低比特推理如何实现高效 AI

In just the past few years, large machine learning models have made incredible strides. Today’s models are not only remarkably capable but also achieve impressive results across a range of applications, from software engineering and scientific research to content creation and data analysis. With the arrival of models like Kimi-K2.5 and GLM-5, the pace of progress shows no sign of slowing down. (Kimi-K2.5 has an impressive 1 trillion parameters, nearly twice as many as the DeepSeek V3 model family that was released just last year.) And as these models continue to grow in size and capability, so does the demand for memory, computing power, and energy.

短短几年内，大型机器学习模型取得了令人难以置信的进步。今天模型不仅能力出众，还在从软件工程和科学研究到内容创作和数据分析的各种应用中取得了令人印象深刻的结果。随着像Kimi-K2.5和GLM-5等模型的到来，进步步伐没有放缓的迹象。（Kimi-K2.5拥有令人印象深刻的1万亿参数，几乎是去年刚发布的DeepSeek V3模型系列的两倍。）随着这些模型在规模和能力上的持续增长，对内存、计算能力和能源的需求也在增加。

One of the most effective ways teams are addressing these constraints is through low-bit inference, a set of techniques widely adopted across the industry that make AI models faster and cheaper to run by reducing how much memory and compute they need when serving real user requests. At Dropbox, products like Dropbox Dash rely on various models to deliver fast, reliable, and cost-effective AI-powered search and understanding across vast amounts of user content. Making this possible requires careful attention to model efficiency, hardware utilization, and latency constraints. And making this technology accessible to individuals and businesses means tackling new challenges around efficiency and resource use.

团队应对这些约束的最有效方式之一是通过 low-bit inference，这是一组在行业中广泛采用的技术，通过减少服务真实用户请求时所需的内存和计算量，使 AI 模型运行更快、更便宜。在 Dropbox，诸如 Dropbox Dash 等产品依赖各种模型来跨海量用户内容提供快速、可靠且成本效益高的 AI 驱动搜索和理解。要实现这一点，需要仔细关注模型效率、硬件利用率和延迟约束。而使这项技术对个人和企业可用，则意味着应对围绕效率和资源使用的全新挑战。

In this article, we’ll dive into the current landscape of low-bit compute for efficient inference. We’ll cover the different types of quantization, why and when they’re needed, and the key optimization challenges required to deploy advan...