When you implement a custom Large Language Model (LLM), it’s important to understand the specific use cases you can apply it to, as each has distinct performance metrics that reveal how well a model can solve a particular task.

When you put a machine learning model into production, it needs to process new data and return results, whether that’s classifying an image, recommending a product, or detecting potential fraud. This step is called inference, and the way you run it can vary depending on your system’s needs.

Quantization is a critical technique for deploying large language models efficiently, reducing memory footprint and improving inference speed. However, lower precision often leads to a trade-off in model quality. In this article, we compare various degrees of quantization, analyzing their impact on ...