What is “Large” in Large Language Model?

The term “Large Language Model” has become ubiquitous in discussions about artificial intelligence, yet the meaning of “large” remains surprisingly unclear to many. Is it about physical size? Computational power? The amount of text processed? Understanding what makes these models “large” matters not just for technical comprehension but for grasping their capabilities, limitations, costs, and societal implications. The “large” in LLMs refers primarily to two interrelated dimensions: the number of parameters within the neural network architecture and the scale of training data used to develop these models. However, this deceptively simple answer obscures fascinating nuances about what scale enables, why bigger models behave differently, and where the boundaries of “large” actually lie. This article explores the multiple dimensions of scale in language models, examining parameter counts, training data volume, computational requirements, emergent capabilities that arise from scale, and the practical implications of model size for organizations deploying these technologies.

Parameter Count: The Primary Definition of “Large”

When researchers and practitioners call a language model “large,” they’re primarily referring to the number of parameters—the internal numerical values the model learns during training that encode its knowledge and capabilities.

Understanding Parameters in Neural Networks

Parameters are the learned weights within a neural network that transform inputs into outputs. Each connection between neurons in the network has an associated weight that determines how strongly signals pass through that connection. During training, these weights adjust to minimize prediction errors, gradually learning patterns that enable the model to perform its task.

In language models, parameters encode everything the model “knows”—grammatical rules, factual information, reasoning patterns, stylistic conventions, and countless other aspects of language. When GPT-3 generates coherent text about quantum physics, medieval history, or Python programming, it’s drawing on patterns encoded across billions of parameters learned from training data.

The sheer number of parameters distinguishes LLMs from earlier language models. Traditional models might contain millions of parameters. Modern LLMs contain billions or even trillions:

• GPT-2 (2019): 1.5 billion parameters
• GPT-3 (2020): 175 billion parameters
• PaLM (2022): 540 billion parameters
• GPT-4 (2023): Estimated 1+ trillion parameters (exact number undisclosed)
• Llama 2 (2023): 7B to 70 billion parameters depending on variant
• Mixtral 8x7B: 47 billion total parameters (though only ~13B active per token)

This exponential growth in parameters correlates strongly with model capabilities—larger models generally perform better across diverse tasks, though with diminishing returns.

Why Parameter Count Matters

More parameters enable richer representations of language patterns. A model with billions of parameters can learn subtle distinctions between similar words in different contexts, understand complex grammatical structures, and store vast amounts of factual knowledge. Smaller models must make crude generalizations; larger models can capture nuance.

Parameter scaling follows observable patterns documented in scaling laws—mathematical relationships between model size, training data, and performance. Research by OpenAI, DeepMind, and others demonstrates that model performance improves predictably as parameters increase, following power law curves. Doubling parameters doesn’t double performance but provides consistent improvements.

Emergent capabilities appear at certain parameter thresholds. Models below ~10 billion parameters struggle with complex reasoning, few-shot learning, or following intricate instructions. Models crossing ~100 billion parameters begin exhibiting capabilities not explicitly trained for—chain-of-thought reasoning, in-context learning from examples, and complex instruction following. These emergent behaviors make scale qualitatively transformative, not just quantitative.

Memory footprint scales with parameters. Each parameter typically requires 2-4 bytes of memory depending on precision (16-bit float vs 32-bit float). A 175 billion parameter model requires approximately 350-700 GB of memory just to store the weights—before considering activation memory during inference. This memory requirement shapes deployment costs and accessibility.

Training Data Volume: The Other Dimension of “Large”

Parameter count tells only part of the story—training data scale equally defines what makes language models “large.”

Massive Text Corpora

Training data for LLMs spans hundreds of billions to trillions of tokens (where a token is roughly a word or word fragment). To contextualize this scale:

• GPT-3 trained on approximately 300 billion tokens
• Gopher (DeepMind) trained on 300 billion tokens from MassiveText dataset
• PaLM trained on 780 billion tokens
• LLaMA models trained on 1-1.5 trillion tokens

A trillion tokens represents roughly 750 billion words—equivalent to millions of books, billions of web pages, vast code repositories, and countless other text sources. Reading this much text would take a human tens of thousands of years at normal reading speeds.

Data Diversity and Composition

LLM training data comes from diverse sources:

Web crawls form the largest component, including Common Crawl—a nonprofit organization that continuously archives the web. This provides broad coverage of topics, writing styles, and human knowledge but includes noise, misinformation, and biases reflecting internet content.

Books from various sources add long-form content with coherent narratives, formal writing styles, and diverse fiction and non-fiction topics. Books2 and other book corpora contribute substantial high-quality text.

Academic papers and scientific literature provide technical knowledge, formal reasoning, and specialized terminology across disciplines. Sources like arXiv, PubMed, and academic archives contribute research content.

