The AI Summer of Smaller Models and Their Rise on Everyday Devices

The Shift to Small Language Models

As the AI summer unfolds, the trend of using small language models (SLMs) continues to grow. Companies are finding that these models, with sizes ranging from one hundred million to one hundred billion parameters, can run on standard PCs and smartphones, making advanced AI more accessible.

Test-and-Learn Arc of SLM Adoption

Organizations began with large language models (LLMs) but soon realized smaller models from firms like Microsoft, Meta, and Google, as well as startups like Hugging Face and Mistral, meet their needs at a lower cost.

Advantages of Going Small

• Cost Efficiency: SLMs offer significant savings for IT budgets.
• Speed and Efficiency: SLMs run locally and generate quick results.
• Reduced Latency: Faster prompt responses due to fewer parameters.
• Domain Specificity: Tailored training leads to more relevant outputs.
• Lower Error Rates: Using smaller data sets reduces hallucinations.
• Sustainability: Smaller models consume less power, supporting green initiatives.

Insights from Industry Experts

Experts emphasize that training data needs to be more selective to create highly effective SLMs, echoing the importance of resource efficiency. Drew Breunig points out that this new approach helps face the data scarcity challenge linked with LLMs.

The Dell AI Factory: Your Partner in AI Deployment

Regardless of your choice in models, Dell Technologies offers a structured pathway to deploying AI efficiently. The Dell AI Factory provides guidance on preparing corporate data and selecting appropriate AI infrastructure, proving invaluable as organizations navigate this dynamic landscape.

Remember, in the AI era, you don’t always need the largest models to achieve substantial business outcomes.