"Bigger is always better" — this principle is deeply rooted in the artificial intelligence (AI) world. The race to create larger models like Large language models (LLMs) is in full swing; each month brings reports of even larger models featuring more parameters than their predecessors. Yet, their “one-size-fits-all” architecture often falls short for enterprises in specific applications and niche fields like finance or customer support.

Small language models (SLMs) emerge as an alternative to LLMs, which have shown exceptional performance in specialized domains, such as healthcare. For instance, the Diabetica-7B model, designed for diabetes-related inquiries, achieved an accuracy rate of 87.2%, surpassing GPT -4 and Claude-3.5. Similarly, Mistral 7B, a popular SLM with 7 billion parameters, has been reported to outperform Meta's LLaMA 2 13B across various benchmarks.

LLMs, like GPT-3, which powers OpenAI’s ChatGPT, are generative AI models trained on internet-scale data, excelling at general-purpose text generation and natural language understanding. In contrast, SLMs—compact models fine-tuned for specific workflows—provide targeted solutions.

Choosing between LLMs and SLMs depends on critical factors for enterprise AI teams, such as computational efficiency, deployment speed, cost-effectiveness, and accuracy. This article examines how SLMs transform enterprise AI with scalable, high-performance solutions. We will also compare their strengths through real-world use cases and share best practices for fine-tuning and deployment.

Ready to deploy a top-performing SLM without the hassle? Partner with Invisible to get your model deployment-ready efficiently, maximizing ROI with expert precision. Request a Demo.

What is a large language model?

A large language model (LLM) is an advanced foundation model that processes, understands, and generates human-like text or human language at scale. They are trained on huge datasets—often encompassing trillions of tokens from internet texts, books, and other sources—to develop a generalized understanding of language patterns, context, and semantics. 

LLMs, which are available as both open source models and for purchase via subscriptions, work by using deep learning architectures like transformers to perform various tasks, including:

• Text generation
• Content creation and content generation
• Code generation
• Summarization
• Language translation
• Question answering

These large models need high computational power for training and inference and operate on specialized hardware such as GPUs or TPUs. They are also quite resource-intensive to build and fine tune.

Key characteristics of LLMs

• Massive scale: LLMs like GPT-4, Llama 2, and Claude contain billions or trillions of parameters that enable them to understand complex linguistic relationships.
• General-purpose capabilities: They perform well on cross-domain tasks, from drafting emails to coding and creative writing, without requiring task-specific training.
• Contextual understanding: LLMs analyze the input context to generate coherent and relevant responses, although their knowledge is confined to the training data cutoff date.

What is a small language model?

Small language models (SLMs) are smaller models with significantly fewer parameters than LLMs, typically millions to a few billion rather than tens or hundreds of billions. These models run efficiently on consumer hardware, including laptops, smartphones, and edge devices.

While SLMs may not match the full capabilities of larger models, they provide advantages in speed, cost, and privacy by enabling local deployment. SLMs are developed using techniques such as distillation or quantization of larger models or through more efficient architectures and training methods.

• Distillation transfers knowledge from a large "teacher" model to a smaller "student" model by training the student to mimic the teacher's output. This preserves capabilities while reducing size. 
• Quantization reduces the model size by representing weights with fewer bits (for example, converting 32-bit floating-point numbers to 8-bit integers). This leads to significant memory and computational savings while maintaining reasonable performance.

Examples of small language models include Phi-3 Mini (Microsoft), Mixtral (Mistral AI), Llama 3 (Meta), and Gemma (Google).

Ready to deploy a top-performing SLM without the hassle? Partner with Invisible to get your model deployment-ready efficiently, maximizing ROI with expert precision. Request a Demo.

Key characteristics of SLMs

• Lightweight architecture: SLMs like Mistral 7B and Microsoft’s Phi series are built for agility, requiring minimal computational resources compared to LLMs.
• Domain-specific optimization: They excel in niche tasks when fine-tuned on targeted datasets.
• Faster inference: Reduced model size enables sub-second response times, which are critical for edge computing applications like financial trading algorithms or emergency response systems.

Enterprise use cases for LLMs and SLMs

Enterprises are increasingly adopting language models to streamline operations. To understand the practical implications of choosing between LLMs and SLMs, let's examine two key use cases where language models are being deployed more frequently: Customer support automation and Financial document processing. 

