Concept of Large Language Models (LLMs).
- saurabhkamal14
- 7 days ago
- 3 min read
Understanding Large Language Models (LLMs)
Large Language Models (LLM) are powerful neural networks, typically comprising billions of parameters, that are trained on huge volumes of text so that they can understand, generate, and reason about human-language content. They mark a major shift on how natural-language processing (NLP) systems are built and deployed.
Key Characteristics
Scale: LLMs often run into billions of parameters. For example, some well-known models push into the 100s of billions (or even beyond)
Training data: They draw on massive text corpora - web pages, books, articles - enabling rich language understanding.
Architecture: Most are based on the Transformer model (self-attention, multi-head attention, etc.).
Capabilities: Thanks to their scale + training, LLMs can do things like text-generation, summarization, language translation, question-answering, and even some forms of reasoning.
How LLMs Work - A simplified View
Pre-training: In the pre-training phase, a LLM is given a very large volumes of text data (books, articles, web pages, etc.). The idea is that the model learns the general patterns of language - grammar, syntax, semantics, factual knowledge, how words relate to one another in context - without being told a specific task.
Fine Tuning: After pre-training, the model is adapted (or refined) for a specific task, domain or business use-case. This is the fine-tuning stage.
Inference: Once the model is trained (pre-trained + fine-tuned), you run it in production or for use cases. This is inference: feeding a prompt or input, and the model generates output (text) based on its learned language patterns.
Practical Analogy
Think of an LLM like a "super-reader" who has read millions of books and articles, and now can:
Autocomplete sentences
Answer questions (like a knowledgeable conversation partner)
Write stories or articles (creative writing assistant)
Translate between languages
Architecture Flow
Input Text → The raw human-readable sentence or prompt that you feed into the model.
Tokenization → The process of splitting the input into smaller units (tokens) and mapping them to numeric identifiers.
Embeddings → Each token ID is converted into a continuous vector representation that captures semantic meaning.
Transformer Layers → A stack of self-attention and feed-forward layers that process embeddings to build contextual language understanding.
Output Probabilities → The model computes a probability distribution over the vocabulary for what the next token (or sequence of tokens) should be.
Generated Text → The selected tokens are mapped back to human-readable text, producing the model's final output.
Computational Requirements for Training an LLM?
Training an LLM is non-trivial in terms of infrasturcture, cost, time, and engineering complexity. Below are the key dimensions.
Hardware & Infrastructure
To train large models you need massive compute resources, many GPUs, high-end memory, fast interconnect, storage for datasets, etc.
Hardware must support efficient parallel and distributed training, model parallelism, data parallelism, memory optimisation techniques are used for training models with hundreds of billions of parameters.
The memory footprint grows with model size: storing weights, activations, gradients, optimizer states all require substantial memory
Compute / Training Steps
Training cost often correlates with number of parameters × number of tokens (data) × compute per token. Some scaling-law research shows that for optimal performance you need to scale both model size and training data in tandem.
Training the largest models can take weeks to months of wall-clock time—even with state-of-the-art hardware.
Data Requirements
Huge volumes of text-tokens are necessary. As models’ parameter counts rise, so too must the number of training tokens to avoid “under-training”.
The quality and diversity of training data matter: more domain-diverse, high-quality corpora help the model generalise better.
Cost, Energy & Operational Considerations
Training large LLMs is costly — both in terms of direct compute infrastructure, energy consumption, and engineering overhead
It also has an energy footprint: large GPU clusters consume large amounts of power and cooling
Final Thoughts
LLMs are one of the most exciting developments in AI and language processing in recent years. They combine scale, data, and architecture in a way that unlocks powerful new capabilities. But with power comes complexity: the computational requirements for training them are substantial, which means practical adoption often involves fine-tuning rather than ground-up training.
Saurabh Kamal
