📄 中文摘要
大型语言模型在原型设计上相对简单，但在企业规模的运营中却面临诸多挑战。随着使用量的增加，流量模式的变化以及工作负载的不确定性，新的问题开始显现，包括在负载下的延迟峰值、内存不稳定、日志系统干扰请求性能、性能随时间逐渐下降以及重启和扩展的操作复杂性。在小规模应用中，这些问题是可以容忍的，但在企业规模下，它们则成为基础设施风险。因此，建立一个专门的基础设施层显得尤为重要，以应对这些挑战并确保系统的稳定性和高效性。
企业在生产 LLM 系统中所需的基础设施层
出处: The Infrastructure Layer Enterprises Need for Production LLM Systems
发布: 2026年3月4日
📄 English Summary
The Infrastructure Layer Enterprises Need for Production LLM Systems
Large language models are relatively easy to prototype but present significant challenges when operating at enterprise scale. As usage increases, traffic patterns change, and workloads become unpredictable, new issues emerge, including latency spikes under load, memory instability, logging systems interfering with request performance, gradual performance degradation over time, and operational complexity regarding restarts and scaling. While these problems may be tolerable at a small scale, they pose infrastructure risks at an enterprise level. Therefore, establishing a dedicated infrastructure layer becomes crucial to address these challenges and ensure system stability and efficiency.