The rapid expansion of Artificial Intelligence (AI) technology was accompanied by the development of powerful large language models (LLMs). They have many applications across different industries and are designed to process large volumes of data. LLMs can grasp subtle nuances of human language and provide context-relevant replies. However, their implementation may be costly and time-consuming, making them hardly a good choice for enterprises on a tight budget. This is why many ventures utilize smaller alternatives (SLMs). They are easier to deploy, configure, and maintain. In this guide, we will compare LLM vs SLM to see whether they deliver a comparable performance. We will analyze their intended purpose and consider the unique strengths that increase their applicability in various spheres.
What is LLM?
Such AI frameworks were built to interpret client queries and respond using a human-sounding tone. They utilize deep learning technology to analyze and produce text using natural semantic patterns. LLMs are based on convoluted architecture comprising the encoder and decoder parts. They are taught to split data into tokens and discover the links between fragments. Below, we have outlined the fundamental principles of such models:
- Machine learning: Developers build algorithm-powered models with a multitude of parameters that stand for the most realistic predictions. During initial training, LLMs master the ropes and discover how to replicate natural language, which allows them to produce human-sounding sentences most frequently employed in a specific context.
- Deep learning architecture: Phrases are turned into numerical representations. LLMs compare their significance when generating content.
- Training and adjustment: AI engineers feed high-quality data into neural networks and analyze the performance to eliminate bias and inaccuracies. During this stage, it’s essential to utilize industry-specific knowledge and constantly monitor LLM performance by adjusting the parameters if necessary.
Building LLMs requires strong technical skills and significant investments, so many ventures prefer to outsource this task to trusted providers. MetaDialog has a team of experts who specialize in developing enterprise-level solutions. Its custom LLMs correctly interpret queries in context, provide replies in multiple languages, and allow companies to reduce the average resolution time to 20 seconds. They can be deployed on-premises to ensure top-grade security of user data.
What is SLM?
AI tools of this type were created for those who want to discover less resource-consuming solutions that can understand human language and generate comprehensive replies. What makes them different from LLMs is that they are trained on a more limited amount of data. Besides, they do not use as many parameters as LLMs and are based on a smaller network architecture.
After taking a closer look at small language models vs large language models, it is easy to notice that the former are created with the help of statistical methods. They usually leverage limited neural networks, which accounts for higher efficiency. However, they prove to be worse than LLMs when solving advanced tasks. Serving as a less resource-intensive solution, they are deployed in situations when a company does not have access to significant computational assets. SLMs are perfect for simple language processing, which explains why they are used increasingly often by companies on a limited budget.
What Makes LLMs Different from SLMs?
Regardless of the size of an AI framework, building it from scratch is an arduous task requiring significant investments. This is why many ventures rely on pre-trained solutions. A company has to spend about $12 million USD to build and deploy a powerful LLM comparable with the average offerings on the market. This is the main reason why businesses opt for affordable alternatives. By deploying pre-trained small LLMs, they save valuable assets and invest only in customization. Choosing between AI models of different sizes is an arduous task. We have outlined the key factors a venture should consider.
Resource consumption
A brief LLM vs SLM comparison shows that the usage of computational power is one of the main differences between differently-sized AI models. Until recently, it was widely believed that LLMs are the only solution to most tasks companies need to solve, as they utilize a large number of parameters. However, as they are extremely expensive, many owners prefer cheaper alternatives.
Due to high memory consumption, LLM deployment is impossible without significant computing capacity and well-developed infrastructure. A company should invest in expensive hardware, which makes it impossible for many small and medium-sized businesses (SMBs) to use such solutions.
The key selling point of SLMs is that they utilize a limited number of parameters and deploy efficient optimization practices. Companies embrace such approaches:
- Run lightweight versions of existing LLMs;
- Use quantization to minimize a model’s size and launch SLMs on multiple devices without making substantial investments into hardware.
These methods facilitate optimizing infrastructure usage and leveraging the power of AI.
Implementation and maintenance expenses
Companies should consider the costs associated with training the AI model and deploying it. It allows them to calculate the cost of adoption and usage with high accuracy. By configuring LLM’s or SLM’s parameters, a company can ensure that a model will produce high-accuracy outputs and perform specific functions.
The larger the dataset a venture uses, the more expansive it will be to adjust settings. LLMs typically require limited fine-tuning, as they have advanced learning capabilities. SLMs need to be adjusted and configured to function properly. While LLMs produce satisfying outputs in 86% of cases, an average SLM provides acceptable results in 63% of cases. However, SLMs are more suitable for automating simple daily tasks as they offer great value.
Time spent on adjustments
A fine tuned language model demonstrates higher accuracy and allows companies to significantly optimize processes without adding a human to the loop. Nevertheless, the training process requires a high level of expertise, knowledge of specific terminology, and the usage of mistake-free documentation and data.
Due to a high number of parameters, LLMs are difficult to calibrate. It could take several months to adjust such models. SLMs are easier to configure, as a company can spend a few weeks enhancing its performance.
National context
Most modern LLMs were developed in the US and China, making their outputs hardly relevant for many cultures. The development of context-specific models is expected to address this issue. LLM developers are aware of the problem and release smaller versions targeting specific groups, but their adoption remains limited.
Supported capabilities
LLMs utilize insights extracted from massive volumes of data, which allows them to produce output content of different types. They can be used to power advanced virtual assistants capable of maintaining an engaging conversation with clients. Firms utilize LLMs to solve industry-specific tasks.