We will examine how both LLMs and SLMs can be used in each scenario, as well as where the strengths of each model type truly stand out.

Use case: Customer support automation

Customer support is a key area where AI-powered language models are enhancing operations.  Businesses aim to deliver instant, efficient, and personalized support experiences, and both LLMs and SLMs provide unique ways to achieve this.

LLM approach: General chatbot for diverse customer queries

For broad-spectrum customer support, LLMs are used as versatile conversational AI chatbots capable of addressing various inquiries as a virtual assistant. These chatbots, powered by LLMs and trained on massive datasets, can elevate customer experiences by doing the following:

• Address diverse question types: LLMs can use their broad knowledge base to understand and respond to customer questions, from basic FAQs to complex troubleshooting steps.
• Handle open-ended interactions: LLMs are adept at managing language tasks such as free-form conversations, adapting to different communication styles and user intents, and making interactions feel more natural and less scripted.
• Provide 24/7 availability: LLM chatbots can offer round-the-clock support, improving customer satisfaction and reducing reliance on human agents for initial query handling.

For example, Amarra, a global distributor of special-occasion gowns, integrated AI into its operations to write product descriptions and enhance customer service. The company handled 70% of customer inquiries using AI-powered chatbots, reducing response times and improving satisfaction.

However, the generality of LLMs in this context also has potential drawbacks. While capable, they may sometimes lack deep domain expertise for specific issues and can be costly to operate for many interactions.

SLM approach: Task-specific bots for FAQs, troubleshooting, or regional responses

SLMs provide a more focused and effective approach to automating customer support. Enterprises can create a modular and highly effective customer service ecosystem by deploying a stack of specialized SLMs as AI tools. Examples include:

• FAQ bots: SLMs fine-tuned to a company’s specific FAQ knowledge base can provide highly accurate and quick answers to common questions, often outperforming general LLMs in precision and speed for these queries.
• Troubleshooting bots: Specialized SLMs can be trained on product manuals, troubleshooting guides, and historical support tickets to expertly guide customers through common technical issues that offer step-by-step solutions tailored to specific products or services.
• Regional response bots: SLMs can be tailored to various dialects, cultural nuances, or regional product variations for global enterprises, providing more localized and culturally relevant support experiences than a single, general LLM.

SLMs excel in customer support due to their efficiency, accuracy, and cost-effectiveness for specific tasks. Businesses can achieve superior customer service outcomes by strategically deploying a network of task-specific SLMs while optimizing resource utilization.

Use case: Financial document processing

The financial sector is filled with documents—reports, invoices, contracts—all containing essential data that must be processed and analyzed efficiently. Language models offer powerful tools for automating these tasks, but the choice between LLMs and SLMs again presents different advantages.

LLM approach: Summarizing lengthy reports across industries

LLMs can be utilized for their extensive summarization abilities across various financial documents. Their strengths include:

• General report summarization: LLMs can easily summarize lengthy financial reports from various sectors (e.g., market analysis, risk assessments, economic forecasts) into concise overviews, highlighting key findings.
• Cross-industry applicability: Their general training allows them to handle reports from different financial domains without requiring extensive customization, offering a versatile summarization solution.
• Understanding context and nuance: LLMs can often capture the broader context and subtle nuances within complex financial texts, generating summaries that retain key insights and implications.

For example, JPMorgan Chase has adopted AI to improve employee productivity and customer service. The bank's new LLM Suite helps process and summarize large legal documentation, which enhances efficiency in managing complex financial matter reports. 

However, for highly specific data extraction or tasks that demand meticulous accuracy within a narrow domain, the generalist nature of LLMs may not always be the best fit.

SLM approach: Fine-tuned for extracting specific data from invoices or contracts

SLMs excel when there is a need for precise and efficient extraction of specific information from financial documents. Examples include:

• Invoice data extraction: An SLM fine-tuned to invoice layouts and financial terminology can be incredibly effective at automatically extracting key data points from invoices, such as invoice numbers, dates, amounts, line items, and vendor details, with high accuracy and speed, far exceeding manual data entry efficiency.
• Contract clause extraction: SLMs can be specialized to pinpoint and extract specific clauses from legal contracts, such as payment terms, liability clauses, or renewal conditions. This targeted extraction can significantly accelerate contract review, analysis, and decision-making processes, reducing errors and saving legal professionals valuable time.
• Regulatory compliance checks: SLMs can be trained to identify and flag financial document sections relevant to specific regulatory frameworks, streamlining compliance checks and reducing the risk of regulatory oversight.

