Why Small Language Models Make Business Sense
Small Language Models are changing the way businesses implement AI by providing solutions that operate efficiently using standard hardware.
Despite the attention given to massive AI models, these compact alternatives demonstrate that in the real world, smaller often means smarter, faster, and more cost-effective.
What are SLMs
Small Language Models (SLMs) are much like the Large Language Models (LLMs) that we are all familiar with (e.g. ChatGPT), except that they are smaller in size.
Model Scale
SLMs typically range from a few million to a few billion parameters
LLMs are much larger, with tens of billions to trillions of parameters
Example: Meta's Llama 2 comes in 7B (7 billion parameters) and 70B (70 billion parameters) variants, each serving different needs
Practical Implementation
Can run on standard computing hardware
Suitable for mobile devices and edge computing
Adaptable to specific business needs through fine-tuning
| Characteristic | Small Language Model | Large Language Model |
|---|---|---|
| Number of Parameters | ~1B - 10B parameters | 70B+ parameters |
| Hardware Requirements | Standard servers, edge devices | Specialised hardware |
| Response Time | Milliseconds - seconds | Seconds - minutes |
| Deployment | Local or cloud | Primarily cloud-based |
| Use Cases | Specific tasks, domain expertise | General purpose, broad tasks |
| Fine-tuning Cost | Lower, more practical | Higher, resource-intensive |
SLM vs LLM Comparison
Increased Adoption
From an AI consulting standpoint, one of the key reasons for this is that SLMs are ideally suited to some of the smaller datasets that are more prevalent within typical businesses. They can also be more easily fine-tuned to the exact needs of a specific company and their own data.
Clem Delangue, CEO of Hugging Face, predicts that up to 99% of AI use cases could be addressed using SLMs.
Prominent firms, like Microsoft and Google along with IBM and Meta have introduced SLMs, such as Phi Trio and Gemma as well as compact versions of Llama models. Illustrating broad acceptance, within the industry.
| Use Case | Model Type |
|---|---|
| Complex, multi-domain tasks that require deep understanding and creativity | LLM |
| Handling long, nuanced content or large datasets | LLM |
| Multilingual tasks or advanced NLP | LLM |
| Domain-specific tasks with small/medium datasets | SLM |
| Real-time applications requiring low latency | SLM |
| Applications where sensitive data must remain local to ensure privacy | SLM |
| Resource-constrained environments like mobile devices or IoT | SLM |
Suitable Use Cases for LLMs vs SLMs
Key Advantages of Small Language Models
These compact models deliver impressive results with millions rather than billions of parameters.
Their benefits include:
Efficiency and Cost-Effectiveness: SLMs require significantly less computational power and memory compared to LLMs: this makes them faster to train, as well as more affordable to run long-term.
Domain-Specific Applications: SLMs perform well in tasks that require specialist knowledge, such as customer support chatbots, real-time translation, document summarisation, and IoT device operations. Their smaller size allows for easier fine-tuning on specific datasets, which allows them to excel, with particularly niche use cases.
Enhanced Privacy: Due to their compact nature, SLMs can operate locally (e.g., on mobile devices). This significantly enhances data protection by limiting the need for cloud based processing services.
Reduced Environmental Impact: Lower computing requirements lead to less energy consumption, in both training and inference processes. Which is a crucial factor, for companies aiming to achieve sustainability goals.
Technical Foundations
Three key strategies enable the creation of these models;
Knowledge Distillation: This process involves training a smaller model to mimic a larger one's behaviour. For example, a customer service SLM might learn the most common support scenarios from a larger model while remaining compact enough to run on local servers.
Model Pruning: This technique involves removing less important connections within the model: through careful pruning, models can often maintain most of their performance while significantly reducing their size.
Quantisation: This method optimises how the model stores and processes numerical data. Instead of using high-precision numbers (which require more storage), quantisation uses smaller number formats, which maintain acceptable accuracy. For example, decreasing precision, from 32 bits to 8 bits can notably shrink model size while generally preserving adequate performance levels, for various business uses
Understanding the Limitations
While SLMs offer many advantages, it’s important to understand some of their limitations:
Task Complexity
SLMs are like specialised tools, while LLMs are more like swiss army knives
They excel at specific tasks but may struggle with broader applications
Best suited for focused, well-defined business problems
Input Types
Most SLMs work with a single type of input (usually text)
Unlike larger models, they typically aren't multimodal (can't process images, audio, etc.)
For many business uses, this single-focus approach is actually beneficial
Context Window
SLMs have smaller context windows - the amount of text they can process at once
Example: SLMs like Llama 3.2 handle 128k tokens, while Gemini 1.5 processes 2 million tokens
Solutions like "chunking" help manage longer texts by breaking them into smaller pieces
The Road Ahead
SLMs are finding success in several key business applications:
Targeted Applications: Businesses with specific, limited-scope language tasks, such as customer feedback analysis or domain-specific queries, can benefit from the efficiency of SLMs.
Real-Time Processing: SLMs are well-suited for real-time interactions, such as chatbots and live translation services, as they deliver faster response times due to their smaller size.
Data Privacy Concerns: In industries like healthcare and finance, SLMs can process sensitive data locally, helping companies to comply with regulations such as GDPR or HIPAA.
AI Agents and Orchestration: SLMs excel as specialised AI agents, each handling specific tasks. Businesses can create systems of these agents working together, combining the efficiency of SLMs with the versatility of having multiple specialised components.
Making the Right Choice for Your Business
When deciding if SLMs are suitable for your company's needs, our AI consulting experience suggests considering these key factors:
What is the specific task or problem you're trying to solve?
What are your computational resources and budget constraints?
Do you have specific data privacy requirements?
What is the expected volume and frequency of model usage?
Do you need real-time processing capabilities?
SLMs represent practical AI implementation. They offer an effective balance of capability and efficiency for businesses seeking reliable, cost-effective AI solutions.
If you are looking for expert AI consulting guidance on how SLMs could be used within your business, or if you are interested in any other AI consulting services, please get in contact.