Many healthcare providers and law companies have already deployed AI-driven chatbots. Nevertheless, as SMBs are unlikely to need a chatbot that can write a poem, such ventures use more practical solutions designed for specific purposes. While SLMs do not have an extensive range of capabilities, they are perfectly suitable when a firm needs to solve a narrow scope of issues.
Performance speed
Even though LLMs have more advanced capabilities, reaching a conclusion and generating an output may take longer. A model with a trillion parameters will be significantly slower than an SLM with a billion parameters. LLM developers recognize this shortcoming and try to solve it by deploying innovative ML techniques like Mixture of Experts networks.
Output accuracy
When assessing an AI model’s performance, developers analyze the relevance of generated responses and check them for possible mistakes. LLMs with large context windows summarize huge amounts of information. Besides, they make it easier to improve initial inputs to produce more relevant answers.
LLMs outperform SLMs when it comes to formulating high-quality replies. Custom LLMs created by MetaDialog produce reliable results while keeping customer data protected.
Top-grade security
Firms with a small budget find it challenging to implement AI without compromising sensitive data. Regardless of the size of an AI model, ensuring its safety remains the main priority. Many ventures are reluctant to adopt AI technology due to security concerns. However, using internal datasets and knowledge bases and implementing industry best practices allow companies to improve protection measures. Businesses rely on the following approaches:
- Advanced data encryption;
- API usage to stop third parties from intercepting information;
- The configuration of access rights to prevent unregistered users from deploying a model;
- The development of a transparent policy outlining the utilization of an LLM.
Implementing these methods requires finding a trustworthy provider that specializes in LLM development. MetaDialog builds fully compliant models that enable companies to maintain full control over their data.
How to Use LLMs and SLMs?
Advanced models with many parameters generate sophisticated outputs, enabling ventures to handle increased volumes of queries during peak times. They have a variety of applications, which explains their widespread popularity across modern industries. The main LLM use cases include the following:
- Chatbots: Unlike basic pre-programming solutions, LLM-based virtual assistants handle challenging queries and can improve every step of a client’s journey. They are used in finance, healthcare, and retail, producing human-sounding replies with accurate information.
- Content generation: As LLMs create texts in different styles, they are equally well suited for generating marketing materials, code, scripts, and tutorials.
- ML-powered translation: LLMs translate content in many languages while maintaining a consistent brand style, which makes them perfect for multinational corporations.
Even though SLMs have more limited functionality, they could be fine-tuned to perform specific tasks with high efficiency. Ventures deploy such solutions to do the following:
- Generate replies to emails: SLMs analyze the context and quickly write short replies to reduce response time and answer client’s questions.
- Filter spam: SLMs block suspicious messages after analyzing their content and recipient’s data.
- Make marketing campaigns more targeted: Companies deploy SLMs to analyze information about consumer behaviors to generate recommendations tailored to the buyers’ needs. It makes their social media content more relevant to the interests of specific segments.
By examining these aspects, users can better understand the strengths and weaknesses of different options, helping them choose the right tool for their specific needs and applications.
LLMs are increasingly often used when a venture wants to reduce a workload during peak hours and ensure its chatbots can handle complex issues. These models have impressive summarization capabilities and are used by programmers who want to expedite their workflows using code snippets or auto-completion techniques.
In contrast, SLMs are trained for sentiment analysis to detect the tone of voice in customer feedback. Firms deploy them to organize documents and information about financial transactions. Besides, such models are invaluable when a safety and security team needs to detect fraudulent activities.
SLM vs LLM: Which One to Choose?
When looking for an AI model capable of producing text in various formats, firms often decide to deploy custom LLM solutions. However, SLMs are a cost-saving alternative to such products. Smaller models are best suited for narrow tasks, making them the top choice among those who work with domain-specific information. When choosing between these two options, a venture should consider its budget and strategic goals.
Leveraging natural language processing technology, AI models handle the most unpredictable situations. After fine-tuning their performances, companies can be sure that they will be able to accomplish tasks of different complexity.
LLMs and SLMs can be taught to utilize external resources to detect intent, provide comprehensive summaries, analyze satisfaction levels, generate contextual replies, and maintain engaging conversations. It’s recommended to start using a larger model and then decide whether advanced AI-driven tools are necessary to streamline certain processes.
Weighting potential application areas and accessible assets is the only way to pick a result-yielding AI solution for a specific business environment. If you need to increase the efficiency of your call center or customer support department, an LLM is a better solution.
Custom AI models built by MetaDialog’s professionals analyze extensive datasets and generate helpful replies to clients’ questions. However, when a company needs to save money and solve specific tasks without deploying many resources, an SLM offers a better value. Law firms, healthcare organizations, and companies from the finance industry utilize highly specialized datasets. They can benefit from using an SLM trained on their data, as it allows them to remain compliant.
As this LLM vs SLM comparison demonstrates, the final choice depends on a firm’s preferences. Ventures do not need to spend a lot of time to deploy SLMs. Besides, such AI models are highly efficient and do not require a lot of memory. However, LLMs have better accuracy and demonstrate an improved understanding of different contexts. MetaDialog has a team of seasoned experts who specialize in building AI models. Get in touch with us today and discover how to make LLMs and SLMs an integral part of your business processes.