# Quantization Quantization is a technique to reduce the VRAM required for deploying LLMs. Quantization has several parameters to consider because extreme quantization can lead to model collapse. Additionally, some quantization techniques, when used with the wrong inference engine, can slow down the model's inference speed. One Click Deploy sets the default quantization to 8-bit weight. This technique minimizes the accuracy loss impact on the model and reduces the model size to half of the original. For advanced quantization, we prioritize minimizing accuracy loss not only for a single language but also for multilingual performance. ### Evaluation Dataset * M3Exam (Multiple-choice, Accuracy) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.float16.cloud/getting-started/one-click-deploy/features/quantization.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.