📄 中文摘要
解耦推理是下一代推理能力的核心概念之一，结合智能请求调度和专家并行处理，能够显著提升推理性能、资源利用率和运营效率。通过在 Amazon SageMaker HyperPod EKS 上实施这些技术，用户可以优化其机器学习模型的推理过程，从而在处理复杂请求时实现更高效的资源分配和响应速度。这些新兴技术为机器学习应用提供了更灵活和高效的解决方案，推动了云计算环境下的智能推理能力的发展。
在 AWS 上引入基于 llm-d 的解耦推理
出处: Introducing Disaggregated Inference on AWS powered by llm-d
发布: 2026年3月16日
📄 English Summary
Introducing Disaggregated Inference on AWS powered by llm-d
Disaggregated inference represents a core concept in next-generation inference capabilities, integrating intelligent request scheduling and expert parallelism to significantly enhance inference performance, resource utilization, and operational efficiency. Implementing these technologies on Amazon SageMaker HyperPod EKS allows users to optimize the inference process of their machine learning models, achieving more efficient resource allocation and response times when handling complex requests. These emerging technologies provide flexible and efficient solutions for machine learning applications, advancing intelligent inference capabilities in cloud computing environments.