From an expert-load model to a real project从专家负载模型走向真实项目
A reading order through the concepts, minimal experiment, measurement method and source materials.按顺序连接基本概念、最小实验、测量方法与原始材料。
Before you start
You need basic Python and familiarity with matrix multiplication. The first experiment runs on a CPU; repository GPU experiments have their own hardware and environment requirements.
Build the model
Read useful work and the critical path. Explain why a fixed total assignment count does not determine maximum expert load.
Test one variable
Run the expert-load example. Change the distribution while keeping the total fixed. State what the toy model establishes and what it leaves out.
Design a measurement
Use the paired benchmark playbook. Specify a correctness rule, timing boundary and raw artifact format for the next experiment.
Enter the project
Open MoEFlow. Start with its overview, then follow the repository validation and order-control reports. Keep local layer results distinct from end-to-end serving results.
A question to take away
What additional evidence would let you move from a local improvement to a reliable serving-performance claim? Write down the missing workload coverage and controls before extending the experiment.
开始之前
需要基础 Python 与矩阵乘法知识。第一个实验在 CPU 上即可运行；仓库中的 GPU 实验有各自的硬件与环境要求。
建立模型
先读有效工作与关键路径，解释为什么固定总分配数，不能决定单个专家的最大负载。
验证一个变量
运行专家负载小实验，固定总数并改变分布，说明这个模型证明了什么、遗漏了什么。
设计测量
使用配对测试手册，为下一项实验写出正确性规则、计时边界与原始材料格式。
进入项目
打开 MoEFlow，先读概览，再查看仓库的验证与顺序控制报告。区分局部层级结果与端到端服务结果。
带着问题继续
还需要哪些证据，才能从局部改进走向可靠的服务性能结论？扩大实验前，先写出缺失的负载覆盖与控制条件。