SLMs have an advantage in financial document processing due to their precision, efficiency, and capacity for deep customization to meet specific data extraction needs. They offer high accuracy and speed for automating critical financial workflows due to their focus on specific tasks.

Ready to deploy a top-performing SLM without the hassle? Partner with Invisible to get your model deployment-ready efficiently, maximizing ROI with expert precision. Request a Demo.

Best practices for fine-tuning and deploying SLMs and LLMs

Effectively utilizing large and small language models in enterprise applications requires a clear understanding of their fine-tuning and deployment best practices. Although the fundamental principles of machine learning apply to both, the scale and intended use cases of LLMs and SLMs require different strategies.

LLMs fine-tuning best practices

LLMs, which are pre-trained on large and varied datasets, need careful fine-tuning to align their broad capabilities with enterprise requirements. Here’s how to optimize the process:

• Dataset selection: Prioritize high-quality, task-specific datasets over quantity volume. For example, an LLM fine-tuned for medical diagnostics should use a curated dataset of clinical notes rather than generic text. Quality ensures the model aligns with the target domain without unnecessary training effort.
• Transfer learning strategies: Use transfer learning to fine-tune LLMs efficiently. Techniques like prompt tuning (adjusting input prompts without altering weights) or adapter layers (adding lightweight trainable layers) minimize resource demands while preserving the LLM’s pre-trained knowledge.
• Hyperparameter tuning: Carefully adjust hyperparameters—begin with a low learning rate (e.g., 1e-5) to prevent overwriting pre-trained knowledge. Implement early stopping to pause training when performance levels off, particularly with smaller datasets, to avoid overfitting.
• Compute considerations: Fine-tuning LLMs demands significant resources. Distributed training across GPUs or TPUs can be used to accelerate the process. Cloud platforms like AWS and Google Cloud provide scalability but monitor costs closely to stay within budget.

Deploying considerations for LLMs

LLMs’ size and complexity pose deployment challenges; however, strategic planning ensures success:

• Infrastructure requirements: Deploy LLMs on high-performance GPUs or TPUs to handle their computational load. For real-time use, consider model parallelism or distributed inference to split processing across devices, ensuring acceptable latency.
• Optimization techniques: Reduce resource demands with model pruning (eliminating redundant parameters) or quantization. These shrink the model’s footprint while maintaining accuracy, which is critical for cost-effective scaling.
• Cost management: LLMs can be expensive to run. Implement auto-scaling cloud infrastructure to match resources to demand, and use batch processing for non-urgent tasks to optimize hardware utilization.

SLMs fine-tuning best practices

SLMs, with their smaller size and focused design, enable faster and more resource-efficient fine-tuning. Optimize them as follows:

• Lightweight retraining: Fine-tune SLMs on domain-specific datasets to boost performance in niche areas. For instance, an SLM trained in customer support logs can excel at FAQ responses, often surpassing an LLM due to its specialization.
• Utilizing pre-trained models: Start with pre-trained SLMs such as DistilBERT or Mistral 7B, which retain knowledge from larger models. Fine-tune these on your dataset for high accuracy with minimal compute overhead.
• Efficient training techniques: Apply knowledge distillation (transferring insights from a larger model) or quantization (reducing weight precision) to enhance efficiency. These methods are ideal for edge deployments or constrained environments.
• Iterative fine-tuning: Use an iterative approach, start with a small dataset, evaluate outputs, and refine with additional data as needed. This reduces upfront resource demands and speeds up deployment.

Deploying considerations for SLMs

The smaller size of SLMs makes them agile and cost-effective for deployment.

