📄 中文摘要
针对大规模语言模型（LLM）的推理速度，提出了两种有效的优化技巧。第一种技巧是通过模型剪枝，去除冗余的参数，从而减少计算量，提高推理效率。第二种技巧则是利用量化技术，将模型权重和激活值转换为低精度格式，进一步加快推理速度。这两种方法的结合能够显著提升 LLM 的实时响应能力，适用于需要快速处理的应用场景，如对话系统和实时翻译。通过这些技术的应用，开发者能够在保持模型性能的同时，获得更高的推理速度。
快速 LLM 推理的两种不同技巧
出处: Two different tricks for fast LLM inference
发布: 2026年2月15日
📄 English Summary
Two different tricks for fast LLM inference
Two effective optimization techniques for improving the inference speed of large language models (LLMs) are presented. The first technique involves model pruning, which removes redundant parameters to reduce computational load and enhance inference efficiency. The second technique utilizes quantization, converting model weights and activation values into lower precision formats to further accelerate inference speed. The combination of these methods significantly enhances the real-time responsiveness of LLMs, making them suitable for applications requiring fast processing, such as conversational systems and real-time translation. By implementing these techniques, developers can achieve higher inference speeds while maintaining model performance.