What does INT4 Quantization primarily focus on?

INT4 quantization primarily focuses on maintaining accuracy while reducing complexity within machine learning models. This method compresses the size of the model by converting the weights from floating-point representations to a lower-bit representation, specifically 4 bits. While reducing model size is an inherent benefit of this quantization technique, the primary aim is to ensure that the model's performance does not significantly degrade as a result of this compression.

By using a lower precision format like INT4, the model can operate with less memory and computational resources, making it more efficient without sacrificing the accuracy needed for its tasks. This balance between reducing complexity (in terms of memory usage and required computational power) and preserving the effectiveness of the model aligns with the challenges in deploying machine learning models, especially in resource-constrained environments.

The other options primarily address aspects that are secondary to the main goal of INT4 quantization. While speeding up graphical applications or enhancing user experience may relate to performance improvements, they do not capture the core intent of INT4 quantization as accurately as maintaining model accuracy while reducing complexity does.