When it comes to AI, bigger isn’t always better. Businesses today are finding that small language models—those trained on smaller, more targeted datasets—are more efficient, cost-effective, and often more accurate than large language models (LLMs). These “mini” models focus on delivering high-quality results for specific tasks, making them a powerful alternative for companies looking to streamline their AI operations.
What is a Small Language Model?
Small language models (SLMs) are AI systems trained on highly curated, task-specific data, rather than the enormous datasets that LLMs rely on. With fewer parameters, these models are faster, cheaper, and more efficient, making them ideal for niche applications like answering customer service inquiries, drafting emails, or summarizing sales calls.
What is Molmo?
Molmo (Multimodal Open Language Model), is a family of open-source vision language models developed by the Allen Institute for Artificial Intelligence (Ai2). Molmo’s largest model, with 72 billion parameters, has been shown to outperform OpenAI’s GPT-4. The secret behind Molmo’s success lies in its efficient use of high-quality, carefully curated training data, as opposed to the vast, noisy datasets often used to train LLMs.
How Small Language Models Differ from LLMs
SLMs, like Molmo, are designed to excel in specific tasks, and they do so with fewer resources. The traditional LLM approach—training massive models on oceans of data—is incredibly costly and inefficient. OpenAI’s GPT-4, for instance, costs over $100 million to train, requiring enormous computational power and ongoing maintenance.
In contrast, small models require far fewer resources. Their efficiency means they consume less energy and are easier to maintain, significantly lowering operational costs. This also makes them more environmentally friendly.
On-Device and On-Premise AI: A Game Changer
One of the most exciting applications of small language models is their potential for both on-device and on-premise AI. Business users can access powerful AI tools directly from their devices or within their local infrastructure, without relying on cloud computing or internet connectivity.
Imagine a field technician diagnosing a washing machine problem in a basement where there’s no Wi-Fi. With on-device AI, the technician can still access crucial information, identify the issue, and order the necessary parts—all without an internet connection.
Similarly, on-premise AI allows organizations to deploy models locally within their own data centers, providing full control over data and operations. For example, a hospital handling sensitive patient data could use on-premise AI to run diagnostic models and process medical images without sending any data to the cloud. This setup ensures compliance with strict privacy regulations like HIPAA, while still benefiting from advanced AI capabilities for patient care.
Both on-device and on-premise AI not only improve efficiency but also reduce costs by eliminating the need for constant cloud-based computation, offering increased privacy, reliability, and control.
Conclusion: Small but Mighty
Small language models, alongside innovations like Molmo, are reshaping the AI landscape. Their ability to deliver precise, targeted results with fewer resources makes them an attractive option for businesses looking to streamline their operations. Whether it’s reducing costs, improving accuracy, or enhancing data privacy, small models are proving that sometimes less is indeed more.
At Predictive Systems, Inc., we are building AI solutions based on small language models (SLMs) that run on-premise, offering tailored, efficient, and secure AI capabilities. Contact us to learn more about how we can help transform your business with cutting-edge, on-premise AI technology.
Sources:
- https://molmo.allenai.org/paper.pdf
- https://www.technologyreview.com/2024/09/25/1104465/a-tiny-new-open-source-ai-model-performs-as-well-as-powerful-big-ones
- https://www.salesforce.com/blog/small-language-models/
