Large language models (LLMs) are transforming the AI landscape, but their massive size presents challenges for speed and efficiency. A common solution is quantization, a compression technique that reduces the precision of numerical values within the model. However, traditional quantization methods often struggle to balance accuracy and performance, especially at lower precisions like 4-bit. New research reveals a surprising connection between the statistical distribution of LLM weights and activations and a more efficient quantization strategy. It turns out that these values often follow a Student's t-distribution, a bell-shaped curve similar to the normal distribution but with heavier tails. This discovery has led to the development of a new quantization format called Student Float (SF4), which is tailored to the t-distribution. SF4 has demonstrated remarkable improvements in accuracy compared to existing methods, even surpassing the more complex Normal Float (NF4) format. For example, tests on the LLaMA2-7B model showed a 0.76% average accuracy increase across various tasks. Researchers further refined the approach by introducing "supernormal support" to existing formats like E2M1, boosting accuracy with minimal hardware overhead. This innovation allows for a flexible trade-off between accuracy and chip area, enabling more LLM applications to run efficiently at 4-bit precision. For instance, Phi-2's accuracy increased by up to 2.19% with only a 1.22% area overhead. This breakthrough opens doors to faster, more efficient LLMs, paving the way for wider adoption and more powerful AI applications on various devices.