• Low-latency deployment: SLMs excel in real-time applications because of their speed. They run efficiently on standard hardware or edge devices, minimizing infrastructure costs.
• Modular deployment: Deploy multiple SLMs, each fine-tuned for a specific task (e.g., sentiment analysis, entity recognition), in a modular system. This “stacking SLMs” approach gives flexibility and scalability over a single LLM.
• Resource efficiency: SLMs use less memory and power, enabling cost-effective scaling. Multiple SLM instances can run on hardware that would support only one LLM, enhancing parallel processing capabilities.

How small language models outperform LLMs

While LLMs draw attention due to their extensive capabilities, enterprises are realizing that optimal AI performance isn't solely about size. In numerous scenarios, SLMs are not simply economical alternatives; they are proving to be the superior choice, delivering improved efficiency tailored to specific tasks.

Here's how SLMs often outperform their larger counterparts.

Efficiency: Faster, leaner, and more scalable

SLMs outperform LLMs by requiring fewer computational resources, resulting in faster processing and reduced operational overhead. This efficiency makes them well-suited for real-time applications and environments with limited resources.

SLMs provide faster responses due to their compact size, which is essential for applications like customer support. They can operate on standard hardware or edge devices, avoiding the need for costly GPU clusters. A manufacturing firm, for instance, could deploy SLMs across factory floors to monitor equipment in real time, reducing energy costs compared to running an LLM in the cloud. 

Task-specific optimization: When smaller is better

SLMs gain an edge through their ability to be fine-tuned for specific domains or tasks, often achieving higher accuracy than LLMs in targeted applications. In contrast to LLMs, which are designed for broad generality, SLMs excel when adapted to specific, high-value workflows.

Use case: Personalized e-commerce recommendations

SLMs fine-tuned on user behavior data—such as browsing history, purchase patterns, and demographic details—can deliver highly targeted product suggestions. This focused approach often outperforms LLMs. For example, an SLM trained on a retailer’s customer data can suggest niche products that align precisely with individual preferences and increase conversion rates compared to the broader, less personalized outputs of an LLM.

Use case: Financial analysis

In regulated industries like finance, SLMs tailored to specific frameworks (e.g., GDPR, HIPAA) ensure higher accuracy and compliance than generic LLMs. SLMs produce precise, reliable outputs that meet strict standards by training on domain-specific datasets. For instance, A leading investment firm, Boosted.ai, in collaboration with Invisible, developed an AI investment assistant powered by an SLM. They achieved a 90% cost reduction and improved data processing speeds by 10 times by fine-tuning the SLM with precise financial expertise.

Stacking small language models for modular AI solutions

The primary strategic advantage of SLMs is their capacity to be "stacked" and integrated into modular AI solutions. This approach provides greater flexibility and scalability than depending on a single, monolithic LLM. 

Let's consider the following practical examples of modular AI in action to show the effectiveness of modular SLM architectures:

Separate SLMs for decomposed natural language processing

Instead of depending on a single, general-purpose LLM to handle all aspects of natural language processing (NLP), a modular approach strategically decomposes complex NLP workflows into distinct, specialized tasks, assigning each to a dedicated SLM. Consider an AI system designed to analyze customer feedback. This system could employ:

• A sentiment analysis SLM: Expertly fine-tuned to accurately gauge the emotional tone of customer reviews and comments (positive, negative, neutral). This SLM focuses on sentiment detection, achieving high precision in this specific task.
• An entity recognition SLM: Specialized in identifying and categorizing key entities within the feedback text, such as product names, company departments, customer names, locations, and dates. This SLM excels at extracting structured information from unstructured text.
• A summarization SLM: Optimized for condensing lengthy customer feedback into concise summaries, highlighting the key issues and recurring themes. This SLM efficiently distills large volumes of text into actionable insights.

These three separate SLMs work in concert. The sentiment analysis and entity recognition SLMs first process the customer feedback text to extract structured data and emotional tone.  This structured output and the original text are then fed into the summarization SLM to generate a concise summary that incorporates both the sentiment and key entities. 

This modular NLP pipeline, built from stacked SLMs, provides an accurate and maintainable solution compared to forcing a single LLM to handle all three complex tasks simultaneously.