Code repositories from platforms like GitHub enable LLMs to understand and generate programming code across multiple languages. This code data explains why models like GPT-4 excel at programming tasks despite being fundamentally language models.

Conversational data from platforms like Reddit adds informal language, dialogue patterns, and diverse perspectives through millions of discussions.

Multilingual content expands models beyond English, though representation varies dramatically by language. High-resource languages like English, Spanish, and Chinese receive substantial training data while low-resource languages get minimal exposure.

Why Training Data Scale Matters

More data enables better generalization. Small datasets lead to memorization and overfitting—models learn training examples rather than underlying patterns. Massive datasets force models to learn genuine patterns that generalize to new contexts.

Diverse data creates versatility. Training on only scientific papers produces models understanding formal technical writing but struggling with casual conversation or creative fiction. Broad data exposure enables LLMs to adapt to varied contexts and tasks.

Data quality impacts model behavior. Training data biases, factual errors, or toxic content propagate into models. The “large” in LLM training data brings not just volume but also the challenge of managing quality at unprecedented scale.

Scaling laws apply to data too. Just as model performance improves predictably with parameters, it also improves with training data volume. However, data and parameters must scale together—adding parameters without sufficient training data causes underfitting, while massive data with insufficient parameters creates a learning bottleneck.

Computational Requirements: The Resource Dimension of “Large”

Behind parameter counts and data volumes lie staggering computational requirements that make training and running LLMs expensive and resource-intensive.

Training Compute Scale

Training modern LLMs requires thousands of GPUs running for weeks or months. Specific examples illustrate the scale:

• GPT-3 training consumed approximately 3,640 petaflop-days (a petaflop-day represents one quadrillion floating-point operations per second sustained for 24 hours)
• Meta’s LLaMA 65B required about 1,022,000 GPU hours on A100 GPUs
• Training GPT-4 reportedly cost over $100 million in compute resources alone

Training infrastructure for frontier LLMs involves specialized clusters:

• Thousands of high-end GPUs (A100, H100, or similar)
• High-bandwidth interconnects enabling GPUs to communicate quickly
• Massive storage systems for training data and checkpoints
• Specialized cooling and power infrastructure
• Redundancy and fault tolerance given month-long training runs

Energy consumption for training large models is substantial. Training a single large language model can consume as much electricity as several hundred homes use in a year, raising environmental concerns about AI development’s carbon footprint.

Inference Compute Requirements

Running trained LLMs also demands significant resources. While inference requires less compute than training, serving millions or billions of queries creates its own challenges:

Memory requirements for inference depend on model size and precision. Serving GPT-3 at full precision requires approximately 350GB of GPU memory, necessitating multiple high-end GPUs working together.

Latency and throughput tradeoffs shape deployment. Faster responses demand more parallel GPU resources, driving costs higher. Batching multiple queries improves throughput but increases latency for individual requests.

Cost per query for API-based LLMs reflects these computational demands. OpenAI’s pricing—cents per thousand tokens—aggregates infrastructure, power, cooling, and operational costs at massive scale.

Optimization techniques like quantization (reducing precision from 32-bit to 8-bit or 4-bit), pruning (removing less important parameters), and distillation (training smaller models to mimic larger ones) reduce inference costs while accepting some performance degradation.

What “Large” Enables: Emergent Capabilities

The most fascinating aspect of scale in LLMs is that beyond certain thresholds, qualitatively new capabilities emerge that smaller models simply don’t exhibit.

Few-Shot and Zero-Shot Learning

Small models require fine-tuning for new tasks—taking a pretrained model and training it further on task-specific examples. This process demands hundreds or thousands of labeled examples and compute resources for training.

Large models learn from prompts without additional training. Provide a few examples in the prompt (few-shot learning) or just describe the task (zero-shot learning), and sufficiently large models adapt their behavior accordingly. This flexibility emerges around 10-100 billion parameters and strengthens with further scaling.

Example: Ask a small model to translate English to French, and it might fail without translation-specific training. Ask GPT-4 the same question, and it translates accurately despite not being explicitly trained for translation—it learned the capability from patterns in its massive training data.

Chain-of-Thought Reasoning

Complex reasoning breaks down multi-step problems into intermediate steps. Large models can engage in this reasoning when prompted appropriately:

Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. 
Each can has 3 tennis balls. How many tennis balls does he have now?

Chain-of-thought: 
- Roger starts with 5 balls
- He buys 2 cans, each containing 3 balls
- 2 cans × 3 balls = 6 balls
- Total: 5 + 6 = 11 balls

Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. 
Each can has 3 tennis balls. How many tennis balls does he have now?

Chain-of-thought: 
- Roger starts with 5 balls
- He buys 2 cans, each containing 3 balls
- 2 cans × 3 balls = 6 balls
- Total: 5 + 6 = 11 balls

Smaller models struggle with this reasoning, often jumping to incorrect answers. Models crossing ~100 billion parameters begin reliably using chain-of-thought when prompted, dramatically improving performance on mathematical, logical, and commonsense reasoning tasks.

