Generative AI: The Growing Excitement Around Small Language Models

Presented by Dell

As generative AI emerged a year ago, technologists were captivated by the capabilities of large language models (LLMs), which deliver human-like responses to inquiries.

In technology, major advancements often shrink over time. Mainframes evolved into client-server models, and PCs partnered with tablets and smartphones in response to the demand for mobile computing. A similar trend is unfolding with generative AI software. The key driver? Deploying compact, powerful generative AI services on smaller devices, similar to how applications were mobilized over a decade ago.

This trend to resize models has amplified the confusion for IT leaders tasked with selecting the right model. Fortunately, there is a strategic framework for choosing a small language model (SLM).

The LLM vs. SLM Comparison

First, let's clarify the differences between LLMs and SLMs, acknowledging that there is no universal standard distinguishing the two.

LLMs typically consist of hundreds of billions of parameters, encompassing the weights and biases learned during training. In contrast, SLMs have parameter counts ranging from hundreds of millions to tens of billions.

While LLMs can generate diverse types of content—text, images, audio, and video—and perform complex natural language processing (NLP) tasks, they require substantial server capacity, storage, and GPUs to operate. The high costs associated with LLMs may deter some organizations, especially when considering environmental, social, and governance (ESG) compliance as these models demand significant computing resources for training, augmentation, fine-tuning, and other tasks.

SLMs, however, consume fewer resources while providing surprisingly strong performance, sometimes rivaling LLMs on specific benchmarks. Their customizable nature allows organizations to tailor SLMs to particular tasks, such as training on selected datasets and enhancing search results through retrieval-augmented generation (RAG). For many, SLMs are ideal for on-premises deployment.

The trend toward downsizing models is gaining traction among hyperscalers and startups, with many launching smaller models designed for mobile devices, from laptops to smartphones. Notable examples include Google's December unveiling of its Gemini line, featuring the compact Nano model, along with Mistral AI's Mixtral 8x7b and Microsoft's Phi-2 models. In February, Google introduced the Gemma models.

Selecting the Right Model

Choosing between an LLM and an SLM hinges on the number of parameters required to meet your needs and your budget. Here’s a guide to determine if an SLM is appropriate for your organization:

1. Evaluate Business Needs: Identify the specific problems you aim to solve—be it a new chatbot for customer care or enhanced content creation for sales and marketing. Understanding your use cases is crucial.

2. Research the Market: Explore various models to identify the best fit based on your current resources, including personnel, processes, and technology. Consider size, performance metrics relevant to your tasks, and data quality for training and fine-tuning. Ensure scalability and security comply with your requirements.

3. Conduct a Model Bake-off: Test favored SLMs through pilot programs to assess model accuracy, generalization, interpretability, and speed. Identify strengths and weaknesses across these dimensions.

4. Assess Resource Requirements: Evaluate your organization’s server, storage, and GPU needs, along with their associated costs. Consider if you should implement observability and AIOps to analyze outputs in relation to business outcomes.

5. Craft a Deployment Strategy: Develop a comprehensive strategy for integrating the chosen SLM into existing systems, addressing security and data privacy, and planning for maintenance and support. If opting for a public model, ensure robust support, and if choosing open-source, stay updated on any changes.

Final Thoughts

The generative AI landscape is evolving rapidly. Staying informed is crucial to avoid missing important developments.

A growing ecosystem of partners is available to assist you in selecting the right model, infrastructure, and strategies tailored to your business. By collaborating with the right partner, you can create optimized generative AI services for your employees and customers.

Ready to collaborate and innovate? Discover how Dell APEX for Generative AI can help you integrate AI seamlessly into your operations.

Clint Boulton

Senior Advisor, Portfolio Marketing, APEX at Dell Technologies.