Interconnected SLM workflows for adaptive AI solutions

SLMs can be interconnected in dynamic workflows to create adaptive and intelligent AI solutions. Consider an AI-powered customer support system:

• Initial intent classification SLM: The system first uses a lightweight SLM to classify the customer's initial query intent, such as FAQ, Troubleshooting, Account Inquiry, or Complaint. This initial classification of SLM ensures quick routing and efficient resource allocation.‍
• Dynamic routing to specialized SLMs: Based on the intent classified by the initial SLM, the system dynamically routes the query to the most appropriate specialized SLM workflow. For FAQ intents, the query is routed to an FAQ-focused SLM for accurate answers from a curated knowledge base. For Complaint intents, the system engages a sentiment-aware SLM combined with an entity recognition SLM to analyze the complaint's emotional tone and extract key details. This information is then used to escalate the case to a human agent, providing them with additional context.

Ready to deploy a top-performing SLM without the hassle? Partner with Invisible to get your model deployment-ready efficiently, maximizing ROI with expert precision. Request a Demo.

When to choose an SLM over an LLM

Choosing between a small language model and a large one isn’t about figuring out which is better; it’s about selecting the right tool for your organization’s specific challenges. With their large parameter counts, LLMs handle complex reasoning tasks, but SLMs offer distinct advantages in efficiency, specialization, and cost-effectiveness. The key is to align the model’s strengths with your operational requirements, resource limitations, and strategic goals.

Cost-benefit analysis: Training vs. deploying

To calculate the cost of adopting and using a language model within an organization, one should primarily consider two different processes:

Training costs

The cost depends on the size of the dataset you are actually using for training. In simple terms, the larger the dataset, the higher the cost will be. Training LLMs from scratch or even fine-tuning them extensively requires substantial investment in computing infrastructure.

SLMs, on the other hand, are significantly less demanding to train and fine-tune, requiring smaller datasets and less computing power. If budget constraints are a major concern, or if rapid prototyping and iteration are prioritized, SLMs provide a much more accessible option.

Deployment and operational costs

Beyond training, the ongoing operational costs of deploying and running language models at scale are critical. LLMs, with their high computational demands for inference, translate to higher infrastructure costs for every query processed. For example, if you consider GPT-4 as a large language model, it costs $0.03 for every 1,000 input tokens and $0.06 for each 1,000 output tokens, leading to a total of $0.09 per request.

In contrast, with their efficient inference, SLMs have lower operational costs, which makes them a much more economical option for numerous high-volume, real-time applications. For example, to calculate the cost for a small language model such as Mistral 7B, it costs $0.0001 per 1,000 input tokens and $0.0003 per 1,000 output tokens, resulting in $0.0004 per request.

Hybrid AI solutions: Combining LLMs and SLMs

Integrating SLMs with LLMs in hybrid AI solutions helps organizations optimize performance, reduce costs, and increase efficiency. In this approach, tasks are assigned to the model best suited for the job. This division reduces the computational burden on LLMs and enables SLMs to function efficiently on simpler tasks, resulting in a more balanced and resource-effective system.

For instance, AI platforms that seamlessly integrate both models enable enterprises to distribute tasks intelligently based on complexity. Businesses can save computational resources while ensuring high-quality outputs for more complex tasks assigned to LLMs. This hybrid approach boosts efficiency and balances performance with cost, enabling organizations to scale AI solutions effectively.

LLMs are powerful, but SLMs offer targeted, efficient, and cost-effective solutions

In essence, choosing between an SLM and an LLM depends on your enterprise’s goals. SLMs excel when efficiency, specialization, and scalability are critical, providing lean, targeted solutions. LLMs excel in versatile applications and complex reasoning, acting as powerful generalists. By evaluating task scope, resource constraints, and performance needs, enterprises can deploy the right model—or combination of models—to drive success and optimize return on investment.

Looking ahead, the future of enterprise AI is increasingly leaning toward modular, task-specific small language models. This shift enables organizations to implement AI solutions that are not only efficient but also tailored to their operational needs. Modular AI architectures allow enterprises to combine multiple SLMs, each addressing specific functions, into a unified system.

As AI advances, adopting modular SLMs enables enterprises to harness AI's potential, driving innovation and maintaining a competitive edge in their respective industries.

Ready to deploy a top-performing SLM without the hassle? Partner with Invisible to get your model deployment-ready efficiently, maximizing ROI with expert precision. Request a Demo.