Instruction Following and Task Generalization

Large models understand and follow complex instructions involving multiple steps, constraints, or nuanced requirements. This instruction-following capability enables conversational AI that actually works rather than frustrating users with rigid responses.

Task transfer becomes possible—models trained primarily on language modeling can perform tasks like summarization, question answering, code generation, or creative writing without task-specific training. This versatility distinguishes LLMs from earlier specialized models.

The Relative Nature of “Large”

Despite discussing billions of parameters, the concept of “large” is contextual and evolving rapidly.

Historical Perspective

What counted as “large” has changed dramatically:

• 2018: BERT’s 340 million parameters seemed large
• 2019: GPT-2’s 1.5 billion parameters caused controversy over release
• 2020: GPT-3’s 175 billion parameters established new scale
• 2023: Models approaching or exceeding one trillion parameters are under development

Moore’s Law for AI suggests capabilities double every few months rather than every two years. What seems impossibly large today may be standard in a few years.

Practical Thresholds

Different applications require different scales:

• 7-13B parameter models suffice for many domain-specific applications
• 70-200B parameter models handle most general-purpose tasks well
• 500B+ parameter models push frontiers on the most challenging reasoning tasks

Smaller models with good training can outperform poorly trained larger models. LLaMA 2’s 70B model competes with GPT-3’s 175B model despite fewer parameters, demonstrating that training quality matters as much as raw size.

The Efficient Scaling Movement

Researchers increasingly question whether ever-larger models are optimal or necessary. Approaches like:

Mixture of Experts (MoE) architectures activate only subsets of parameters for each input, achieving large model benefits with smaller active parameter counts. Mixtral 8x7B contains 47 billion total parameters but only uses ~13 billion per token.

Retrieval augmentation adds external knowledge bases to smaller models, achieving capabilities of larger models without encoding everything in parameters.

Task-specific optimization fine-tunes smaller models for specific domains, often matching or exceeding larger general-purpose models in specialized contexts.

Implications of Scale

Understanding what “large” means in LLMs has practical implications for organizations, researchers, and society.

Accessibility and Democratization

Only well-resourced organizations can train frontier LLMs from scratch. The millions of dollars required for compute, expertise, and data infrastructure concentrates cutting-edge development among tech giants, wealthy startups, and well-funded research institutions.

API access democratizes usage without requiring training resources. Organizations can deploy GPT-4 capabilities without owning GPU clusters, paying only for actual usage. This accessibility enables smaller companies and researchers to build applications using state-of-the-art models.

Open-source models like LLaMA, Mistral, and others narrow the gap. While still requiring substantial resources to train, once released, these models enable self-hosting and fine-tuning without dependence on commercial APIs.

Cost-Performance Tradeoffs

Larger models cost more to run but offer better performance. Organizations must balance:

• Using smaller models for cost efficiency on simpler tasks
• Using larger models for complex tasks justifying higher costs
• Optimizing prompts and retrieval to maximize smaller model performance
• Considering alternatives like fine-tuned smaller models for specific domains

Inference costs scale with usage. At low volumes, API-based LLMs are economical. At high volumes, self-hosting becomes cost-effective despite infrastructure investment.

Environmental Considerations

Training carbon footprint for large models raises sustainability concerns. A single training run can emit as much carbon as several cars over their lifetimes. This environmental cost factors into decisions about when training new large models is justified.

Inference energy at scale also accumulates. Serving billions of queries to hundreds of millions of users globally consumes substantial power continuously, dwarfing one-time training costs over time.

Efficiency research aims to reduce environmental impact through better algorithms, more efficient hardware, and architectural innovations that achieve similar capabilities with less compute.

Conclusion

The “large” in Large Language Model refers primarily to billions or trillions of parameters within neural networks and the hundreds of billions to trillions of tokens used for training, but encompasses broader dimensions including massive computational requirements and qualitatively transformative emergent capabilities that arise at certain scale thresholds. This scale isn’t arbitrary—it enables few-shot learning, complex reasoning, and versatile instruction following that smaller models simply cannot match, representing genuine capability transitions rather than mere quantitative improvements. Understanding these multiple dimensions of scale illuminates not just technical architecture but also accessibility barriers, cost structures, environmental implications, and the ongoing debate about whether ever-larger models represent the optimal path forward.

As the field evolves, “large” remains a moving target—today’s frontier models become tomorrow’s baseline as capabilities double with striking regularity. However, the fundamental insight persists: scale matters profoundly in language models, not just for performance but for unlocking entirely new categories of capabilities that make modern LLMs qualitatively different from their smaller predecessors. Whether the future brings continued scaling to even more massive models or a shift toward more efficient architectures achieving similar capabilities with less scale, understanding what “large” means today provides essential context for navigating the rapidly evolving landscape of AI language technologies.