# solvers.MoERoutingModel { #mlsysim.solvers.MoERoutingModel }

```python
solvers.MoERoutingModel()
```

Models first-order MoE routing imbalance and expert-parallel all-to-all cost.

Sparse models decouple memory from compute, but routing is rarely perfectly
balanced. This model keeps the abstraction small: a single imbalance factor
inflates the effective active experts and the routed-token communication
volume. It does not simulate a router or token dispatcher.

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.solvers.MoERoutingModel.solve) | Estimate effective active parameters and optional EP all-to-all latency. |

### solve { #mlsysim.solvers.MoERoutingModel.solve }

```python
solvers.MoERoutingModel.solve(
    model,
    batch_size,
    seq_len,
    precision='fp16',
    ep_size=1,
    routing_imbalance_factor=1.0,
    fleet=None,
)
```

Estimate effective active parameters and optional EP all-to-all latency.