will gradually improve, but watch for signs of overfitting (performing well on training data but poorly on new inputs) or unwanted behavior shifts.


Training Options: DIY vs. Specialized Infrastructure

Training LLMs demands serious computing muscle, which isbsomething most companies can’t justify building in-house. Thankfully, AI infrastructure providers like TensorWave offer a compelling alternative. Our platform uses AMD’s latest accelerators to create purpose-built environments for LLM training without the headaches of managing hardware.

The advantage? You skip months of infrastructure setup and focus on what matters: your data and model development. While hyperscalers like AWS and Azure offer similar services, specialized providers often deliver better price-performance ratios for AI workloads specifically. Get in touch today.


Key Takeaways

Training an LLM isn’t just for tech giants anymore, but it still requires thorough planning. Start with clear goals, invest heavily in data quality, and be realistic about computing requirements. Most teams will find fine-tuning existing models more practical than building from scratch.

TensorWave can dramatically cut your time-to-results by handling the complex hardware setup while you focus on what makes your model unique. Whichever path you choose, LLM training rewards patience and methodical execution. Want to learn more? Connect with a Sales Engineer today.

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