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Why small language models are on the rise

Small language models challenge the 'bigger is better' AI myth -- but can they fully replace their larger counterparts?

Donald Farmer
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Published: 11 Mar 2025

Large language models are everywhere in business, from writing code to creating content to analyzing data. As they become ubiquitous, the conventional wisdom has linked model size with stronger performance: The larger the LLM, the better.

Small language models (SLMs) directly challenge this assumption. Whereas LLMs like OpenAI's GPT-4 and Anthropic's Claude rely on hundreds of billions of parameters, SLMs take a more focused, lightweight approach, typically operating with fewer than 30 billion parameters. Like LLMs, they have use cases in various industries, from healthcare to manufacturing to retail.

SLMs can balance efficiency and high performance, are useful at the edge, and differ significantly from LLMs. But they can't always replace their larger counterparts. Teams must weigh the costs and benefits of each language model type to decide which is best suited for their use case.

How do small language models work?

Unlike LLMs, which are trained to handle a wide variety of general tasks, SLMs focus on precision for specific purposes. This efficiency stems from key technological features and a unique training philosophy:

  • Knowledge distillation involves training a smaller "student" model to mimic a larger, already-trained "teacher" model.
  • Model quantization reduces high-precision numbers in the model to more efficient formats. This can considerably shrink model size while maintaining original performance.
  • Pruning removes redundant connections within a neural network that limit the model's ability to answer general questions. With careful results testing, pruning can significantly reduce model size.
  • Sparse attention mechanisms enable SLMs to focus only on the most important connections between words, significantly reducing the computing power needed to process information. In contrast, LLMs examine how each word relates to all others when analyzing text.

Training an SLM also involves a different data approach compared with training an LLM; namely, SLMs prioritize quality over quantity. They rely on carefully curated, domain-specific data sets that are regularly updated for relevance, rather than using giant, highly diverse text data sets.

For example, an SLM for healthcare document analysis does not need to train on thousands of newspaper articles or novels. Instead, it should train on medical documents that are regularly updated to keep up with emerging trends and practices.

This combination of technological features and focused training enables SLMs to achieve remarkable efficiency while maintaining high performance in their intended scenarios.

Small language models at the edge

SLM deployments can include robots, drones or edge devices, with data being processed directly on or near the device that collects it rather than on a distant cloud server. For example, when a manufacturing system uses sensors and an SLM to detect defects, the analysis happens on the factory floor rather than at a remote data center.

SLMs at the edge offer numerous benefits:

  • Near-instant response times -- milliseconds instead of seconds.
  • Continued operation during limited internet connectivity.
  • Reduced data transmission costs.
  • Enhanced privacy and security, as sensitive data stays local.

Small language model use cases

Organizations can tailor SLMs to specific industry needs while maintaining high performance and security standards.

SLMs' ability to deploy at the edge, maintain data sovereignty and operate in real time makes them particularly valuable in scenarios where traditional cloud-based LLMs would be impractical or noncompliant.

Industry Use case Example implementation Key benefits
Healthcare Clinical documentation analysis Medical clinics' use of on-premises SLMs for real-time medical note analysis without exposing private data
  • Data privacy, such as HIPAA compliance
  • Real-time processing
  • Ability to function offline
Manufacturing Quality control inspection Manufacturers' deployment of SLMs on assembly lines for real-time defect detection with response times under 100 ms
  • Low latency
  • Edge device deployment
  • 24/7 operation
Financial services Fraud detection European banks using local SLMs for transaction monitoring to comply with GDPR
  • Data sovereignty
  • Real-time analysis
  • Regulatory compliance
Legal Contract analysis Law firms using SLMs to review nondisclosure agreements and contracts without cloud transmission
  • Client confidentiality
  • On-premises processing
  • Specialized knowledge
Telecommunications Network management Telecom providers using SLMs in network nodes for immediate threat detection and response
  • Edge processing
  • Real-time response
  • Continuous operation
Retail In-store customer service Retail chains deploying SLMs in store systems for real-time customer assistance
  • Offline operation
  • Low latency
  • Personalization
Defense and aerospace Mission systems Defense contractors using SLMs for classified document analysis in secure facilities
  • Air-gapped operation
  • Security clearance compliance
  • Specialized knowledge
Energy and utilities Grid management Utility companies using SLMs in smart grid systems for immediate anomaly detection
  • Real-time monitoring
  • Edge deployment
  • Continuous operation

How to choose between SLMs vs. LLMs

While both are language model types, SLMs and LLMs vary in key characteristics:

Feature Small language models Large language models
Parameter count Typically 30 billion or fewer Hundreds of billions to trillions
Training data Curated and domain-specific Massive, diverse and scraped from the internet
Hardware requirements Standard GPUs or even CPUs Multiple high-end GPUs or TPUs
Inference speed Milliseconds to seconds Seconds to minutes
Memory usage Typically 2 to 16 GB Typically 50 GB or more
Deployment Can run on device Usually requires cloud infrastructure
Use cases Specialized tasks General-purpose tasks
Cost to train Thousands of dollars Millions of dollars
Energy consumption Relatively low; can run on standard hardware Very high; requires specialized cooling systems

Where do compact models fit in?

The differences between SLMs and LLMs become more complex when compared with compact models like OpenAI's o3-mini or Anthropic's Claude Haiku. Although they are marketed as lightweight language models, compact models still demand substantial compute. These streamlined versions are faster and more cost-effective than their full-sized counterparts, but they remain general-purpose tools designed for cloud deployments.

Understanding this distinction helps avoid a common misconception. When AI companies advertise smaller or faster models, they're usually referring to optimized versions of their cloud-based LLMs, not true SLMs. These optimized LLMs offer better performance and lower costs, but are fundamentally different from purpose-built SLMs that can run independently on private infrastructure.

Even DeepSeek's R1 reasoning model, which generated a great deal of excitement in early 2025, is still considered a large model at over 671 billion parameters. The excitement was due to its remarkable breakthroughs in efficiency, not the scale of the model.

The key business consideration between an LLM and an SLM is matching the tool to fit unique needs. Choose cloud-based LLMs, including smaller versions like Claude Haiku, when the use case demands versatile AI capabilities and doesn't have strict data privacy or latency requirements. Choose SLMs when the use case demands specialized performance, local deployments or complete control over data.

Small language model examples

The following are among the most widely used SLMs today:

  • DistilBERT is a lighter version of Google's BERT model. While some purists would call it a distilled transformer model, not an SLM, it has many characteristics in common with SLMs and is often chosen for SLM use cases.
  • Gemma is Google's compact model, excelling in conversational AI and fast language processing.
  • Llama 3.2 is Meta's model for edge and mobile devices. Meta has also used quantization to create versions that are even more efficient.
  • OpenELM is Apple's family of on-device AI models that range from 270 million to 3 billion parameters. They are designed for privacy and efficiency, but are not publicly available.
  • Phi-3-mini is Microsoft's 3.8 billion-parameter model, suitable for mobile deployment.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups.

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