Small language model
Description
In the AI landscape, bigger is not always better. While Large Language Models (LLMs) like GPT-4 or Claude are celebrated for their encyclopedic knowledge and versatility, they require immense computational power (GPU) to run. They are also prone to "hallucinations" and can be difficult to deploy in secure, on-premise environments. Small language models emerged as the pragmatic answer to these challenges.
A small language model is like a specialist consultant rather than a generalist know-it-all. Because they have fewer parameters, they require less memory and processing power, making them ideal for edge computing – running directly on laptops, mobile devices or secure local servers without sending data to the cloud. This architecture offers significant advantages in data privacy and latency. The secret to a small language model’s performance lies in data quality. An LLM learns from the entire internet, including the noise; a small language model is often trained or fine-tuned on strictly curated, domain-specific data (such as a company’s technical documentation or verified code repositories).
Example use cases
- Edge AI: Deploying voice assistants or translation tools directly on smartphones and IoT devices.
- Security: Running internal chatbots on private servers to ensure sensitive corporate data never leaves the organization.
- Workflows: Powering Agentic AI workflows that need to perform thousands of rapid reasoning steps.
- Coding: Assisting developers with autocompletion and debugging tools that run locally within the IDE.
- Compliance: Analyzing legal or medical documents within strict compliance boundaries.
Key benefits
RWS perspective
At RWS, we see Small Language Models as a critical enabler of Agentic AI and enterprise automation. We believe that for AI to be truly useful, it must be specialized, secure and grounded in truth.
Our partnership with leaders in the space allows us to help clients build "Specialized Language Models" that are trained on their own trusted content – managed within Tridion. By feeding a small language model with structured, semantic data from a Component Content Management System (CCMS), we ensure the model learns from the "golden source" of truth rather than unstructured noise. Furthermore, our TrainAI services provide the essential Human-in-the-Loop (HITL) validation required to curate the high-quality datasets that these models depend on.