What Are Small Language Models (SLMs)? How Do They Work?

Everyone, it seems, is talking about large language models, or LLMs. No wonder, because publicly accessible LLM-driven chatbots like ChatGPT and Microsoft Copilot have revolutionized, well, everything. However, for businesses seeking to integrate AI into applications, LLMs aren’t always necessary—in fact, they may well be overkill. Enter the small language model. SLMs are lean, targeted AI models that are well suited for many use cases, including applications that run entirely on devices or systems that need access to sensitive internal data or extensive training to minimize hallucinations. Small language models have also become popular among research teams and academic groups that need custom models but lack the infrastructure or budget to build an LLM.

What Are Small Language Models (SLMs)?

SLMs are language models that operate the same way that LLMs do but on a much smaller scale—SLMs tend to be 100 to 1,000 times smaller than most LLMs. Trainers can use smaller data sets requiring less training time, and the finished model is relatively cost-effective and manageable. Because an SLM can operate offline, it may be configured to be more secure than an LLM, and in production, inferencing times are generally faster. Many SLMs can run locally on phones, tablets, or edge devices.

SLMs are typically trained for a narrow set of tasks, often in one area of specialization, such as creating summaries of transcripts, translating simple user requests into code snippets, or handling sensitive information entirely in local environments to avoid cloud data transfers and improve compliance. Because SLMs can be fine-tuned or trained on proprietary data without leaving an organization’s secure environment, they may be less prone to errors or hallucinations compared to LLMs.

SLMs vs. LLMs: Efficiency and Scalability

SLMs and LLMs share development steps and core technical needs. The difference is scale—which affects everything from training to ongoing operations, including resource use and costs.

Most large language models contain hundreds of billions of parameters and have been trained on massive data sets for demanding jobs, such as public-facing chatbots. Deploying an LLM in-house requires significant computational resources, energy, data storage, and physical infrastructure. That’s why IT architects are turning to SLMs to provide specialized AI functionality in cases where an on-premises or cloud-based LLM isn’t necessary or practical. Because SLMs are designed for specific purposes, their training can be narrowed to a much smaller number of parameters—usually several billion or even hundreds of millions. When you compare that with hundreds of billions, it’s clear how an SLM is more manageable in terms of training, testing, deployment, and ongoing management.

SLMs vs. LLMs: Key Differences

Key Takeaways

• SLMs use fewer parameters and receive more focused training, often in a single domain or discipline for a specific need.
• SLMs can run on common handheld or edge devices, making it easier to meet security and regulatory compliance needs compared with LLMs.
• SLMs are often trained for narrow functions, such as automatically categorizing and assigning help-desk tickets or powering customer service chatbots.

Small Language Models Explained

SLMs are trained, refined, and deployed using the same steps as LLMs, just on a much smaller scale. Key areas of focus include

• Architecture: SLMs use fewer parameters, and the models employ fewer layers.
• Training data sets: SLMs use more focused data sets, often involving a single discipline, such as law or pharmacology.
• Deployment: Models can be deployed on laptops, phones, and edge devices, making them more practical for field operations.

SLMs can be a sensible choice when technical, regulatory, or operational limitations prevent the use of LLMs, or when an LLM’s broad capabilities aren’t required for the business need. SLMs also come with inherent benefits including faster inference, lower latency, greater deployment flexibility, and potentially fewer hallucinations. Like LLMs, SLMs can run in the cloud.

Benefits of Small Language Models

SLMs can help deliver AI locally despite hardware and power limitations and tight budgets. The following are some of the most common benefits of SLMs:

• Efficiency and cost-effectiveness: SLMs require less computational power and infrastructure resources and can optimize energy consumption and minimize operating expenses.
• Enhanced user experience: SLMs let mobile and edge devices deliver in-app AI features and offer flexible deployment options.
• Security: Because SLMs can often run entirely on a mobile or edge device, they may be more appropriate than LLMs in situations that involve stringent security or privacy demands or use of regulated data.
• Speed and responsiveness: Because they’re compact and efficient, SLMs can process information and generate responses more quickly than LLMs, especially on lower-powered processors or devices with limited memory. SLMs can also run on small devices without dependency on external network resources.
• Real-world applicability: SLMs are well suited for domain-specific tasks in field operations, remote environments, and other scenarios where quick responses and low resource requirements are essential. You’ll find SLMs in smart speakers, mobile phones, in-app assistants, and more.

