LLM Model Selection

Model Selection

Selecting the right LLM model is critical to your AI project success. There is no one size fits all. Model selection will depend on your industry & business use case. The priority for each business use case is different. For ex, an AI Assistant may prioritize low latency. A deep analytics application may prioritize advanced reasoning capabilities of LLMs. The use case, industry domain and its complexity significantly influences the selection of the appropriate model.

As shown in the below diagram, there are several factors to consider when deciding on which LLM model to use. An AI application may use one or more LLM model. Breakup your AI application in tasks. Then for each task, decide which LLM model would make sense. See the slideshow below or download to learn more

LLM Model Selection: Key Factors (download pdf)

"An AI application may leverage one or more LLMs (Large Language Models). To optimize its performance, break down your AI application into specific tasks. For each task, evaluate and choose the most appropriate LLM based on its capabilities and alignment with the task's requirements."

LLM vs SLM

Large language models (LLMs) are giants and can have billions or even trillions of parameters. While LLMs have created exciting new opportunities to infuse intelligence into applications, their large size means they require significant computing resources to operate. That's where small language models come in. Small language models are smaller in size and are cost effective to operate. GPT-4o mini and Phi-3 models are two examples of SLMs that are expanding the range of applications built with AI by making them much more affordable.