Inference speed: Smaller models generally provide quicker inference times, enabling real-time processing and increasing energy efficiency and cost savings.
Accuracy: Larger models enhanced with retrieval-augmented generation, aka RAG, often yield higher accuracy.
Deployability: Smaller models are well-suited for edge devices and mobile applications, while larger models run ideally in a cloud or data center.
Cost: Larger models require more compute infrastructure to run.
Developers should also consider which languages the AI model must support, based on who will use it and where it will be applied. This is particularly important in modern workplaces, where employees may speak many different languages. Ensuring the model can seamlessly translate languages is vital for effective communication and collaboration across its users.
Additionally, with the growing importance of sovereign AI, many countries are building proprietary models trained on local languages and data sets. This allows nations to maintain control and autonomy over AI, ensuring the development and application of these technologies align with their unique cultural, ethical, and legal standards.
How companies are using LLMs
LLMs are powering AI applications, including chatbots and predictive analytics tools, that are delivering breakthroughs and efficiencies across industries.