Getting Started with LLM Inference Optimization: Best Resources

Combining layers in transformer models makes them bigger and better at understanding language tasks. But making these big models costs a lot to train and they need a lot of memory and computer power to use afterward.

The most popular Large Language Models (LLM) today such as ChatGpt have billions of settings and sometimes they have to handle long pieces of text, which makes them even more expensive to use. 

For example, RAG pipelines require putting large amounts of information into the input of the model, greatly increasing the amount of processing work the LLM has to do.

In the article, you will be provided with a comprehensive list of resources to delve into the foremost challenges encountered in LLM inference and proffer practical solutions.