Small Language Models Use Cases and Examples

SLMs are often built for embedding into IoT/edge devices or integrating into software workflows. The following are some common examples of SLMs in action:

• Customer support automation: SLMs provide automated handling of common customer inquiries via embedding in chatbots and ticketing systems, shortening response times while reducing operational costs.
• Content personalization: SLMs can turn user data into customized recommendations for content, products, and messaging while addressing privacy and compliance requirements.
• On-demand translation and multilingual support: SLMs can provide instant, on-device language understanding and translation, eliminating the need for external server transmissions.
• Text analytics and data parsing: SLMs can turn unstructured data, such as emails and chat logs, into actionable insights, powering summaries, categorization, anomaly detection—most any job that could benefit from real-time analysis.
• Virtual assistants in healthcare: SLMs that are compliant with the Health Insurance Portability and Accountability Act, or HIPAA, can privately and securely handle questions, appointment booking, record retrieval, and other common tasks when integrated into healthcare platforms.

Future of Small Language Models

Like their bigger counterparts, LLMs, SLMs are still evolving. Here’s what’s just over the horizon:

• Advancements in training techniques: SLMs are becoming further refined as the AI industry develops new optimizations, such as parameter-efficient fine-tuning and new distillation methods to enhance knowledge transfer from LLMs to SLMs.
• Expansion in edge and mobile computing: As AI advances facilitate resource-efficient inferencing and more compact model sizes, SLMs should gain greater flexibility in deployment on edge devices and in mobile environments.
• Broader accessibility and democratization: Because they require fewer resources to train and use, SLMs are expected to be integrated into more projects across organizations and in regions previously excluded due to resource or budget constraints.

Why Use Oracle for SLMs?

Oracle Cloud Infrastructure (OCI) offers right-sized compute options and a full range of managed AI services, making it ideal for developing, training, and deploying SLMs. OCI provides cost-efficient and scalable infrastructure that lets teams pay only for what they need. Multiple regions and data residency options provide global reach while addressing regulatory and compliance requirements, including AI sovereignty. Specialized OCI offerings are available for organizations that can’t migrate data to or use public cloud models. Advanced security features provide additional peace of mind.

While LLMs often capture more attention, SLMs are a key generative AI technology for specialized tasks, such as embedding language features in enterprise apps and providing on-device AI for phones and tablets. With OCI, teams can train, tune, and test SLMs, integrating them into runtimes designed for domain-specific expertise while staying within hardware and budget boundaries—bringing the benefits of AI to environments where LLMs aren’t practical.

SLM FAQs

How are small language models fine-tuned for specific applications?

Fine-tuning an SLM involves customizing it to a specific task or domain with a focused data set (for example, customer support scripts or medical documents). SLMs can be trained and fine-tuned on cloud infrastructure using domain data, with parameters adjusted for the application’s priorities. A health application’s runtime, for example, doesn’t need to quote Shakespeare, and an email summary generator doesn’t need to know recent sports scores. The idea is to help the model learn specialized vocabulary, patterns, and context. Fine-tuning is particularly effective for SLMs, helping them overcome the limitations of their smaller capacity by focusing on specialized functions. However, trainers should be on guard for overfitting, where the model performs well on training data but poorly on new data because it fails to generalize.

What are the security implications of using small language models?

Training, tuning, and testing SLMs can be done efficiently in a secure cloud environment. When deployed on devices, SLMs may provide greater security benefits than LLMs since they don’t require external resources for inference. Additionally, because SLM runtimes are more focused in scope, organizations may find it easier to address regulatory and privacy requirements.