In the field of artificial intelligence, the computational demands of large language models (LLMs) have always been a major challenge. However, this problem has recently been significantly addressed. The Quattuor-Octo-Quattuor (QoQ) algorithm and QServe system, jointly developed by researchers from MIT, NVIDIA, UMass Amherst, and MIT-IBM Watson AI Lab, provide a revolutionary solution for efficient deployment of LLMs. Quantization technology has been a key approach to managing the massive computational requirements of LLMs and has attracted much attention. However, traditional quantization methods often come with the dual challenges of computational overhead and accuracy loss. To overcome these issues, a research team led by MIT has developed the QoQ algorithm, which employs progressive group quantization to effectively alleviate accuracy loss. Through a two-stage quantization process, the algorithm significantly improves computational throughput and reduces latency. In the detailed analysis, the QoQ algorithm first quantizes the weights to 8 bits and further quantizes them to 4 bits, enabling general matrix multiplication (GEMM) operations on INT8 tensor cores. This technological innovation not only enhances computational efficiency but also incorporates SmoothAttention technology to further optimize model performance. To support the deployment of the QoQ algorithm, the research team has also developed the QServe system. This system, tailored runtime environment, fully leverages the potential of the algorithm. By employing computation-aware weight reordering and fused attention mechanisms, it significantly reduces quantization overhead and provides strong support for throughput and latency optimization in real-time applications. Performance test results demonstrate that the QoQ algorithm achieves significant throughput improvements on NVIDIA A100 and L40S GPUs. Particularly on the L40S platform, the QServe system achieves up to 3.5 times higher throughput enhancement, significantly reducing the cost of LLM services. This achievement not only proves the effectiveness of the QoQ algorithm and QServe system but also showcases their outstanding performance in handling large-scale computational tasks. Industry experts believe that the introduction of the QoQ algorithm and QServe system will greatly promote the widespread adoption and efficient utilization of LLMs in practical applications. They not only address the computational overhead and accuracy loss issues in traditional quantization methods but also inject new vitality into the development of the artificial intelligence field by improving processing speed and reducing economic costs. Looking ahead, researchers will continue to optimize the QoQ algorithm and QServe system to adapt to a wider range of application scenarios and more complex computational requirements. This breakthrough achievement will provide strong technical support and driving force for the continuous development of the artificial intelligence field.