Large language models (LLMs) are impressive, but their size makes them resource-intensive. Imagine trying to run a supercomputer on your phone—that's the challenge with deploying LLMs in real-world applications. One solution is quantization, a technique that shrinks the model's memory footprint by representing its parameters with lower precision. Think of it like compressing an image: you lose some detail, but the overall picture remains. However, quantization can introduce errors, especially when dealing with the complex calculations within LLMs. A new research paper introduces BiSup, a method to suppress these errors and improve the efficiency of LLMs. BiSup tackles the problem of error propagation, where quantization errors accumulate and spread throughout the model like ripples in a pond. It uses a clever combination of techniques, including fine-grained clipping, smoothing, and low-rank compensation, to counteract these errors. Furthermore, BiSup introduces a "prompt mixed-precision" strategy, which keeps critical parts of the model at higher precision to minimize error diffusion. This is like preserving the sharpest details in an image while compressing the rest. The results are promising: BiSup improves performance across various LLM families and quantization settings, paving the way for more efficient and accessible AI. This means we can bring the power of LLMs to more devices and applications, without needing massive computing resources. While BiSup shows great potential, the research also highlights challenges and future directions. Fine-tuning the model for optimal quantization remains a delicate balancing act, and further research is needed to refine these techniques. As LLMs continue to grow, efficient deployment will be crucial, and BiSup offers a valuable step towards making these powerful models more practical.