LLMs are highly versatile, able to handle a wide range of tasks with only a few training examples.
In contrast, smaller models tend to be more specialised and studies show that fine-tuning them on domain-specific datasets can sometimes lead to better performance than general LLMs on specific tasks.
LLMs demand significant computational resources for both training and inference, resulting in high costs and latency, which makes them less suitable for real-time applications, such as information retrieval, or in resource-limited environments like edge devices.
In contrast, smaller models require less training data and computational power, providing competitive performance while greatly reducing resource requirements.
Smaller, simpler models are generally more transparent and easier to interpret compared to larger, more complex models.
In areas like healthcare, finance and law, smaller models are often preferred because their decisions need to be easily understood by non-experts, such as doctors or financial analysts.
Below is an insightful graphic illustrating the collaboration between Small and Large Language Models.
It highlights how Small Models frequently support or enhance the capabilities of Large Models, demonstrating their crucial role in boosting efficiency, scalability and performance.
The examples make it clear that Small Models play a vital part in optimising resource use while complementing larger systems.
