Sharing experts across layers can save computational resources but may reduce model flexibility.

When experts are shared across layers, the model reuses the same components multiple times. This means fewer total expert modules, which saves memory and speeds up training. However, since the same expert is used for different layers, it might not specialize as much in each layer’s unique role. This trade-off can impact the model's ability to learn complex patterns effectively. Researchers weigh these pros and cons when designing MOE models, choosing sharing if resource limits are strict or separate experts if maximum learning power is needed.