How to choose between large and small AI models: A cost-benefit analysis

There’s a clear trend: bigger models generally perform better. Larger AI models, built with more data and parameters, demonstrate superior power, efficiency and accuracy compared to their smaller counterparts. However, this comes with a significant caveat: as models grow larger, they become increasingly resource-intensive. So must every team adopt these larger models? Or can organizations achieve their goals with smaller, more efficient alternatives?

This blog post will guide you through the differences, use cases and cost-benefit analysis of these models, especially in the context of machine learning inference. Additionally, you’ll discover how to utilize Nebius AI Studio to select the ideal model for your specific needs.

Some key terms associated with language modelsSome key terms associated with language models

A language model is a machine learning model trained on vast amounts of text data to understand, interpret and generate human-like text. These models use statistical and probabilistic methods to predict the likelihood of a sequence of words, enabling them to perform various tasks such as text completion, translation, summarization and even coding assistance.

Language models are the backbone of natural language processing, allowing machines to understand, generate and analyze human language. They are trained on vast datasets—think books, articles, websites and use learned patterns to predict or generate text that is both grammatically correct and semantically coherent.

• Context length: The amount of text a model can consider when generating a response. Longer context lengths allow for more nuanced understanding and generation of text.

• Tokens: The basic units of text that a model processes. Tokens can be words, subwords or characters, depending on the model’s architecture. For example, the word “unconditional” might be tokenized as [“un”, “condition”, “al”].

• Parameters: The adjustable values within the model that are fine-tuned during training. The number of parameters often correlates with the model’s capacity to learn and generate complex outputs.

• Fine-tuning: The process of adapting a pre-trained model to a specific task or domain by training it on a smaller, specialized dataset.

Types of language modelsTypes of language models

Statistical modelsStatistical models

These models rely on statistical patterns to predict word likelihood. They use n-grams (sequences of 'n' words) to calculate probabilities, based on the Markov assumption that the chance of a word sequence depends only on the previous word.

Neural modelsNeural models

Neural language models use neural networks to predict word sequences. They can handle large vocabularies and deal with rare or unknown words through distributed representations. Recurrent neural networks (RNNs) and transformer networks are common architectures here.

RNNs are adept at memorizing previous outputs for future predictions, while transformers analyze relationships in sequential data, like words in a sentence and can process entire sequences simultaneously, making them faster to train and use. Most of the modern LLMs use a transformer-based architecture.

Language models based on sizeLanguage models based on size

Large language modelsLarge language models

LLMs are generally trained over massive amounts of textual data such as large collections of books and articles or even the entire internet. They use different types of transformers and neural network-based complex algorithms to learn the patterns and structure of the language, thus enabling them to generate coherent and meaningful responses on a wide range of topics.

These models are also generally called or known as generative AI models because they can generate text in a variety of different ways such as being able to predict the next word in a sentence or even completing a sentence or generating new text from scratch based on a given prompt.

As an example, some of the large language models include Claude, GPT-4 and GPT-3.5.

Small language models (SLMs)Small language models (SLMs)

SLMs have fewer parameters and are designed for more specific tasks. They’re efficient, cost-effective and ideal for applications with limited computational power. These are intentionally small in size. They are a type of language model which are designed to be able to run on local devices. They are lightweight and optimized for lower computational resources and thus they have limited memory which makes them suitable for deployment for applications that don’t require a lot of compute and they work well for low latency and even potentially offline processing as well.

Examples include BERT Mini or DISTILBERT, which excel in specialized tasks like sentiment analysis.

When to choose LLMs vs SLMsWhen to choose LLMs vs SLMs

LLMsLLMs

Advantages

1. They provide versatility across a wide range of tasks

2. LLMs generally have a higher capability to solve more complex and multi-step reasoning-based problems as they have more parameters enabling them to understand and generate more complex and contextually relevant information.

3. They have a much broader context as they have been trained on vast amounts of diverse data, this makes them more suitable for tasks specifically requiring a deep understanding of language nuances.

4. They have the potential to be fine-tuned for emergent capabilities not explicitly trained for.

Disadvantages

1. LLMs have high computational requirements for training and inference.

2. They have a longer inference time, which may impact real-time critical applications.

3. Challenges in interpretability due to model complexity.

SLMsSLMs

Advantages

1. Small language models require lesser compute and memory, thus making them faster to train, fine-tune and deploy. They are highly suitable for applications with limited resources or are constrained by time.

2. Training and finetuning SLMs is generally less expensive in comparison to large language models as they require computation resources owing to having a smaller number of parameters.

3. One can fine-tune small models for specific domain-related tasks resulting in better performance.

4. SLMs are able to filter through a smaller subset of data, making them faster, more affordable and if you have your own data, far more customizable and even more accurate.

Disadvantages

1. Small language models have limited versatility and need to be often specialized for specific tasks.

2. These models may struggle with complex or nuanced tasks outside of their specific domain on which they have been trained.

3. They do have a potential for lower latency compared to large models.

4. They can be less adaptable to new tasks/problems without fine-tuning.

Choosing the right model for your organization’s needsChoosing the right model for your organization’s needs

Let’s discuss how to choose the right kind of language model. Primarily it depends on the following factors:

1. Defining your exact task requirement: Considering the complexity and the specific requirement of the task at hand, a small language model may suffice if the task is not that deep. But in case the task requires a much more deeper understanding and context for more complex tasks where you have multiple stakeholders or multiple steps involved, then a small language model is likely to fail. And that’s where a large language model with its broader understanding and context, because it has been trained over so much data, will be better to be able to handle such kind of complex tasks that require a deeper understanding and might have multiple steps involved in order to actually solve that particular task.

2. Availability of the resources: We need to also assess the amount of compute, memory and the budgets involved. Normally, a small language model may be better if the resources are limited due to its efficiency and lower cost of running it because it has lower parameters. However, if budget and computing are not issues or constraints, then you can run large language models.

3. Domain specificity: In case the task is highly domain-specific, then you could technically fine-tune a small language model for that particular domain, which could give you better results than a large language model. Now of course you can still fine-tune a large language model as well, but that would be more costly. So if you have smaller specific requirements in place, then a small language model might be better.

4. Cost of Adoption: To calculate the cost of a language model’s adoption and usage at an organization, one should take into account primarily two different processes: